# Best MLOps Platforms

## How Many MLOps Platforms Products Does G2 Track?

**Total Products under this Category:** 263

### Category Stats (Jul 2026)

- **Average Rating:** 4.5/5 (↓0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Arize AI (+1.02%) - Among all products in this category, Arize AI recorded the largest rating increase compared to last month

_Last updated: July 31, 2026_

## How Does G2 Rank MLOps Platforms Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 7,600+ Authentic Reviews
- 263+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for MLOps Platforms
 ![G2 Grid® for MLOps Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/mlops-platforms/grids.png?focus%5B%5D=10470&focus%5B%5D=21469&focus%5B%5D=1333204&focus%5B%5D=1308795&focus%5B%5D=52115&focus%5B%5D=125020&focus%5B%5D=10938&focus%5B%5D=1327283)

Highlighted products: Databricks, Gemini Enterprise Agent Platform, Microsoft Fabric, IBM watsonx.ai, Amazon SageMaker, Roboflow, Snowflake, and SAS Viya.

Underlying data: [Grid® JSON](https://www.g2.com/categories/mlops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=microsoft-fabric&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=amazon-sagemaker&focus%5B%5D=roboflow&focus%5B%5D=snowflake&focus%5B%5D=sas-sas-viya)

**Sponsored**

### Gemini Enterprise Agent Platform

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1910&secure%5Bchosen_at%5D=2026-08-01T01%3A35%3A19Z&secure%5Bdisplayable_resource_id%5D=1910&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1910&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=21469&secure%5Bresource_id%5D=1910&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmlops-platforms%3Fopen_modal_url%3D%252Fproducts%252Fibm-spectrum-conductor-deep-learning-impact-dli%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fmlops-platforms%2526source%253Dcategory&secure%5Btoken%5D=923eb46772e6561aea79eeef5c8edf4b586c5e57d5379cb718482a1d576592d4&secure%5Burl%5D=https%3A%2F%2Fcloud.google.com%2Fproducts%2Fgemini-enterprise-agent-platform%3Futm_source%3DG2%26utm_medium%3Ddisplay%26utm_campaign%3DCloud-SS-DR-GCP-1713658-GCP-DR-NA-US-en-G2-Display-Banner-All-%2525epid%21-%2525ecid%21-geap%26utm_content%3D%257Bdevice%257D-%257Badgroupid%257D-%257Bnetwork%257D-%257Btargetid%257D-%257Bloc_physical_ms%257D-%257Bcampaignid%257D&secure%5Burl_type%5D=custom_url)

### [Databricks](https://www.g2.com/products/databricks/reviews)

Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,326

#### How Do G2 Users Rate Databricks?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 9.0/10 (Category avg: 9.0/10)
- **Metrics:** 8.8/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.8/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Databricks?

- **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc)
- **Company Website:** databricks.com
- **Year Founded:** 2013
- **HQ Location:** San Francisco, CA
- **Twitter:** @databricks  
92,269 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bddca64732f61b923d96364e8c8eb35711aab4f98797cb00ab071ff24fbdd392&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3477522%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,627 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 47% Large, 38% Medium

#### What Do G2 Reviewers Say About Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications.
- Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services.
- Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency.
- Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights.
- Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization.

##### Cons

- Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability.
- Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects.
- Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools.
- Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes.
- Users face **complex setup** challenges initially, though support helps simplify the experience over time.

#### What Are Recent G2 Reviews of Databricks?

**["Helpful for Managing and Analyzing Operational Data"](https://www.g2.com/survey_responses/databricks-review-13090803)**

**Rating:** 4.5/5.0 stars

_— Vishaka C._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13090803)

**["Databricks Streamlines ETL and Analytics with Scalable Notebooks"](https://www.g2.com/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13181721)

#### What Are G2 Users Discussing About Databricks?

- [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

### [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 654

#### How Do G2 Users Rate Gemini Enterprise Agent Platform?

- **Ease of Use:** 8.2/10 (Category avg: 8.8/10)
- **Scalability:** 8.8/10 (Category avg: 9.0/10)
- **Metrics:** 8.2/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Gemini Enterprise Agent Platform?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small, 31% Large

#### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of Gemini Enterprise Agent Platform, enhancing productivity and streamlining workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity and streamlining machine learning workflows effectively.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity through reduced manual work in projects.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity by streamlining various tasks and processes.
- Users value the **integrated platform** of Gemini, enhancing productivity by combining various functionalities in a unified system.

##### Cons

- Users find the **pricing ambiguous** with unexpected costs, making budget management a challenge on the Gemini platform.
- Users find the platform's **complexity** ,particularly in navigation and advanced features, challenging, especially for beginners.
- The **learning curve is steep** for new users, especially with complex features and pricing transparency issues.
- Users find the **complexity issues** of the Gemini Enterprise Agent Platform lead to high costs and a steep learning curve.
- Users find the **difficult learning** curve of Gemini Enterprise Agent Platform challenging, especially for newcomers to Google Cloud.

#### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform?

**["Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)**

**Rating:** 5.0/5.0 stars

_— Danyal A._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12437893)

**["Seamless Google Suite Integration for Everyday Work"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)**

**Rating:** 4.5/5.0 stars

_— Shubham S._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-12855480)

#### What Are G2 Users Discussing About Gemini Enterprise Agent Platform?

- [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 3 comments, 4 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 3 comments, 4 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 2 comments, 2 upvotes

### [Microsoft Fabric](https://www.g2.com/de/products/microsoft-fabric/reviews)

Microsoft Fabric ist eine umfassende, KI-gestützte Datenanalyseplattform, die verschiedene Datenmanagement- und Analysetools in einer einzigen, integrierten Umgebung vereint. Sie kombiniert die Fähigkeiten von Microsoft Power BI, Azure Synapse Analytics und Azure Data Factory und bietet ein nahtloses Erlebnis für Datenintegration, -engineering, -speicherung, Echtzeitanalysen, Data Science und Business Intelligence. Durch die Zentralisierung dieser Dienste vereinfacht Fabric das Datenmanagement, verbessert die Zusammenarbeit und beschleunigt die Umwandlung von Rohdaten in umsetzbare Erkenntnisse. Hauptmerkmale und Funktionalität: - Einheitlicher Data Lake (OneLake): Fabric bietet einen einzigen, KI-bereiten Data Lake, der alle Geschäftsdaten in einem einheitlichen, verwalteten Hub zentralisiert und kuratiert, um sicherzustellen, dass alle Teams sicher auf genaue Datensätze zugreifen können. - KI-gestützte Tools: Die Plattform bietet KI-verbesserte Tools, die auf verschiedene Datenprojekte zugeschnitten sind, und ermöglicht es Teams, schneller zu innovieren und nahezu in Echtzeit Erkenntnisse zu gewinnen, die geschäftliche Auswirkungen haben. - Integrierte Analyselösungen: Fabric umfasst Datenintegration, Datenengineering, Datenspeicherung, Echtzeitanalysen, Data Science und Business Intelligence, die alle auf einer lake-zentrierten SaaS-Lösung gehostet werden, um Einfachheit zu gewährleisten und eine einzige Quelle der Wahrheit zu bewahren. - Eingebaute Sicherheit und Governance: Mit robusten Datensicherheits-, Governance- und Compliance-Funktionen stellt Fabric sicher, dass Daten verantwortungsvoll und in Übereinstimmung mit Industriestandards verwaltet werden. Primärer Wert und Benutzerlösungen: Microsoft Fabric adressiert die Komplexitäten, die mit der Verwaltung disparater Datensysteme verbunden sind, indem es eine einheitliche Plattform bietet, die Daten-Workflows rationalisiert. Es befähigt Organisationen, das volle Potenzial ihrer Daten zu nutzen, erleichtert fundierte Entscheidungsfindung und fördert Innovation. Durch die Integration verschiedener Datendienste reduziert Fabric den betrieblichen Aufwand, steigert die Produktivität und unterstützt die Entwicklung von KI-gesteuerten Lösungen, wodurch Unternehmen in einer datenzentrierten Landschaft gedeihen können.

**Average Rating:** 4.7/5.0

**Total Reviews:** 44

#### How Do G2 Users Rate Microsoft Fabric?

- **Einfache Bedienung:** 9.1/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.3/10 (Category avg: 9.0/10)
- **Metriken:** 8.9/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 9.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Microsoft Fabric?

- **Verkäufer:** [Microsoft](https://www.g2.com/de/sellers/microsoft)
- **Gründungsjahr:** 1975
- **Hauptsitz:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** MSFT

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen, Versicherung
- **Company Size:** 38% Large, 38% Medium

#### What Do G2 Reviewers Say About Microsoft Fabric?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer finden die **Benutzerfreundlichkeit** von Microsoft Fabric außergewöhnlich, was eine nahtlose Einführung auch ohne technische Erfahrung ermöglicht.
- Benutzer schätzen den **freundlichen und reaktionsschnellen Kundensupport** , der alle Anfragen und Bedenken effektiv bearbeitet.
- Benutzer finden Microsoft Fabric **unglaublich intuitiv** , was die Datenverwaltung für alle in der Organisation zugänglich macht.
- Benutzer finden, dass die **einfache Einrichtung** von Microsoft Fabric es für Teams ohne vorherige ETL-Erfahrung zugänglich macht.
- Benutzer schätzen die **Integration von Werkzeugen** in Microsoft Fabric, was die Benutzerfreundlichkeit verbessert und umfangreiche Funktionen für Effizienz bietet.

##### Cons

- Benutzer haben Schwierigkeiten mit **Formelbeschränkungen** in Microsoft Fabric, da sich einige Formeln von ihrer vertrauten Excel-Umgebung unterscheiden.
- Benutzer stehen vor einer erheblichen **Lernkurve** mit Microsoft Fabric, was die Erfahrung und Effizienz neuer Benutzer beeinträchtigen kann.
- Benutzer finden **Excel-Formelprobleme** frustrierend, was zu Verzögerungen führt, während sie sich an die unterschiedliche Formellogik von Microsoft Fabric anpassen.
- Benutzer finden eine **steile Lernkurve** in Microsoft Fabric, was die Nutzung für Neulinge auf der Plattform erschweren kann.
- Benutzer finden, dass **eine Schulung erforderlich ist** , um sich an die Formeldifferenzen von Microsoft Fabric im Vergleich zu Excel anzupassen, aber Unterstützung ist verfügbar.

#### What Are Recent G2 Reviews of Microsoft Fabric?

**["Großartige Plattform für die Entwicklung von Datenanalysen und das Workflow-Management"](https://www.g2.com/de/survey_responses/microsoft-fabric-review-10981663)**

**Rating:** 4.5/5.0 stars

_— Amr a._

[Read full review](https://www.g2.com/de/survey_responses/microsoft-fabric-review-10981663)

**["Endlich haben wir unseren Datenstapel an einem Ort, aber die Kosten müssen beachtet werden."](https://www.g2.com/de/survey_responses/microsoft-fabric-review-12740895)**

**Rating:** 4.0/5.0 stars

_— rishabh m._

[Read full review](https://www.g2.com/de/survey_responses/microsoft-fabric-review-12740895)

### [Amazon SageMaker](https://www.g2.com/de/products/amazon-sagemaker/reviews)

Amazon SageMaker ist ein vollständig verwalteter Dienst, der Datenwissenschaftlern und Entwicklern ermöglicht, Machine-Learning-Modelle (ML) in großem Maßstab zu erstellen, zu trainieren und bereitzustellen. Es bietet eine umfassende Suite von Tools und Infrastruktur, die den gesamten ML-Workflow von der Datenvorbereitung bis zur Modellbereitstellung rationalisiert. Mit SageMaker können Benutzer schnell auf Trainingsdaten zugreifen, Algorithmen auswählen und optimieren sowie Modelle in einer sicheren und skalierbaren Umgebung bereitstellen. Hauptmerkmale und Funktionalität: - Integrierte Entwicklungsumgebungen (IDEs): SageMaker bietet eine einheitliche, webbasierte Oberfläche mit integrierten IDEs, einschließlich JupyterLab und RStudio, die eine nahtlose Entwicklung und Zusammenarbeit ermöglichen. - Vorgefertigte Algorithmen und Frameworks: Es umfasst eine Auswahl optimierter ML-Algorithmen und unterstützt beliebte Frameworks wie TensorFlow, PyTorch und Apache MXNet, was Flexibilität in der Modellentwicklung ermöglicht. - Automatisierte Modelloptimierung: SageMaker kann Modelle automatisch optimieren, um optimale Genauigkeit zu erreichen, wodurch der Zeit- und Arbeitsaufwand für manuelle Anpassungen reduziert wird. - Skalierbares Training und Bereitstellung: Der Dienst verwaltet die zugrunde liegende Infrastruktur, was ein effizientes Training von Modellen auf großen Datensätzen und deren Bereitstellung über automatisch skalierende Cluster für hohe Verfügbarkeit ermöglicht. - MLOps und Governance: SageMaker bietet Tools zur Überwachung, Fehlerbehebung und Verwaltung von ML-Modellen, um robuste Abläufe und die Einhaltung von Unternehmenssicherheitsstandards zu gewährleisten. Primärer Wert und gelöstes Problem: Amazon SageMaker adressiert die Komplexität und ressourcenintensive Natur der Entwicklung und Bereitstellung von ML-Modellen. Durch das Angebot einer vollständig verwalteten Umgebung mit integrierten Tools und skalierbarer Infrastruktur beschleunigt es den ML-Lebenszyklus, reduziert den betrieblichen Aufwand und ermöglicht es Organisationen, effizienter Erkenntnisse und Wert aus ihren Daten zu gewinnen. Dies befähigt Unternehmen, schnell zu innovieren und KI-Lösungen zu implementieren, ohne umfangreiche interne Expertise oder Infrastrukturmanagement zu benötigen.

**Average Rating:** 4.3/5.0

**Total Reviews:** 54

#### How Do G2 Users Rate Amazon SageMaker?

- **Einfache Bedienung:** 8.4/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.6/10 (Category avg: 9.0/10)
- **Metriken:** 9.4/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 8.8/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Amazon SageMaker?

- **Verkäufer:** [Amazon Web Services (AWS)](https://www.g2.com/de/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Gründungsjahr:** 2006
- **Hauptsitz:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 33% Medium, 33% Large

#### What Do G2 Reviewers Say About Amazon SageMaker?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer finden die **Benutzerfreundlichkeit** von Amazon SageMaker außergewöhnlich, was eine schnelle Anpassung und effizientes Modelltraining mit benutzerfreundlichen Funktionen ermöglicht.
- Benutzer schätzen die **nahtlose KI-Integration** von Amazon SageMaker, die die Effizienz des maschinellen Lernlebenszyklus verbessert.
- Benutzer schätzen die **überlegene Rechenleistung** von Amazon SageMaker, die die Modelltrainingszeit erheblich verkürzt und die Effizienz steigert.
- Benutzer schätzen die **außergewöhnliche Effizienz** von Amazon SageMaker, die die Modelltrainingszeit erheblich verkürzt und Arbeitsabläufe optimiert.
- Benutzer loben die **schnellen Verarbeitungs** fähigkeiten von Amazon SageMaker, die die Modelltrainingszeit erheblich verkürzen und die Benutzerfreundlichkeit verbessern.

##### Cons

- Benutzer finden Amazon SageMaker **teuer** , mit einer komplexen Preisgestaltung, die zu unerwarteten Kosten für das Training und die Bereitstellungen führt.
- Benutzer finden die **Preisstruktur komplex** und stehen oft vor hohen Kosten bei langen Trainingsjobs und Bereitstellungen.
- Benutzer finden, dass die **Komplexität der Preisgestaltung** in Amazon SageMaker zu unerwarteten Kosten und Verwirrung führen kann.
- Benutzer bemerken eine **steile Lernkurve** bei Amazon SageMaker, insbesondere für diejenigen, die neu bei AWS-Diensten und -Einrichtungen sind.
- Benutzer erleben eine **schwierige Lernkurve** während der anfänglichen Einrichtung von Amazon SageMaker, was die Produktivität beeinträchtigen kann.

#### What Are Recent G2 Reviews of Amazon SageMaker?

**["End-to-End-ML-Plattform, die den gesamten Lebenszyklus optimiert"](https://www.g2.com/de/survey_responses/amazon-sagemaker-review-13180609)**

**Rating:** 4.5/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/de/survey_responses/amazon-sagemaker-review-13180609)

**["Vollständig verwaltetes End-to-End-ML in AWS mit leistungsstarkem verteiltem Training"](https://www.g2.com/de/survey_responses/amazon-sagemaker-review-12853074)**

**Rating:** 4.0/5.0 stars

_— Hem J._

[Read full review](https://www.g2.com/de/survey_responses/amazon-sagemaker-review-12853074)

#### What Are G2 Users Discussing About Amazon SageMaker?

- [Wofür wird Amazon SageMaker verwendet?](https://www.g2.com/de/discussions/what-is-amazon-sagemaker-used-for)
- [Is AWS SageMaker good?](https://www.g2.com/de/discussions/is-aws-sagemaker-good) - 1 upvote
- [Who uses SageMaker?](https://www.g2.com/de/discussions/who-uses-sagemaker)
- [How do you use Amazon SageMaker?](https://www.g2.com/de/discussions/how-do-you-use-amazon-sagemaker)
- [What does Amazon SageMaker do?](https://www.g2.com/de/discussions/what-does-amazon-sagemaker-do)

### [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 136

#### How Do G2 Users Rate IBM watsonx.ai?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 8.8/10 (Category avg: 9.0/10)
- **Metrics:** 9.1/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.7/10 (Category avg: 8.7/10)

#### Who Is the Company Behind IBM watsonx.ai?

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 41% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.ai?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in IBM watsonx.ai, facilitating quicker AI integration and effective management.
- Users appreciate the **model variety** of IBM watsonx.ai, enabling customized training on existing models for enhanced performance.
- Users appreciate the **seamless integration of enterprise-grade AI** in IBM watsonx.ai, enhancing decision-making and workflow efficiency.
- Users appreciate the **enterprise-grade integrated studio** of IBM watsonx.ai for seamless AI training and reliable insights.
- Users value the **enterprise-grade AI integration** of IBM watsonx.ai, enhancing decision-making and business operations efficiently.

##### Cons

- Users find the **difficult learning** curve of IBM watsonx.ai daunting, making it less accessible for newcomers and smaller teams.
- Users find the **complex setup** of IBM watsonx.ai challenging, making it less suitable for small teams and beginners.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, making it less accessible for non-technical teams.
- Users find the product **expensive** and challenging for small teams, citing high costs and complex setup requirements.
- Users find the **complex setup** of IBM watsonx.ai challenging, especially for beginners and small teams.

#### What Are Recent G2 Reviews of IBM watsonx.ai?

**["Enterprise-Ready AI with Strong Governance and Flexible Model Support"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-12773148)

**["Unified, Governed AI Studio with Strong Performance and Seamless IBM Integrations"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13184421)**

**Rating:** 4.0/5.0 stars

_— Manan S._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13184421)

### [Roboflow](https://www.g2.com/products/roboflow/reviews)

Roboflow has everything you need to build and deploy computer vision applications. Over 1,000,000 users from businesses of every size — from startups to public companies — use the company's end-to-end platform for image and video collection, organization, annotation, preprocessing, model training, and deployment. Roboflow provides tools for each step in the computer vision deployment lifecycle and integrates with your existing solutions so you can tailor your pipeline to meet your needs.

**Average Rating:** 4.7/5.0

**Total Reviews:** 155

#### How Do G2 Users Rate Roboflow?

- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)
- **Scalability:** 10.0/10 (Category avg: 9.0/10)
- **Metrics:** 10.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Roboflow?

- **Seller:** [Roboflow](https://www.g2.com/sellers/roboflow)
- **Company Website:** roboflow.com
- **Year Founded:** 2019
- **HQ Location:** Remote, US
- **Twitter:** @roboflow  
13,577 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website)  
137 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Founder, Researcher
- **Top Industries:** Computer Software, Research
- **Company Size:** 78% Small, 14% Medium

#### What Do G2 Reviewers Say About Roboflow?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **intuitive interface** of Roboflow, enabling efficient annotation and seamless collaboration for computer vision projects.
- Users value the **efficiency** of Roboflow, praising its streamlined dataset management that saves time and reduces errors.
- Users appreciate the **annotation efficiency** of Roboflow, enjoying streamlined dataset management that saves time and reduces errors.
- Users appreciate the **easy data labelling process** in Roboflow, which streamlines annotation and enhances team collaboration.
- Users appreciate the **powerful and versatile features** of Roboflow, enhancing academic projects and computer vision tasks.

##### Cons

- Users find Roboflow **expensive** , especially for students, as key features require paid plans for privacy and customization.
- Users note a **lack of features** for advanced analytics and customization on lower-tier plans in Roboflow.
- Users find **limited functionality** in Roboflow, facing challenges like feature restrictions and lack of flexibility in advanced tasks.
- Users experience **annotation issues** with Roboflow, often needing extensive manual adjustments for accuracy and efficiency.
- Users find **inefficient labeling** management cumbersome, needing manual organization and lacking shortcuts for smoother annotation completion.

#### What Are Recent G2 Reviews of Roboflow?

**["Speeds up our agri‑CV research"](https://www.g2.com/survey_responses/roboflow-review-12692685)**

**Rating:** 5.0/5.0 stars

_— Alexey K._

[Read full review](https://www.g2.com/survey_responses/roboflow-review-12692685)

**["Roboflow Makes Computer Vision Projects Easy to Build, Train, and Deploy"](https://www.g2.com/survey_responses/roboflow-review-12984362)**

**Rating:** 5.0/5.0 stars

_— noah r._

[Read full review](https://www.g2.com/survey_responses/roboflow-review-12984362)

### [Snowflake](https://www.g2.com/pt/products/snowflake/reviews)

A Snowflake permite que todas as organizações mobilizem seus dados com o AI Data Cloud da Snowflake. Os clientes usam o AI Data Cloud para unir dados isolados, descobrir e compartilhar dados com segurança, alimentar aplicativos de dados e executar diversas cargas de trabalho de IA/ML e analíticas. Onde quer que os dados ou usuários estejam, a Snowflake oferece uma experiência de dados única que abrange várias nuvens e geografias. Milhares de clientes em muitos setores, incluindo 691 dos 2000 maiores do mundo segundo a Forbes em 2023 (G2K) até 31 de janeiro, usam o AI Data Cloud da Snowflake para impulsionar seus negócios.

**Average Rating:** 4.5/5.0

**Total Reviews:** 711

#### How Do G2 Users Rate Snowflake?

- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Escalabilidade:** 9.4/10 (Category avg: 9.0/10)
- **Métricas:** 8.9/10 (Category avg: 8.7/10)
- **Flexibilidade de estrutura:** 9.5/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Snowflake?

- **Vendedor:** [Snowflake, Inc.](https://www.g2.com/pt/sellers/snowflake-inc)
- **Website da Empresa:** www.snowflake.com
- **Ano de Fundação:** 2012
- **Localização da Sede:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ad18ff73a9b8bb34dd1b98a6ba1c6be57f7364939ad352612ecc483aba05d2b2&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsnowflake-computing%2F&secure%5Burl_type%5D=linkedin_company_website)  
11,308 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 45% Medium, 43% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Snowflake, achando-o rápido e eficaz para compartilhamento de dados e análises.
- Os usuários valorizam os **recursos confiáveis** do Snowflake, apreciando sua interface intuitiva e integração de dados perfeita para análises.
- Os usuários acham as **capacidades de gerenciamento de dados** do Snowflake excelentes para agregar e consultar eficientemente em vários conjuntos de dados.
- Os usuários admiram a **escalabilidade perfeita** do Snowflake, acomodando sem esforço as demandas de trabalho e garantindo desempenho ideal.
- Os usuários apreciam a **rápida análise de dados** do Snowflake, permitindo insights rápidos sem preocupações com infraestrutura.

##### Cons

- Os usuários acham os **altos custos** do Snowflake onerosos, especialmente para pequenas empresas com orçamentos limitados.
- Os usuários encontram **limitações de recursos** no Snowflake, como a falta de blocos de código e desafios na gestão de permissões.
- Os usuários descobrem que o **gerenciamento de custos** requer disciplina, pois cobranças inesperadas podem se acumular rapidamente sem monitoramento cuidadoso.
- Os usuários acham a **estrutura de custos difícil de otimizar** , levando a despesas iniciais inesperadamente altas durante a implementação.
- Os usuários acham que os **recursos limitados** do Snowflake em scripts dinâmicos e monitoramento prejudicam a flexibilidade e a usabilidade.

#### What Are Recent G2 Reviews of Snowflake?

**["Escalonamento Elástico e Análises Rápidas com Snowflake"](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)

**["Snowflake Simplifica o Gerenciamento de Dados em Escala"](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)

#### What Are G2 Users Discussing About Snowflake?

- [What is Snowflake used for?](https://www.g2.com/pt/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote

### [SAS Viya](https://www.g2.com/de/products/sas-sas-viya/reviews)

SAS Viya ist eine cloud-native Daten- und KI-Plattform, die es Teams ermöglicht, erklärbare KI zu entwickeln, bereitzustellen und zu skalieren, die vertrauenswürdige, sichere Entscheidungen fördert. Sie vereint den gesamten Daten- und KI-Lebenszyklus und befähigt Teams, schnell zu innovieren, während sie Geschwindigkeit, Automatisierung und Governance von Anfang an in Einklang bringen. Viya vereint Datenmanagement, fortschrittliche Analytik und Entscheidungsfindung in einer einzigen Plattform, sodass Organisationen mit Zuversicht vom Experimentieren zur Produktion übergehen können und messbare Geschäftsergebnisse liefern, die sicher, erklärbar und skalierbar in jeder Umgebung sind. Wichtige Fähigkeiten, die erforderlich sind, um vertrauenswürdige Entscheidungen zu liefern, umfassen: • End-to-End-Klarheit über den Daten- und KI-Lebenszyklus, mit eingebauter Herkunft, Prüfbarkeit und kontinuierlicher Überwachung zur Unterstützung verteidigbarer Entscheidungen. • Governance von Anfang an, die eine konsistente Aufsicht über Daten, Modelle und Entscheidungen ermöglicht, um Risiken zu reduzieren und die Akzeptanz zu beschleunigen. • Erklärbare KI im großen Maßstab, sodass Einblicke und Ergebnisse von Unternehmen und Regulierungsbehörden gleichermaßen verstanden, validiert und vertraut werden können. • Operationalisierte Analytik, die sicherstellt, dass der Wert über die Bereitstellung hinaus durch Überwachung, Neutraining und Lebenszyklusmanagement erhalten bleibt. • Flexible, cloud-native Bereitstellung, die es Organisationen ermöglicht, überall zu beginnen und überall zu skalieren, während die Kontrolle beibehalten wird.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

#### How Do G2 Users Rate SAS Viya?

- **Einfache Bedienung:** 8.2/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 8.2/10 (Category avg: 9.0/10)
- **Metriken:** 8.7/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 8.5/10 (Category avg: 8.7/10)

#### Who Is the Company Behind SAS Viya?

- **Verkäufer:** [SAS Institute Inc.](https://www.g2.com/de/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Unternehmenswebsite:** www.sas.com
- **Gründungsjahr:** 1976
- **Hauptsitz:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=64db42c044af5bbad79bd9677a620a6c31a8ff1abf4e7b2a6f1d6ed9561d105d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1491%2F&secure%5Burl_type%5D=linkedin_company_website)  
18,638 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Biostatistiker
- **Top Industries:** Pharmazeutika, Bankwesen
- **Company Size:** 33% Large, 33% Small

#### What Do G2 Reviewers Say About SAS Viya?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Benutzerfreundlichkeit** von SAS Viya, die die Datenvisualisierung vereinfacht und die Effizienz der Entscheidungsfindung verbessert.
- Benutzer schätzen die **sophisticated analytical capabilities** von SAS Viya, die eine einfache Bereitstellung und Entscheidungsfindung in Echtzeit ermöglichen.
- Benutzer schätzen die **fortschrittlichen Analysemethoden** , die von SAS Viya angeboten werden, da sie die Entscheidungsfindung und die Fähigkeiten zur Analyse logistischer Daten verbessern.
- Benutzer schätzen das **End-to-End-Datenlebenszyklus-Tooling** von SAS Viya, das die Geschäftseinblicke und strategische Entscheidungsfindung verbessert.
- Benutzer lieben die **intuitive Benutzeroberfläche** von SAS Viya, die Datenanalyse und Modellbereitstellung für alle Fähigkeitsstufen mühelos macht.

##### Cons

- Benutzer finden, dass SAS Viya eine **Lernschwierigkeit** aufweist, was es für nicht-technische Personen schwierig macht, effektiv zu navigieren.
- Benutzer finden die **Lernkurve steil** , was es für nicht-technische Benutzer schwierig macht, SAS Viya effektiv zu navigieren.
- Benutzer finden die **Visualisierungskomplexität** in SAS Viya herausfordernd, insbesondere für nicht-technische Benutzer und Anfänger.
- Benutzer kämpfen mit der **schwierigen Lernkurve** von SAS Viya, insbesondere neue und nicht-technische Benutzer.
- Benutzer finden die **teuren Preise** von SAS Viya als eine bedeutende Eintrittsbarriere für eine potenzielle Einführung.

#### What Are Recent G2 Reviews of SAS Viya?

**["Effektive Datenanalyse mit SAS Viya"](https://www.g2.com/de/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

[Read full review](https://www.g2.com/de/survey_responses/sas-viya-review-11872818)

**["SAS Viya: Leistungsstarke KI & Datenanalyse mit nahtlosen Integrationen"](https://www.g2.com/de/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Verifizierter Benutzer in Krankenhaus & Gesundheitswesen_

[Read full review](https://www.g2.com/de/survey_responses/sas-viya-review-11855145)

#### What Are G2 Users Discussing About SAS Viya?

- [Wofür wird SAS Visual Data Mining und Machine Learning verwendet?](https://www.g2.com/de/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments

### [Azure Machine Learning](https://www.g2.com/de/products/microsoft-azure-machine-learning/reviews)

Azure Machine Learning ist ein unternehmensgerechter Dienst, der den gesamten Lebenszyklus des maschinellen Lernens erleichtert und es Datenwissenschaftlern und Entwicklern ermöglicht, Modelle effizient zu erstellen, zu trainieren und bereitzustellen. Hauptmerkmale und Funktionalität: - Datenvorbereitung: Iterieren Sie schnell die Datenvorbereitung auf Apache Spark-Clustern innerhalb von Azure Machine Learning, interoperabel mit Microsoft Fabric. - Feature Store: Erhöhen Sie die Agilität beim Versand Ihrer Modelle, indem Sie Features über Arbeitsbereiche hinweg auffindbar und wiederverwendbar machen. - KI-Infrastruktur: Nutzen Sie die speziell entwickelte KI-Infrastruktur, die einzigartig darauf ausgelegt ist, die neuesten GPUs und InfiniBand-Netzwerke zu kombinieren. - Automatisiertes maschinelles Lernen: Erstellen Sie schnell genaue maschinelle Lernmodelle für Aufgaben wie Klassifikation, Regression, Vision und Verarbeitung natürlicher Sprache. - Verantwortungsvolle KI: Erstellen Sie verantwortungsvolle KI-Lösungen mit Interpretierbarkeitsfunktionen. Bewerten Sie die Fairness von Modellen durch Disparitätsmetriken und mindern Sie Unfairness. - Modellkatalog: Entdecken, verfeinern und implementieren Sie Grundmodelle von Microsoft, OpenAI, Hugging Face, Meta, Cohere und mehr mit dem Modellkatalog. - Prompt Flow: Entwerfen, konstruieren, bewerten und implementieren Sie Sprachmodell-Workflows mit Prompt Flow. - Verwaltete Endpunkte: Operationalisieren Sie die Modellbereitstellung und -bewertung, protokollieren Sie Metriken und führen Sie sichere Modell-Rollouts durch. Primärer Wert und bereitgestellte Lösungen: Azure Machine Learning beschleunigt die Zeit bis zur Wertschöpfung, indem es das Prompt Engineering und die Workflows für maschinelle Lernmodelle rationalisiert und die schnellere Modellentwicklung mit leistungsstarker KI-Infrastruktur erleichtert. Es rationalisiert die Abläufe, indem es reproduzierbare End-to-End-Pipelines ermöglicht und Workflows mit kontinuierlicher Integration und kontinuierlicher Bereitstellung (CI/CD) automatisiert. Die Plattform gewährleistet Vertrauen in die Entwicklung durch einheitliche Daten- und KI-Governance mit integrierter Sicherheit und Compliance, sodass Berechnungen überall für hybrides maschinelles Lernen ausgeführt werden können. Darüber hinaus fördert es verantwortungsvolle KI, indem es Einblick in Modelle bietet, Sprachmodell-Workflows bewertet und Fairness, Vorurteile und Schäden mit integrierten Sicherheitssystemen mindert.

**Average Rating:** 4.3/5.0

**Total Reviews:** 87

#### How Do G2 Users Rate Azure Machine Learning?

- **Einfache Bedienung:** 8.5/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.2/10 (Category avg: 9.0/10)
- **Metriken:** 8.3/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 9.2/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Azure Machine Learning?

- **Verkäufer:** [Microsoft](https://www.g2.com/de/sellers/microsoft)
- **Gründungsjahr:** 1975
- **Hauptsitz:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Software-Ingenieur
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 40% Large, 33% Small

#### What Do G2 Reviewers Say About Azure Machine Learning?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer finden Azure Machine Learning **einfach zu bedienen** , was eine nahtlose Datenverwaltung und Modellimplementierung erleichtert.
- Benutzer schätzen die **Skalierbarkeit und Integration** von Azure Machine Learning, was die Bereitstellung von KI in verschiedenen Anwendungen verbessert.
- Benutzer schätzen den **ausgezeichneten Kundensupport** von Azure Machine Learning, mit hilfreicher Dokumentation und verfügbarer Unterstützung durch die Community.
- Benutzer schätzen die **Benutzerfreundlichkeit und umfangreichen Funktionen** von Azure Machine Learning für effektives Datenmanagement.
- Benutzer schätzen die **Effizienz** von Azure Machine Learning, um Jobs nahtlos zu starten und zu überwachen, was die Produktivität steigert.

##### Cons

- Benutzer finden die **Lernkurve herausfordernd** , was Zeit und Mühe erfordert, um die Werkzeuge der Plattform effektiv zu nutzen.
- Benutzer finden die **schwierige Navigation** von Azure Machine Learning frustrierend aufgrund seiner unübersichtlichen Benutzeroberfläche und nicht intuitiven Arbeitsabläufe.
- Benutzer finden die **Benutzeroberfläche unorganisiert** , was zu Verwirrung und übermäßigem Klicken führt, um Optionen zu finden.
- Benutzer finden die **komplexe Benutzeroberfläche** von Azure Machine Learning nicht intuitiv, was ihren Arbeitsablauf und ihre Erfahrung erschwert.
- Benutzer stehen vor einer **schwierigen Lernkurve** mit Azure Machine Learning, insbesondere wenn sie neu auf der Plattform sind.

#### What Are Recent G2 Reviews of Azure Machine Learning?

**["Ein Unternehmensgerechter Weg zur Operationalisierung von ML"](https://www.g2.com/de/survey_responses/azure-machine-learning-review-12853548)**

**Rating:** 4.0/5.0 stars

_— Vytas J._

[Read full review](https://www.g2.com/de/survey_responses/azure-machine-learning-review-12853548)

**["Kosteneffiziente medizinische Datenintegration unterstützt durch großartigen Support"](https://www.g2.com/de/survey_responses/azure-machine-learning-review-12845990)**

**Rating:** 5.0/5.0 stars

_— Giridharan U._

[Read full review](https://www.g2.com/de/survey_responses/azure-machine-learning-review-12845990)

#### What Are G2 Users Discussing About Azure Machine Learning?

- [Wofür wird Azure Machine Learning Studio verwendet?](https://www.g2.com/de/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment
- [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/de/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for)
- [What are the key features of Azure Machine Learning?](https://www.g2.com/de/discussions/what-are-the-key-features-of-azure-machine-learning)
- [How do I use Microsoft Azure for machine learning?](https://www.g2.com/de/discussions/how-do-i-use-microsoft-azure-for-machine-learning)
- [What is Azure Machine Learning Studio?](https://www.g2.com/de/discussions/what-is-azure-machine-learning-studio)

### [Dataiku](https://www.g2.com/de/products/dataiku/reviews)

Dataiku ist die Plattform für AI-Erfolg: die AI-Orchestrierungsschicht, auf der Unternehmen Analysen, Modelle und Agenten in großem Maßstab erstellen, bereitstellen und verwalten. Sie sitzt über den Datenplattformen, Clouds und AI-Diensten, die Sie bereits nutzen, und arbeitet über alle hinweg, ohne Sie an eine bestimmte zu binden. Dataiku erweitert, wer produktionsreife AI erstellen kann, indem es die richtigen Werkzeuge sowohl in die Hände von Datenwissenschaftlern als auch von Fachexperten legt, von Betrugsanalysten bis hin zu Bedarfsplanern. Es orchestriert maschinelles Lernen, Regeln, LLMs und Agenten als ein verwaltetes System, das auf mehr als einem Jahrzehnt der Durchführung von Produktions-AI basiert. Governance ist Teil des Aufbaus und nicht etwas, das nachträglich hinzugefügt wird, sodass Teams schneller liefern können, während sie Leistung, Kosten und Risiken unter Kontrolle halten. Das Ergebnis: AI, die von der Experimentierung zu vertrauenswürdiger, messbarer Ausführung übergeht, jetzt und nicht erst in 18 Monaten.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **Einfache Bedienung:** 8.7/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.1/10 (Category avg: 9.0/10)
- **Metriken:** 8.7/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Dataiku?

- **Verkäufer:** [Dataiku](https://www.g2.com/de/sellers/dataiku)
- **Unternehmenswebsite:** Dataiku.com
- **Gründungsjahr:** 2013
- **Hauptsitz:** New York, NY
- **Twitter:** @dataiku  
22,917 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Datenwissenschaftler, Datenanalyst
- **Top Industries:** Finanzdienstleistungen, Pharmazeutika
- **Company Size:** 60% Large, 22% Medium

#### What Do G2 Reviewers Say About Dataiku?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen, wie Dataiku die **einfache ML-Entwicklung** erleichtert, sodass sie sich auf den Aufbau von Modellen konzentrieren können, ohne sich mit der Komplexität auseinandersetzen zu müssen.
- Benutzer lieben die **Benutzerfreundlichkeit** von Dataiku, die komplexe Aufgaben vereinfacht und ihre Datenanalyse-Erfahrung verbessert.
- Benutzer schätzen die **Benutzerfreundlichkeit** von Dataiku, die Zusammenarbeit sowohl für technische als auch nicht-technische Benutzer ermöglicht.
- Benutzer schätzen die **einfachen Integrationen** von Dataiku, die eine reibungslose Zusammenarbeit und Bereitstellung über verschiedene Analysetools hinweg erleichtern.
- Benutzer profitieren von der **Produktivitätssteigerung** durch Dataiku, was eine schnellere Projektentwicklung und verbessertes Karrierewachstum ermöglicht.

##### Cons

- Benutzer finden die **steile Lernkurve** von Dataiku herausfordernd, was es Anfängern schwer macht, die Plattform zu meistern.
- Benutzer finden die **steile Lernkurve** für Anfänger herausfordernd, was ihre Fähigkeit beeinträchtigt, Dataiku effektiv zu nutzen.
- Benutzer finden die **schwierige Lernkurve** herausfordernd, insbesondere für Anfänger, die sich in fortgeschrittenen Funktionen zurechtfinden.
- Benutzer erleben **langsame Leistung** mit Dataiku beim Umgang mit großen Datensätzen, was Effizienz und Produktivität beeinträchtigt.
- Benutzer finden Dataiku **teuer** , insbesondere für kleinere Organisationen und Projekte, was die Zugänglichkeit und Erschwinglichkeit beeinträchtigt.

#### What Are Recent G2 Reviews of Dataiku?

**["Vereinheitlichte, Low-Code-Plattform, die die End-to-End-Daten- und KI-Produktivität steigert"](https://www.g2.com/de/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/de/survey_responses/dataiku-review-13125252)

**["Erstellen Sie schnellere Workflows mit verbundenen Daten von vielen Anbietern oder verschiedenen Datenquellen."](https://www.g2.com/de/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/de/survey_responses/dataiku-review-13120436)

#### What Are G2 Users Discussing About Dataiku?

- [Is Dataiku an ETL tool?](https://www.g2.com/de/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/de/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/de/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/de/discussions/what-is-dataiku-dss-used-for)

### [SuperAnnotate](https://www.g2.com/de/products/superannotate/reviews)

SuperAnnotate überbrückt die Kluft zwischen modernster KI-Innovation und den hochwertigen menschlichen Daten, die sie antreiben - und hilft fortschrittlichen KI-Teams, intelligentere Modelle zu entwickeln. Mit einem globalen Netzwerk von Tausenden von sorgfältig geprüften Experten, ethischen und skalierbaren verwalteten Operationen, präziser Talentvermittlung und speziell entwickelter Technologie bietet SuperAnnotate vollständige Projekttransparenz und unvergleichliche Datenqualität. SuperAnnotate unterstützt komplexe Annotations-, Evaluations- und Verstärkungslern-Workflows, um fortschrittliche KI zu entwickeln, zu bewerten und auszurichten. Vertraut von Innovatoren wie Databricks, IBM und ServiceNow - und unterstützt von NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises und Lionel Messis Play Time VC - ermöglicht SuperAnnotate den weltweit führenden KI-Teams, verantwortungsvolle und hochmoderne Modelle mit menschlichen Daten zu entwickeln.

**Average Rating:** 4.8/5.0

**Total Reviews:** 353

#### How Do G2 Users Rate SuperAnnotate?

- **Einfache Bedienung:** 9.5/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.9/10 (Category avg: 9.0/10)
- **Metriken:** 9.7/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 9.9/10 (Category avg: 8.7/10)

#### Who Is the Company Behind SuperAnnotate?

- **Verkäufer:** [SuperAnnotate](https://www.g2.com/de/sellers/superannotate)
- **Unternehmenswebsite:** superannotate.com
- **Gründungsjahr:** 2018
- **Hauptsitz:** San Francisco, CA
- **Twitter:** @superannotate  
720 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website)  
361 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Daten Trainer
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 57% Small, 23% Medium

#### What Do G2 Reviewers Say About SuperAnnotate?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **intuitive Benutzeroberfläche** von SuperAnnotate, die groß angelegte Annotationsprojekte einfach zu verwalten und effizient macht.
- Benutzer schätzen die **benutzerfreundliche Oberfläche** von SuperAnnotate, die die Effizienz und Genauigkeit ihrer Annotationsaufgaben verbessert.
- Benutzer schätzen die **Annotationseffizienz** von SuperAnnotate, die schnelle, konsistente und hochwertige Annotationen über verschiedene Projekte hinweg ermöglicht.
- Benutzer loben die **Effizienz** von SuperAnnotate, schätzen die eingesparte Zeit und den optimierten Annotationsprozess.
- Benutzer schätzen SuperAnnotate für seine **hochwertigen Anmerkungen** , die eine konsistente und effiziente Zusammenarbeit und Verwaltung von Projekten gewährleisten.

##### Cons

- Benutzer bemerken **Leistungsprobleme** mit SuperAnnotate, insbesondere in Bezug auf Ladezeiten und gelegentliche technische Störungen.
- Benutzer erleben **langsame Leistung** mit SuperAnnotate, insbesondere beim Zuschneiden von Bildern und beim Umgang mit großen Projekten.
- Benutzer finden die **schwierige Lernkurve** für fortgeschrittene Funktionen herausfordernd, was ihre Gesamterfahrung mit SuperAnnotate beeinträchtigt.
- Benutzer finden die **Komplexität der Plattform** abschreckend, insbesondere für neue Benutzer, die sich in den erweiterten Funktionen zurechtfinden.
- Benutzer finden einen **Mangel an Anleitung** in SuperAnnotate, was es für Neulinge schwierig macht, die erweiterten Funktionen effektiv zu navigieren.

#### What Are Recent G2 Reviews of SuperAnnotate?

**["Einfach zu verwendende Datenorganisations- und leistungsstarke Bildaufteilungstools"](https://www.g2.com/de/survey_responses/superannotate-review-13146852)**

**Rating:** 5.0/5.0 stars

_— Doniaa K._

[Read full review](https://www.g2.com/de/survey_responses/superannotate-review-13146852)

**["Vereinfacht die Annotation mit einer einfachen Einrichtung und starker Unterstützung"](https://www.g2.com/de/survey_responses/superannotate-review-12584940)**

**Rating:** 4.0/5.0 stars

_— Nada A._

[Read full review](https://www.g2.com/de/survey_responses/superannotate-review-12584940)

#### What Are G2 Users Discussing About SuperAnnotate?

- [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/de/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment
- [How do I annotate an image in OpenCV?](https://www.g2.com/de/discussions/how-do-i-annotate-an-image-in-opencv)
- [Is SuperAnnotate free?](https://www.g2.com/de/discussions/is-superannotate-free)
- [How do you use SuperAnnotate?](https://www.g2.com/de/discussions/how-do-you-use-superannotate)
- [What is SuperAnnotate?](https://www.g2.com/de/discussions/what-is-superannotate) - 1 comment, 2 upvotes

### [Apache Airflow](https://www.g2.com/de/products/apache-airflow/reviews)

Apache Airflow ist eine Open-Source-Plattform, die für das Erstellen, Planen und Überwachen komplexer Workflows entwickelt wurde. In Python entwickelt, ermöglicht sie es Benutzern, Workflows als Code zu definieren, was die dynamische Generierung von Pipelines und die nahtlose Integration mit verschiedenen Technologien erleichtert. Die modulare Architektur und das Nachrichtenschlangensystem von Airflow ermöglichen eine effiziente Skalierung, die Workflows von einzelnen Maschinen bis hin zu groß angelegten verteilten Systemen verwaltet. Die benutzerfreundliche Weboberfläche bietet umfassende Überwachungs- und Verwaltungsmöglichkeiten und bietet klare Einblicke in den Status von Aufgaben und Ausführungsprotokolle. Hauptmerkmale: - Reines Python: Workflows werden mit Standard-Python-Code definiert, was die dynamische Generierung von Pipelines und die einfache Integration mit bestehenden Python-Bibliotheken ermöglicht. - Benutzerfreundliche Weboberfläche: Eine robuste Webanwendung ermöglicht es Benutzern, Workflows zu überwachen, zu planen und zu verwalten, ohne dass Befehlszeilenschnittstellen erforderlich sind. - Erweiterbarkeit: Benutzer können benutzerdefinierte Operatoren definieren und Bibliotheken erweitern, um sie an ihre spezifische Umgebung anzupassen, was die Flexibilität der Plattform erhöht. - Skalierbarkeit: Die modulare Architektur von Airflow und die Verwendung von Nachrichtenschlangen ermöglichen es, eine beliebige Anzahl von Arbeitern zu orchestrieren, sodass es bei Bedarf skalierbar ist. - Robuste Integrationen: Die Plattform bietet zahlreiche Plug-and-Play-Operatoren zur Ausführung von Aufgaben über verschiedene Cloud-Plattformen und Drittanbieterdienste, was die einfache Integration in bestehende Infrastrukturen erleichtert. Primärer Wert und Problemlösung: Apache Airflow adressiert die Herausforderungen bei der Verwaltung komplexer Daten-Workflows, indem es eine skalierbare und dynamische Plattform für die Workflow-Orchestrierung bereitstellt. Durch die Definition von Workflows als Code wird Reproduzierbarkeit, Versionskontrolle und Zusammenarbeit zwischen Teams sichergestellt. Die Erweiterbarkeit der Plattform und die robusten Integrationen ermöglichen es Organisationen, sie an ihre spezifischen Bedürfnisse anzupassen, den betrieblichen Aufwand zu reduzieren und die Effizienz bei Datenverarbeitungsaufgaben zu verbessern. Die benutzerfreundliche Oberfläche und die Überwachungsfunktionen verbessern die Transparenz und Kontrolle über Workflows, was zu einer verbesserten Datenqualität und Zuverlässigkeit führt.

**Average Rating:** 4.4/5.0

**Total Reviews:** 128

#### How Do G2 Users Rate Apache Airflow?

- **Einfache Bedienung:** 8.5/10 (Category avg: 8.8/10)
- **Skalierbarkeit:** 9.1/10 (Category avg: 9.0/10)
- **Metriken:** 8.5/10 (Category avg: 8.7/10)
- **Flexibilität des Rahmens:** 9.0/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Apache Airflow?

- **Verkäufer:** [The Apache Software Foundation](https://www.g2.com/de/sellers/the-apache-software-foundation)
- **Gründungsjahr:** 1999
- **Hauptsitz:** Wakefield, MA
- **Twitter:** @TheASF  
66,168 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b8484c4bb31e87bc0ba9e683d86f2af14309539343a063507c88bcdcff98434d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F215982%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,470 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Dateningenieur
- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 45% Medium, 31% Large

#### What Do G2 Reviewers Say About Apache Airflow?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Benutzerfreundlichkeit** von Apache Airflow, die eine effiziente Erstellung und Überwachung von Workflows erleichtert.
- Benutzer schätzen das **intuitive Dashboard** von Apache Airflow, um mühelos Workflows und Aufgabenstatus zu überwachen.
- Benutzer schätzen die **Flexibilität** von Apache Airflow sehr, da es angepasste Workflows über Python-Code ermöglicht.
- Benutzer schätzen die **Workflow-Automatisierungs** fähigkeiten von Apache Airflow, die das Management komplexer Datenpipelines vereinfachen.
- Benutzer schätzen die **einfachen Integrationen** in Apache Airflow, was es flexibel für die Verbindung verschiedener Systeme und Werkzeuge macht.

##### Cons

- Benutzer stehen vor einer **schwierigen Einrichtung** bei der Installation von Apache Airflow, insbesondere auf Windows-Systemen, was den Onboarding-Prozess erschwert.
- Benutzer finden die **Lernkurve herausfordernd** , da sie Zeit benötigen, um Operatoren zu verstehen und Workflows effektiv zu verwalten.
- Benutzer finden die **steile Lernkurve** von Airflow herausfordernd, insbesondere mit Konzepten und Einrichtungskomplexitäten.
- Benutzer empfinden die **Lernschwierigkeit** von Apache Airflow als Hindernis, insbesondere bei Jinja und der Einrichtung von Jobs.
- Benutzer finden, dass die **veraltete Benutzeroberfläche** von Apache Airflow von einer ansonsten zuverlässigen Erfahrung ablenkt.

#### What Are Recent G2 Reviews of Apache Airflow?

**["Skalierbare Workflows mit Apache Airflow, bestes Daten-Engineering-Tool für Orchestrator, einfache Bereitstellung"](https://www.g2.com/de/survey_responses/apache-airflow-review-12703177)**

**Rating:** 4.5/5.0 stars

_— Rajesh K._

[Read full review](https://www.g2.com/de/survey_responses/apache-airflow-review-12703177)

**["Leistungsstark für komplexe ML-Pipelines, aber mit einer steilen Lernkurve für die Infrastruktur verbunden."](https://www.g2.com/de/survey_responses/apache-airflow-review-12935519)**

**Rating:** 5.0/5.0 stars

_— Sachin G._

[Read full review](https://www.g2.com/de/survey_responses/apache-airflow-review-12935519)

#### What Are G2 Users Discussing About Apache Airflow?

- [Wofür wird Apache Airflow verwendet?](https://www.g2.com/de/discussions/what-is-apache-airflow-used-for)
- [Was ist Luftstromtechnologie?](https://www.g2.com/de/discussions/what-is-airflow-technology) - 1 comment
- [Ist Airflow ein Framework?](https://www.g2.com/de/discussions/is-airflow-a-framework) - 1 comment
- [Is Apache airflow an ETL tool?](https://www.g2.com/de/discussions/is-apache-airflow-an-etl-tool) - 1 comment
- [Wer verwendet Apache Airflow?](https://www.g2.com/de/discussions/who-is-using-apache-airflow) - 1 comment

### [Weights & Biases](https://www.g2.com/products/weights-biases/reviews)

Weights & Biases is the AI developer platform to build AI applications and models with confidence. ML engineers and AI developers use W&B Weave and W&B Models to coordinate all LLMops and MLops processes, including evaluating, debugging, training, fine-tuning, and deploying. W&B Weave helps developers evaluate, monitor and iterate on their AI applications to continuously improve quality, latency, cost, and safety. W&B Models boosts experiment speed and team collaboration among ML teams, helping them bring models to production faster while ensuring performance, data reliability, and security. W&B also serves as the system of record for all ML and AI activities.

**Average Rating:** 4.6/5.0

**Total Reviews:** 50

#### How Do G2 Users Rate Weights & Biases?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 8.3/10 (Category avg: 9.0/10)
- **Metrics:** 9.0/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.6/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Weights & Biases?

- **Seller:** [CoreWeave](https://www.g2.com/sellers/coreweave)
- **Year Founded:** 2017
- **HQ Location:** New York, US
- **Twitter:** @CoreWeave  
23,758 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8dc6fb72f750b09440d04ea4332dc85d5858c061be8cb5e2539ff9987d0c7aee&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcoreweave%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,289 employees on LinkedIn®
- **Ownership:** NASDAQ:CRWV

#### Who Uses This Product?

- **Top Industries:** Computer Software, Research
- **Company Size:** 51% Small, 30% Medium

#### What Do G2 Reviewers Say About Weights & Biases?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of Weights & Biases, simplifying tracking and sharing experiments effortlessly.
- Users praise the **seamless integration** of Weights & Biases with libraries, enhancing collaboration and simplifying experiment management.
- Users value the **easy setup** of Weights & Biases, enhancing productivity and simplifying collaboration across multiple platforms.
- Users appreciate the **fast and experienced customer support** of Weights & Biases, enhancing their overall experience.
- Users appreciate the **customization flexibility** of Weights & Biases for logging parameters and visualizing model comparisons.

##### Cons

- Users are often frustrated by the **insufficient documentation for basic functionalities** in Weights & Biases.
- Users find the **lack of guidance** in documentation frustrating, especially when seeking basic functionalities in Weights & Biases.
- Users find a **lack of tools** for easily discarding non-useful runs, complicating their workflow with Weights & Biases.
- Users desire **additional features** like global normalization settings and better control over window management on reload.
- Users find the **poor documentation** frustrating, especially when seeking basic functionalities of Weights & Biases.

#### What Are Recent G2 Reviews of Weights & Biases?

**["Essential ML Experiment Tracking with Real-Time Metrics and Team Collaboration"](https://www.g2.com/survey_responses/weights-biases-review-13193236)**

**Rating:** 4.5/5.0 stars

_— Muhammed A._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13193236)

**["A Must-Have Tool for Keeping ML Experiments Organized"](https://www.g2.com/survey_responses/weights-biases-review-13174389)**

**Rating:** 4.0/5.0 stars

_— Jeni J._

[Read full review](https://www.g2.com/survey_responses/weights-biases-review-13174389)

#### What Are G2 Users Discussing About Weights & Biases?

- [What is Weights & Biases used for?](https://www.g2.com/discussions/what-is-weights-biases-used-for)

### [JFrog](https://www.g2.com/products/jfrog-2024-03-28/reviews)

JFrog Ltd. (Nasdaq: FROG), the creators of the unified DevOps, DevSecOps, DevGovOps and MLOps platform, is on a mission to create a world of software delivered without friction from development to production. Driven by a “Liquid Software” vision to keep software continuously flowing, secure, and always up to date, the JFrog Platform serves as the definitive software supply chain system of record. It is uniquely engineered to power organizations as they build, manage, and distribute trusted software with unprecedented speed, security, and scale across hybrid and multi-cloud environments. As software engineering evolves in the AI era, JFrog’s newest offerings address the industry's most pressing trend: the rise of agentic software development and the hidden security risks of "Shadow AI." In response to threat actors increasingly targeting developer workflows including a massive surge in malicious open-source AI models and infected packages; JFrog has expanded its platform capabilities to deliver absolute end-to-end visibility and automated compliance. Key new innovations include the JFrog AI Catalog, which enables organizations to centralize, govern, and control the lifecycle of AI models approved for enterprise use. To secure autonomous coding environments, JFrog introduced the Universal MCP Registry and the Agent Skills Registry (developed alongside NVIDIA). These new solutions establish the industry’s first enterprise-grade trust layer to safely manage and store AI agent skills, monitor connections, and instantly block unsafe developer tools or malicious coding extensions right where developers work. Furthermore, the integration of advanced DevGovOps and Runtime Security tools allows teams to replace slow, manual compliance audits with continuous, background policy enforcement. By shifting security left directly into the binary pipeline, JFrog ensures that the volume of AI-assisted code does not outpace an organization's ability to verify its safety. Today, millions of users and approximately 6,600 organizations worldwide, including a majority of the Fortune 100, depend on the universal JFrog Platform to eliminate point-solution fatigue, bridge the governance gap, and securely embrace digital transformation. Learn more at www.jfrog.com or follow us on X @JFrog.

**Average Rating:** 4.2/5.0

**Total Reviews:** 149

#### How Do G2 Users Rate JFrog?

- **Ease of Use:** 8.1/10 (Category avg: 8.8/10)
- **Scalability:** 10.0/10 (Category avg: 9.0/10)

#### Who Is the Company Behind JFrog?

- **Seller:** [JFrog Ltd](https://www.g2.com/sellers/jfrog-ltd)
- **Company Website:** jfrog.com
- **Year Founded:** 2008
- **HQ Location:** Sunnyvale, CA
- **Twitter:** @jfrog  
23,186 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9e9f01c1efeb3f3e7b4535b3aefc16344bbb21773bc11bf4ad186f193dbcaabf&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fjfrog-ltd%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,364 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, DevOps Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 50% Large, 31% Medium

#### What Do G2 Reviewers Say About JFrog?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **comprehensive integration and multi-format support** of JFrog, streamlining their DevOps processes effectively.
- Users appreciate JFrog's **centralized artifact management** , enhancing efficiency in storing and tracking components across environments.
- Users value the **seamless deployment integration** of JFrog, enhancing CI/CD pipelines and security management effectively.
- Users value the **seamless integrations** of JFrog, enhancing their CI/CD processes across various package formats.
- Users value the **easy integrations** of JFrog with various tools, enhancing their CI/CD workflows seamlessly.

##### Cons

- Users find JFrog's platform to be **overly complex** , requiring significant training to navigate its extensive features effectively.
- Users find JFrog to be **expensive** , with costs posing challenges for smaller teams and individual developers.
- Users often face a **steep learning curve** with JFrog, requiring significant time to master its complexity.
- Users find the **difficult learning curve** of JFrog requires extensive training to navigate its complex features effectively.
- Users find JFrog to have a **steep learning curve** , requiring significant time and effort to reach proficiency.

#### What Are Recent G2 Reviews of JFrog?

**["JFrog Simplifies Artifact Management for Organized, Reliable Deployments"](https://www.g2.com/survey_responses/jfrog-review-12870354)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/survey_responses/jfrog-review-12870354)

**["Efficient, Scalable Artifact Management That Streamlines the Software Delivery Lifecycle"](https://www.g2.com/survey_responses/jfrog-review-12788318)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/jfrog-review-12788318)

#### What Are G2 Users Discussing About JFrog?

- [What are the benefits and challenges of using JFrog for managing your software supply chain?](https://www.g2.com/discussions/what-are-the-benefits-and-challenges-of-using-jfrog-for-managing-your-software-supply-chain)
- [What does Jfrog Platform do?](https://www.g2.com/discussions/what-does-jfrog-platform-do)
- [What is difference between JFrog and Nexus?](https://www.g2.com/discussions/what-is-difference-between-jfrog-and-nexus)
- [What is Artifactory software used for?](https://www.g2.com/discussions/what-is-artifactory-software-used-for)

### [Edge Impulse](https://www.g2.com/products/edge-impulse/reviews)

Edge Impulse is an end-to-end platform for edge AI application development. We enable developers to use their own sensor, audio and vision data to train AI models for classification, regression and anomaly detection. Our platform is hardware-aware and developers can build models that scale from MCUs to NPUs. We support MLOps from start to finish - from initial data collection to monitoring the model in the field.

**Average Rating:** 4.5/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate Edge Impulse?

- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)
- **Scalability:** 8.1/10 (Category avg: 9.0/10)
- **Metrics:** 8.3/10 (Category avg: 8.7/10)
- **Framework Flexibility:** 8.3/10 (Category avg: 8.7/10)

#### Who Is the Company Behind Edge Impulse?

- **Seller:** [Qualcomm](https://www.g2.com/sellers/qualcomm)
- **Year Founded:** 1985
- **HQ Location:** San Diego, CA
- **Twitter:** @Qualcomm  
441,209 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c011c5355b10e30f99e8c597addd4e1f5e9def722397be57a2defdd1a5140c47&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fqualcomm%2F&secure%5Burl_type%5D=linkedin_company_website)  
56,625 employees on LinkedIn®
- **Ownership:** NASDAQ:QCOM

#### Who Uses This Product?

- **Company Size:** 64% Small, 36% Large

#### What Do G2 Reviewers Say About Edge Impulse?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **extensive data augmentation and deployment options** offered by Edge Impulse for enhanced model performance.
- Users appreciate the **ease of use** of Edge Impulse, finding its interface intuitive for importing and scaling data.
- Users value the **data augmentation and preprocessing tools** that enhance training data quality and improve model performance.
- Users value the **flexibility** of Edge Impulse for deploying models across diverse edge devices and formats.
- Users find Edge Impulse's **user-friendly interface** makes machine learning accessible for various edge devices effortlessly.

##### Cons

- Users feel that the **lack of offline documentation** can hinder their usage of Edge Impulse in low-connectivity areas.
- Users feel that the **lack of tools** limits support for custom embedded devices, hindering broader experimentation.
- Users find the **limited customization** of Edge Impulse restrictive for building complex or specialized machine learning models.
- Users feel that **missing features** for custom embedded devices limit the product's appeal and usability for developers.
- Users find the **model limitations** of Edge Impulse restrictive for complex, specialized applications, craving more customization options.

#### What Are Recent G2 Reviews of Edge Impulse?

**["Empowering Edge AI Innovation: A Comprehensive Edge Impulse Review"](https://www.g2.com/survey_responses/edge-impulse-review-8506779)**

**Rating:** 5.0/5.0 stars

_— Alex G._

[Read full review](https://www.g2.com/survey_responses/edge-impulse-review-8506779)

**["Using Edge as a fairly new user"](https://www.g2.com/survey_responses/edge-impulse-review-8506732)**

**Rating:** 5.0/5.0 stars

_— Georgian C._

[Read full review](https://www.g2.com/survey_responses/edge-impulse-review-8506732)

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[Browse MLOps Platforms Themes](/categories/mlops-platforms/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Machine learning operationalization (MLOps) platforms allow users to manage, monitor, and deploy machine learning models as they are integrated into business applications, automating deployment, tracking model health and accuracy, and enabling teams to scale machine learning across the organization for tangible business impact.

### Core Capabilities of MLOps Platforms

To qualify for inclusion in the MLOps Platforms category, a product must:

- Offer a platform to monitor and manage machine learning models
- Allow users to integrate models into business applications across a company
- Track the health and performance of deployed machine learning models
- Provide a holistic management tool to better understand all models deployed across a business

### Common Use Cases for MLOps Platforms

Data science and ML engineering teams use MLOps platforms to operationalize models and maintain their performance over time. Common use cases include:

- Automating the deployment pipeline for ML models built by data scientists into production applications
- Monitoring model drift, accuracy degradation, and performance anomalies in deployed models
- Managing experiment tracking, model versioning, and security governance across the ML lifecycle

### How MLOps Platforms Differ from Other Tools

MLOps platforms focus on the maintenance and monitoring of deployed models rather than initial model development, distinguishing them from [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which focus on model building and training. Some MLOps solutions offer centralized management of all models across the business in a single location, and may be language-agnostic or optimized for specific languages like Python or R.

### Insights from G2 on MLOps Platforms

Based on category trends on G2, model monitoring and experiment tracking stand out as the most valued capabilities. Improved model reliability and faster iteration cycles stand out as primary benefits of adoption.

Top Tools at a Glance

| 

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Unified lakehouse for ML and data engineering

 | 

User Review

"Databricks Streamlines ETL and Analytics with Scalable Notebooks"

 |
| 

 | 

End-to-end ML lifecycle on Google Cloud

 | 

User Review

"Vertex AI Streamlines ML Training and Deployment with a Unified, Feature-Rich Platform"

 |
| 

 | 

Unified data-to-analytics pipelines inside Microsoft ecosystem

 | 

User Review

"Finally got our data stack in one place, but costs need attention"

 |
| 

 | 

End-to-end ML workflows inside AWS ecosystem

 | 

User Review

"End-to-End ML Platform That Streamlines the Full Lifecycle"

 |
| 

 | 

Enterprise AI governance with foundation model deployment

 | 

User Review

"Enterprise-Ready AI with Strong Governance and Flexible Model Support"

 |
| 

 | 

Computer vision dataset annotation to deployment

 | 

User Review

"Roboflow Makes Computer Vision Projects Easy to Build, Train, and Deploy"

 |
| 

 | 

ML pipelines on centralized multi-source data

 | 

User Review

"Snowflake Simplifies Data Management at Scale"

 |
| 

 | 

Enterprise ML governance with SAS code continuity

 | 

User Review

"SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"

 |
| 

 | 

Beginner-friendly model deployment with Azure integration

 | 

User Review

"Cost-Efficient Medical Data Integration Backed by Great Support"

 |
| 

 | 

Cross-functional ML workflows with visual and code flexibility

 | 

User Review

"Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"

 |

* * *

Show More

* * *

## How Do You Choose the Right MLOps Platforms?

### What You Should Know About MLOps Platforms

### What are MLOps Platforms?

MLOps solutions apply tools and resources to ensure that machine learning projects are run properly and efficiently, including data governance, model management, and model deployment.

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With machine learning, users are enabled to mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and helps make data-driven predictions.

One crucial aspect of the machine learning process is the development, management, and monitoring of machine learning models. Users leverage MLOps Platforms to manage and monitor machine learning models as they are integrated into business applications. 

Although MLOps capabilities can come together in software products or platforms, it is fundamentally a methodology. When data scientists, data engineers, developers, and other business stakeholders collaborate and ensure that the data is properly managed and mined for meaning, they need MLOps to ensure that teams are aligned, and that machine learning projects are tracked and can be reproduced.

#### What Types of MLOps Platforms Exist?

Not all MLOps Platforms are created equal. These tools allow developers and data scientists to manage and monitor machine learning models. However, they differ in terms of the data types supported, as well as the method and manner of deployment. 

**Cloud**

With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insights from it as well as to ensure its quality. These platforms allow them to train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models which have been deployed.

**On-premises**

Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for a number of reasons, including data security and latency issues. In cases like health care, strict regulations such as HIPAA require data to be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes vital.

**Edge**

Some platforms allow for spinning up algorithms on the edge, which consists of a mesh network of data centers that process and store data locally prior to being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** **

### What are the Common Features of MLOps Platforms?

The following are some core features within MLOps Platforms that can be useful to users:

**Model training:** Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and results in improved model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process whereby the proper values are determined for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.

**Model management:** The process does not end once the model is released. Businesses must monitor and manage their models to ensure they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss. It can help with recording, cataloging, and organizing all machine learning models deployed across the business. Not all models are meant for all users. Therefore, some tools allow for provisioning users based on authorization to both deploy and iterate upon machine learning models.

**Model deployment:** The deployment of machine learning models is the process of making the models available in production environments, where they provide predictions to other software systems. Some tools allow users to manage model artifacts and track which models are deployed in production. Methods of deployments take the form of REST APIs, GUI for on-demand analysis, and more.

**Metrics:** Users can control model usage and performance in production. This helps track how the models are performing.

### What are the Benefits of MLOps Platforms?

Through the use of MLOps Platforms, data scientists can gain visibility into their machine learning endeavors. This helps them better understand what is and isn’t working, and they are provided with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production.

**Share data insights:** Users are enabled to share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.

**Simplify and scale data science:** Pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms efficiently help scale experiments across many nodes to perform distributed training on large datasets.

**Experiment better:** Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. MLOps Platforms facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning are also used in experimentation, which are algorithms or methods used to change the attributes of neural networks such as weights and learning rate to reduce the losses.

### Who Uses MLOps Platforms?

Data scientists are in high demand, but there is a shortage in the number of skilled professionals available. The skillset is varied and vast (for example, there is a need to understand a vast array of algorithms, advanced mathematics, programming skills, and more); therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms are increasingly including features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.

In addition, for data science projects to initiate, it is key that the broader business buys into these projects. The more robust platforms provide resources that give nontechnical users the ability to understand the models, the data involved, and the aspects of the business which have been impacted.

**Data engineers:** With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.

**Citizen data scientists:** Especially with the rise of more user-friendly features, citizen data scientists who are not professionally trained but have developed data skills are increasingly turning to MLOps to bring AI into their organization.

**Professional data scientists:** Expert data scientists take advantage of these platforms to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment, speeding up data exploration and preparation, as well as model development and training.

**Business stakeholders:** Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.

### What are the Alternatives to MLOps Platforms?

Alternatives to MLOps Platforms can replace this type of software, either partially or completely:

[Data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) **:** Depending on the use case, businesses might consider data science and machine learning platforms. This software provides a platform for the full end-to-end development of machine learning models and can provide more robust features around operationalizing these algorithms.

[Machine learning software](https://www.g2.com/categories/machine-learning) **:** MLOps Platforms are great for the full-scale monitoring and managing of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs.

Many different types of machine learning algorithms perform various tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations looking for point solutions.

#### Software Related to MLOps Platforms

Related solutions that can be used together with MLOps Platforms include:

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although MLOps Platforms offer data preparation features, businesses might opt for a dedicated preparation tool.

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, and to best integrate all their data, they implement a data warehouse. Data warehouses house data from multiple databases and business applications, allowing business intelligence and analytics tools to pull all company data from a single repository. 

[Data labeling software](https://www.g2.com/categories/data-labeling) **:** To achieve supervised learning off the ground, it is key to have labeled data. Putting in place a systematic, sustained labeling effort can be aided by data labeling software, which provides a toolset for businesses to turn unlabeled data into labeled data and build corresponding AI algorithms.

[Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** NLP allows applications to interact with human language using a deep learning algorithm. NLP algorithms input language and give a variety of outputs based on the learned task. NLP algorithms provide voice recognition and natural language generation (NLG), which converts data into understandable human language. Some examples of NLP uses include chatbots, translation applications, and social media monitoring tools that scan social media networks for mentions.

### Challenges with MLOps Platforms

Software solutions can come with their own set of challenges. 

**Data requirements:** For most AI algorithms, a great deal of data is required to make it learn the needful. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application.

**Skill shortage:** There is also a shortage of people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems.

**Algorithmic bias:** Although the technology is efficient, it is not always effective and is marred with various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems.

### Which Companies Should Buy MLOps Platforms?

The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples:

**Financial services:** The use of AI in financial services is prolific, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With MLOps Plat, data science teams can build models with company data and deploy them to both internal and external applications.

**Healthcare:** Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more.

**Retail:** In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers.

### How to Buy MLOps Platforms

#### Requirements Gathering (RFI/RFP) for MLOps Platforms

If a company is just starting out and looking to purchase their first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.

The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, they must look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a data science platform.

#### Compare MLOps Platforms

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

#### Selection of MLOps Platforms

**Choose a selection team**

Before getting started, creating a winning team that will work together throughout the entire process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.

**Negotiation**

Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Do MLOps Platforms Cost?

As mentioned above, MLOps Platforms come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure. 

As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will often not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

#### Return on Investment (ROI)

Businesses decide to deploy MLOps Platforms to derive some degree of ROI. As they are looking to recoup the losses from the software, it is critical to understand its costs. As mentioned above, these platforms are typically billed per user, sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

### Implementation of MLOps Platforms

**How are MLOps Platforms Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is Responsible for MLOps Platforms Implementation?**

It may require a lot of people, or many teams, to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a complete understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together their data and begin the journey of data science, starting with proper data preparation and management.

**What Does the Implementation Process Look Like for MLOps Platforms?**

In terms of implementation, it is typical for the platform deployment to begin in a limited fashion and subsequently roll out in a broader fashion. For example, a retail brand might decide to A/B test their use of a personalization algorithm for a limited number of visitors to their site to better understand how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).

If the deployment was not successful, the team could go back to the drawing board, attempting to figure out what went wrong. This will involve examining the training data, as well as the algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data as a whole.

**When Should You Implement MLOps Platforms?**

As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must prioritize getting their data in order, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output. 

### MLOps Platforms Trends

**AutoML**

AutoML helps automate many tasks needed to develop AI and machine learning applications. Uses include automatic data preparation, automated feature engineering, providing explainability for models, and more.

**Embedded AI**

Machine and deep learning functionality are getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. Using embedded AI inside software like CRM, marketing automation, and analytics solutions allows users to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI may gradually pick up in the coming years and may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected.

**Machine Learning as a service (MLaaS)**

The software environment has moved to a more granular, microservices structure, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as the same companies offer MLaaS to other businesses.

Developers easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights. Using systems built by enterprise companies helps small businesses save time, resources, and money by eliminating the need to hire skilled machine learning developers. MLaaS will grow further as businesses continue to rely on these microservices and as the need for AI increases.

**Explainability**

When it comes to machine learning algorithms, especially deep learning, it may be particularly difficult to explain how they arrived at certain conclusions. Explainable AI, also known as XAI, is the process whereby the decision-making process of algorithms is made transparent and understandable to humans. Transparency is the most prevalent principle in the current AI ethics literature, and hence explainability, a subset of transparency, becomes crucial. MLOps Platforms are increasingly including tools for explainability, helping users build explainability into their models and meet data explainability requirements in legislation such as the European Union's privacy law, the GDPR.