# Best Data Science and Machine Learning Platforms *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Data science and machine learning (DSML) platforms provide tools to build, deploy, and monitor machine learning (ML) algorithms by combining data with intelligent, decision-making models to support business solutions. These platforms may offer prebuilt algorithms and visual workflows for nontechnical users or require more advanced development skills for complex model creation. Core capabilities of data science and machine learning (DSML) software To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must: - Present a way for developers to connect data to algorithms so they can learn and adapt - Allow users to create ML algorithms and offer prebuilt algorithms for novice users - Provide a platform for deploying AI at scale How DSML software differs from other tools DSML platforms differ from traditional platform-as-a-service (PaaS) offerings by providing ML–specific functionality, such as prebuilt algorithms, model training workflows, and automated features that reduce the need for extensive data science expertise. Insights from G2 Reviews on DSML software According to G2 review data, users highlight the value of streamlined model development, ease of deployment, and options that support both nontechnical and advanced practitioners through visual interfaces or coding-based workflows. ## Best Data Science and Machine Learning Platforms At A Glance - **Leader:** [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) - **Highest Performer:** [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) - **Easiest to Use:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Top Trending:** [RapidCanvas](https://www.g2.com/products/rapidcanvas/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Hurree Hurree is an AI-powered platform that consolidates all your reporting data into a single, easy-to-use command centre. It connects with over 70 popular tools, giving you a single, reliable view of performance across your business. With fully customizable dashboards, Hurree makes it simple for any team to track KPIs, monitor trends, and measure success with no technical skills required. At the heart of the platform is Riva, Hurree's built-in AI assistant. Riva doesn't just show you the numbers; it explains them. From instant summaries to predictive insights and actionable recommendations, Riva helps you understand what’s happening and what to do next. Hurree is built for teams who want better visibility, clearer reporting, and data they can actually use, without the complexity. [Visit company website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=692&secure%5Bdisplayable_resource_id%5D=208&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=620&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1200745&secure%5Bresource_id%5D=692&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-science-and-machine-learning-platforms&secure%5Btoken%5D=22f3951c4c9b3381e0c808baa111269a1f1724905c31ff441aa29bd767b382b4&secure%5Burl%5D=https%3A%2F%2Fhubs.ly%2FQ042rp5S0&secure%5Burl_type%5D=custom_url) --- ## Top-Rated Products (Ranked by G2 Score) ### 1. [Vertex AI](https://www.g2.com/products/google-vertex-ai/reviews) Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. **Average Rating:** 4.3/5.0 **Total Reviews:** 648 **User Satisfaction Scores:** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,840,340 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 41% Small-Business, 31% Enterprise #### Pros & Cons **Pros:** - Ease of Use (162 reviews) - Model Variety (114 reviews) - Features (109 reviews) - Machine Learning (104 reviews) - Easy Integrations (84 reviews) **Cons:** - Expensive (75 reviews) - Learning Curve (63 reviews) - Complexity (62 reviews) - Complexity Issues (58 reviews) - Difficult Learning (47 reviews) ### 2. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Databricks to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified Data Intelligence Platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase and Unity Catalog. **Average Rating:** 4.6/5.0 **Total Reviews:** 721 **User Satisfaction Scores:** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.5/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.4/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (89,234 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (14,779 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 45% Enterprise, 40% Mid-Market #### Pros & Cons **Pros:** - Features (288 reviews) - Ease of Use (278 reviews) - Integrations (189 reviews) - Collaboration (150 reviews) - Data Management (150 reviews) **Cons:** - Learning Curve (112 reviews) - Expensive (97 reviews) - Steep Learning Curve (96 reviews) - Missing Features (69 reviews) - Complexity (64 reviews) ### 3. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control. **Average Rating:** 4.3/5.0 **Total Reviews:** 700 **User Satisfaction Scores:** - **Application:** 7.8/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 7.6/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Company Website:** https://www.sas.com/ - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,957 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Student, Statistical Programmer - **Top Industries:** Pharmaceuticals, Computer Software - **Company Size:** 33% Enterprise, 32% Small-Business #### Pros & Cons **Pros:** - Ease of Use (316 reviews) - Features (218 reviews) - Analytics (196 reviews) - Data Analysis (166 reviews) - User Interface (147 reviews) **Cons:** - Learning Difficulty (151 reviews) - Learning Curve (144 reviews) - Complexity (143 reviews) - Difficult Learning (117 reviews) - Expensive (108 reviews) ### 4. [Deepnote](https://www.g2.com/products/deepnote/reviews) Deepnote is a data workspace where agents and humans work together. It's designed to simplify data exploration, accelerate analysis, and quickly deliver actionable insights for you and your team. Unlike outdated tools such as Jupyter, Deepnote is built with the next decade in mind. Deepnote gives anyone working with data superpowers. It unifies your data workflow through an integrated semantic layer, preparing your data for advanced AI applications. You can also leverage our AI data copilot to chat with your data, create charts, write code, or turn your AI notebooks into fully-fledged data dashboards or apps. Combine data, SQL or Python code, and visualizations side-by-side on a flexible canvas - enhanced with cutting-edge AI reasoning models. 🤖 Analyze with AI • Generate code and visualizations by describing your goal. • Auto-write, run, and debug code with AI. • Move faster with context-aware AI suggestions. 🔗 Unify • Connect to 60+ data sources like BigQuery, Snowflake, and PostgreSQL. • Combine Python and SQL in one notebook. • Build reusable ETL, analytics, and metric modules. • Create a semantic layer with shared definitions and trusted metrics. ⚖️ Scale • Instantly boost compute power, more included than Colab. • Schedule jobs and get notified with fresh results. • Organize work in projects and folders for team clarity. • Manage workflows via REST API. 🚀 Launch • Turn notebooks into dashboards or data apps, natively or with Streamlit. • Let users explore data with interactive inputs. • Share secure, live apps in one click. **Average Rating:** 4.5/5.0 **Total Reviews:** 374 **User Satisfaction Scores:** - **Application:** 8.0/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.2/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Deepnote](https://www.g2.com/sellers/deepnote) - **Company Website:** https://www.deepnote.com - **Year Founded:** 2019 - **HQ Location:** San Francisco , US - **Twitter:** @DeepnoteHQ (5,240 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deepnote (25 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Student, Data Analyst - **Top Industries:** Computer Software, Higher Education - **Company Size:** 68% Small-Business, 24% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (170 reviews) - Collaboration (120 reviews) - Easy Integrations (76 reviews) - Team Collaboration (76 reviews) - Data Management (67 reviews) **Cons:** - Slow Performance (61 reviews) - Limited Features (32 reviews) - Data Management Issues (29 reviews) - Missing Features (26 reviews) - Lacking Features (25 reviews) ### 5. [Anaconda Platform](https://www.g2.com/products/anaconda-platform/reviews) Anaconda Platform is a unified enterprise AI development platform that helps data scientists, AI developers, and platform engineers build, secure, deploy, and observe AI workloads from development to production. The platform addresses critical challenges enterprises face when scaling open-source AI initiatives, including environment complexity, security vulnerabilities, deployment failures, and governance requirements across cloud and on-premises infrastructure. The platform combines trusted Python package distribution with enterprise-grade governance controls, enabling organizations to accelerate AI innovation while maintaining security and compliance. Data scientists access over 12,000 pre-vetted, compatible open-source packages through Anaconda's secure distribution, eliminating dependency conflicts and environment drift that typically slow deployment cycles. Platform administrators gain centralized management, role-based access controls, and automated vulnerability detection across all AI workloads. Key capabilities include: Trusted Distribution - Pre-validated Python packages with signature verification, software bills of materials (SBOMs), and guaranteed uptime SLAs reduce supply chain security risks Secure Governance - Automated CVE scanning, package filtering, audit logs, and compliance tracking for GDPR, HIPAA, and CCPA requirements enable teams to move fast without compromising security Developer Velocity - Pre-configured environments, one-command setup, and automatic dependency resolution eliminate debugging time so developers focus on building solutions Production-Ready AI - High-performance runtimes and proven deployment workflows ensure what works locally runs reliably at scale, bridging the gap between experimentation and production Actionable Insights - Real-time telemetry and usage metrics across packages, environments, and models provide visibility for data-driven optimization decisions Organizations using Anaconda Platform report 60% reduced risk of security breaches, 80% efficiency improvement in package security management, and significant time savings by eliminating manual package vetting processes. **Average Rating:** 4.5/5.0 **Total Reviews:** 237 **User Satisfaction Scores:** - **Application:** 8.9/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Anaconda, Inc.](https://www.g2.com/sellers/anaconda-inc) - **Year Founded:** 2012 - **HQ Location:** Austin, Texas - **Twitter:** @anacondainc (83,784 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25029553/ (575 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Student - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 37% Small-Business, 25% Enterprise #### Pros & Cons **Pros:** - Ease of Use (74 reviews) - Setup Ease (39 reviews) - Efficiency (27 reviews) - Intuitive (25 reviews) - Coding Ease (23 reviews) **Cons:** - Data Management Issues (11 reviews) - Slow Performance (11 reviews) - Lacking Features (10 reviews) - Limited Features (9 reviews) - Limited Storage (9 reviews) ### 6. [Dataiku](https://www.g2.com/products/dataiku/reviews) Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale. Built for AI success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production. Orchestration that scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation. Governance you can trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable. **Average Rating:** 4.4/5.0 **Total Reviews:** 186 **User Satisfaction Scores:** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.2/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.7/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku) - **Company Website:** https://Dataiku.com - **Year Founded:** 2013 - **HQ Location:** New York, NY - **Twitter:** @dataiku (22,923 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,609 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Scientist, Data Analyst - **Top Industries:** Financial Services, Pharmaceuticals - **Company Size:** 60% Enterprise, 22% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (82 reviews) - Features (82 reviews) - Usability (46 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (42 reviews) **Cons:** - Learning Curve (45 reviews) - Steep Learning Curve (26 reviews) - Slow Performance (24 reviews) - Difficult Learning (23 reviews) - Expensive (22 reviews) ### 7. [Hex](https://www.g2.com/products/hex-tech-hex/reviews) Hex is the world’s best AI Analytics platform. With Hex, anyone can explore data using natural language, with or without code, all on trusted context, in one AI-powered platform. Get started now \> https://app.hex.tech/signup?source=g2 Get a demo \> https://hex.tech/request-a-demo/source=g2 **Average Rating:** 4.5/5.0 **Total Reviews:** 379 **User Satisfaction Scores:** - **Application:** 6.9/10 (Category avg: 8.5/10) - **Managed Service:** 6.8/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 5.1/10 (Category avg: 8.3/10) - **Ease of Admin:** 9.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Hex Tech](https://www.g2.com/sellers/hex-tech) - **Company Website:** https://hex.tech/ - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **Twitter:** @_hex_tech (6,762 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/hex-technologies/ (222 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Analyst, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 54% Mid-Market, 22% Small-Business #### Pros & Cons **Pros:** - Ease of Use (130 reviews) - SQL Queries (81 reviews) - Data Management (79 reviews) - SQL Querying (74 reviews) - Data Analysis (62 reviews) **Cons:** - Limited Features (45 reviews) - Missing Features (41 reviews) - Lacking Features (38 reviews) - Limited Visualization (30 reviews) - Data Management Issues (29 reviews) ### 8. [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial **Average Rating:** 4.4/5.0 **Total Reviews:** 150 **User Satisfaction Scores:** - **Application:** 5.8/10 (Category avg: 8.5/10) - **Managed Service:** 7.2/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 7.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com/us-en - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 34% Small-Business, 33% Enterprise #### Pros & Cons **Pros:** - Ease of Use (67 reviews) - Features (47 reviews) - Data Management (41 reviews) - Integrations (33 reviews) - Analytics (31 reviews) **Cons:** - Learning Curve (38 reviews) - Complexity (25 reviews) - Expensive (20 reviews) - Difficult Setup (17 reviews) - Difficulty (17 reviews) ### 9. [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:** 133 **User Satisfaction Scores:** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.5/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com/us-en - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Consultant - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 41% Small-Business, 31% Enterprise #### Pros & Cons **Pros:** - Ease of Use (76 reviews) - Model Variety (31 reviews) - Features (29 reviews) - AI Integration (28 reviews) - AI Capabilities (23 reviews) **Cons:** - Difficult Learning (21 reviews) - Complexity (20 reviews) - Learning Curve (19 reviews) - Expensive (17 reviews) - Improvement Needed (16 reviews) ### 10. [Snowflake](https://www.g2.com/products/snowflake/reviews) Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW). **Average Rating:** 4.6/5.0 **Total Reviews:** 664 **User Satisfaction Scores:** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.0/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.7/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc) - **Company Website:** https://www.snowflake.com - **Year Founded:** 2012 - **HQ Location:** San Mateo, CA - **Twitter:** @SnowflakeDB (237 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (10,857 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 44% Mid-Market, 43% Enterprise #### Pros & Cons **Pros:** - Ease of Use (87 reviews) - Scalability (67 reviews) - Data Management (66 reviews) - Features (64 reviews) - Integrations (61 reviews) **Cons:** - Expensive (52 reviews) - Cost (35 reviews) - Cost Management (32 reviews) - Learning Curve (25 reviews) - Complexity (20 reviews) ### 11. [MATLAB](https://www.g2.com/products/matlab/reviews) MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries. **Average Rating:** 4.5/5.0 **Total Reviews:** 745 **User Satisfaction Scores:** - **Application:** 8.6/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [MathWorks](https://www.g2.com/sellers/mathworks) - **Year Founded:** 1984 - **HQ Location:** Natick, MA - **Twitter:** @MATLAB (103,430 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1194036/ (7,860 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Student, Graduate Research Assistant - **Top Industries:** Higher Education, Research - **Company Size:** 42% Enterprise, 31% Small-Business #### Pros & Cons **Pros:** - Ease of Use (19 reviews) - Features (16 reviews) - Data Visualization (13 reviews) - Tools Variety (10 reviews) - Simulation (9 reviews) **Cons:** - Expensive (12 reviews) - Slow Performance (10 reviews) - High System Requirements (7 reviews) - Expensive Licensing (4 reviews) - Lagging Performance (4 reviews) ### 12. [Deep Learning VM Image](https://www.g2.com/products/deep-learning-vm-image/reviews) Deep Learning VM Images are pre-configured virtual machine images optimized for data science and machine learning tasks. These images come with essential machine learning frameworks and tools pre-installed, enabling users to deploy and scale machine learning models efficiently on Google Cloud's infrastructure. Key Features and Functionality: - Pre-installed Frameworks: Support for TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, catering to various machine learning needs. - Operating System Options: Based on Debian 11 and Ubuntu 22.04, providing flexibility and compatibility with different environments. - Comprehensive Python Environment: Includes Python 3.10 with a suite of libraries such as NumPy, SciPy, Matplotlib, Pandas, NLTK, Pillow, scikit-image, OpenCV, and scikit-learn, facilitating a robust development experience. - JupyterLab Integration: Offers JupyterLab notebook environments for rapid prototyping and interactive development. - GPU Acceleration: Equipped with the latest NVIDIA drivers and packages, including CUDA 11.x and 12.x, CuDNN, and NCCL, to leverage GPU capabilities for accelerated computation. Primary Value and User Solutions: Deep Learning VM Images streamline the setup process for machine learning projects by providing ready-to-use environments with pre-installed frameworks and tools. This reduces the time and effort required for configuration, allowing data scientists and machine learning practitioners to focus on model development and experimentation. The integration with Google Cloud's scalable infrastructure ensures that users can efficiently manage and scale their machine learning workloads, whether they require CPU or GPU resources. Regular updates and community support further enhance the reliability and performance of these VM images, making them a valuable resource for accelerating machine learning initiatives. **Average Rating:** 4.4/5.0 **Total Reviews:** 47 **User Satisfaction Scores:** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.4/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.5/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,840,340 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 52% Small-Business, 30% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (28 reviews) - Setup Ease (15 reviews) - Features (14 reviews) - Easy Integrations (11 reviews) - Easy Setup (11 reviews) **Cons:** - Expensive (15 reviews) - Cost (8 reviews) - Learning Difficulty (7 reviews) - Difficult Learning (6 reviews) - Dependency Issues (5 reviews) ### 13. [TensorFlow](https://www.g2.com/products/tensorflow/reviews) TensorFlow is an open-source machine learning library developed by the Google Brain Team, designed to facilitate the creation, training, and deployment of machine learning models across various platforms. It provides a comprehensive ecosystem that supports tasks ranging from simple data flow graphs to complex neural networks, enabling developers and researchers to build and deploy machine learning applications efficiently. Key Features and Functionality: - Flexible Architecture: TensorFlow's architecture allows for deployment across multiple platforms, including CPUs, GPUs, and TPUs, and supports various operating systems such as Linux, macOS, Windows, Android, and JavaScript. - Multiple Language Support: While primarily offering a Python API, TensorFlow also provides support for other languages, including C++, Java, and JavaScript, catering to a diverse developer community. - High-Level APIs: TensorFlow includes high-level APIs like Keras, which simplify the process of building and training models, making machine learning more accessible to beginners and efficient for experts. - Eager Execution: This feature allows for immediate evaluation of operations, facilitating intuitive debugging and dynamic graph building. - Distributed Computing: TensorFlow supports distributed training, enabling the scaling of machine learning models across multiple devices and servers without significant code modifications. Primary Value and Solutions Provided: TensorFlow addresses the challenges of developing and deploying machine learning models by offering a unified, scalable, and flexible platform. It streamlines the workflow from model conception to deployment, reducing the complexity associated with machine learning projects. By supporting a wide range of platforms and languages, TensorFlow empowers users to implement machine learning solutions in diverse environments, from research labs to production systems. Its comprehensive suite of tools and libraries accelerates the development process, fosters innovation, and enables the creation of sophisticated models that can tackle real-world problems effectively. **Average Rating:** 4.5/5.0 **Total Reviews:** 136 **User Satisfaction Scores:** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.4/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.7/10 (Category avg: 8.3/10) - **Ease of Admin:** 7.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [TensorFlow](https://www.g2.com/sellers/tensorflow) - **Year Founded:** 2016 - **HQ Location:** Centre Urbain Nord, TN - **Twitter:** @TensorFlow (378,445 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/tensorflow-tunis/ (1 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 50% Small-Business, 25% Mid-Market #### Pros & Cons **Pros:** - Machine Learning (23 reviews) - AI Integration (19 reviews) - Ease of Use (19 reviews) - Model Variety (18 reviews) - Scalability (14 reviews) **Cons:** - Steep Learning Curve (25 reviews) - Complexity (8 reviews) - Difficult Learning (8 reviews) - Error Handling (6 reviews) - Slow Performance (6 reviews) ### 14. [Saturn Cloud](https://www.g2.com/products/saturn-cloud-saturn-cloud/reviews) Saturn Cloud is a portable AI platform that installs securely in any cloud account. Access the best GPUs with no Kubernetes configuration or DevOps, enable AI/ML teams to develop, deploy, and manage ML models with any stack, and give IT security the controls that work for your enterprise. Customers include NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. Get started for free at: saturncloud.io **Average Rating:** 4.8/5.0 **Total Reviews:** 320 **User Satisfaction Scores:** - **Application:** 9.1/10 (Category avg: 8.5/10) - **Managed Service:** 9.1/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 9.1/10 (Category avg: 8.3/10) - **Ease of Admin:** 9.2/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Saturn Cloud](https://www.g2.com/sellers/saturn-cloud) - **Year Founded:** 2018 - **HQ Location:** New York, US - **Twitter:** @saturn_cloud (3,229 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/saturn-cloud/ (41 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Scientist, Student - **Top Industries:** Computer Software, Higher Education - **Company Size:** 82% Small-Business, 12% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (18 reviews) - GPU Performance (13 reviews) - Computing Power (10 reviews) - Setup Ease (10 reviews) - Easy Integrations (8 reviews) **Cons:** - Expensive (6 reviews) - Missing Features (5 reviews) - Complexity Issues (4 reviews) - Poor Documentation (4 reviews) - Difficult Setup (3 reviews) ### 15. [Wipro Holmes](https://www.g2.com/products/wipro-holmes/reviews) Wipro HOLMES is an Artificial Intelligence Platform that provide services for the development of digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics and drones to deliver cognitive enhancement to experience and productivity, accelerate process through automation and at the highest stage of maturity reach autonomous abilities **Average Rating:** 3.8/5.0 **Total Reviews:** 10 **User Satisfaction Scores:** - **Application:** 7.1/10 (Category avg: 8.5/10) - **Managed Service:** 7.9/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 6.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Wipro](https://www.g2.com/sellers/wipro) - **Year Founded:** 1945 - **HQ Location:** Bangalore - **Twitter:** @Wipro (513,896 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1318/ (264,517 employees on LinkedIn®) - **Ownership:** WIT **Reviewer Demographics:** - **Company Size:** 40% Enterprise, 30% Mid-Market #### Pros & Cons **Pros:** - Efficiency (3 reviews) - AI Integration (2 reviews) - Automation (2 reviews) - Data Access (2 reviews) - Time-saving (2 reviews) **Cons:** - Complex Interface (1 reviews) - Limited Customization (1 reviews) - Steep Learning Curve (1 reviews) ### 16. [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews) Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management. **Average Rating:** 4.2/5.0 **Total Reviews:** 45 **User Satisfaction Scores:** - **Application:** 8.6/10 (Category avg: 8.5/10) - **Managed Service:** 9.1/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 9.2/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,220,862 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 33% Enterprise, 31% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (3 reviews) - AI Integration (2 reviews) - Computing Power (2 reviews) - Efficiency (2 reviews) - Fast Processing (2 reviews) **Cons:** - Expensive (3 reviews) - Complexity (2 reviews) - Complexity Issues (2 reviews) - Learning Curve (2 reviews) - Difficult Learning (1 reviews) ### 17. [AWS Trainium](https://www.g2.com/products/aws-trainium/reviews) Get high performance for deep learning and generative AI training while lowering costs **Average Rating:** 4.5/5.0 **Total Reviews:** 14 **User Satisfaction Scores:** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.6/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.9/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,220,862 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Reviewer Demographics:** - **Company Size:** 60% Small-Business, 20% Mid-Market ### 18. [Posit](https://www.g2.com/products/posit-posit/reviews) Posit, formerly RStudio, is dedicated to advancing open-source software for data science, scientific research, and technical communication. Trusted by millions of users, including 25% of the Fortune Global 100, Posit empowers organizations to drive innovation and informed decision-making. We focus on making data science more open, intuitive, accessible, and collaborative, offering tools that enable powerful insights and smarter, data-driven decisions. We build popular open-source tools like the RStudio IDE and Shiny, as well as enterprise-level tools for professional data science teams, including Posit Team, Posit Connect, Posit Workbench, and Posit Package Manager. **Average Rating:** 4.5/5.0 **Total Reviews:** 563 **User Satisfaction Scores:** - **Application:** 8.4/10 (Category avg: 8.5/10) - **Managed Service:** 8.3/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Posit](https://www.g2.com/sellers/posit) - **Year Founded:** 2009 - **HQ Location:** Boston, MA - **Twitter:** @posit_pbc (121,263 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1978648/ (448 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Research Assistant, Graduate Research Assistant - **Top Industries:** Higher Education, Information Technology and Services - **Company Size:** 49% Enterprise, 27% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (13 reviews) - Features (9 reviews) - Open Source (7 reviews) - Customer Support (5 reviews) - Easy Integrations (5 reviews) **Cons:** - Slow Performance (7 reviews) - Learning Curve (4 reviews) - Performance Issues (4 reviews) - Steep Learning Curve (4 reviews) - Lagging Performance (3 reviews) ### 19. [IBM Watson Studio](https://www.g2.com/products/ibm-watson-studio/reviews) IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle--simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise. As a key offering of IBM Cloud Pak for Data, a unified data and AI platform, Watson Studio integrates seamlessly with data management services, data privacy and security capabilities, AI application tooling, open source frameworks, and a robust technology ecosystem. It unites teams and empowers businesses to build the modern information architecture that AI requires and infuse it across the organization. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same platform by providing the best of open source tools along with visual, drag-and-drop capabilities. It enables organizations to tap into data assets and inject predictions into business processes and modern applications—helping them maximize their business value. It's suited for hybrid multicloud environments that demand mission-critical performance, security, and governance. Features include: • AutoAI that eliminates time-consuming, repetitive tasks by automating data preparation, model development, feature engineering and hyperparameter optimization. • Text Analytics for uncovering insights from unstructured data • Drag-and-drop visual model-building with SPSS Modeler • Broad data access – flat files, spreadsheets, major relational databases • Sophisticated graphics engine for building stunning visualizations • Support for Python 3 Notebooks Watson Studio is available via several deployment options: • IBM Cloud Pak for Data – An open, extensible data and AI platform that runs on any cloud • IBM Cloud Pak for Data System – A hybrid cloud, on-premises platform-in-a-box • IBM Cloud Pak for Data as a Service – A set of IBM Cloud Pak for Data platform services fully managed on the IBM Cloud **Average Rating:** 4.2/5.0 **Total Reviews:** 160 **User Satisfaction Scores:** - **Application:** 9.2/10 (Category avg: 8.5/10) - **Managed Service:** 9.3/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.9/10 (Category avg: 8.3/10) - **Ease of Admin:** 7.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 50% Enterprise, 30% Small-Business #### Pros & Cons **Pros:** - AI Capabilities (4 reviews) - AI Technology (4 reviews) - Ease of Use (4 reviews) - Machine Learning (4 reviews) - AI Integration (3 reviews) **Cons:** - Expensive (3 reviews) - Learning Curve (3 reviews) - Steep Learning Curve (3 reviews) - Complex Interface (1 reviews) - Complexity (1 reviews) ### 20. [Alteryx](https://www.g2.com/products/alteryx/reviews) Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. **Average Rating:** 4.6/5.0 **Total Reviews:** 647 **User Satisfaction Scores:** - **Application:** 8.7/10 (Category avg: 8.5/10) - **Managed Service:** 8.0/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.9/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx) - **Company Website:** https://www.alteryx.com - **Year Founded:** 1997 - **HQ Location:** Irvine, CA - **Twitter:** @alteryx (26,204 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Financial Services, Accounting - **Company Size:** 62% Enterprise, 22% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (333 reviews) - Automation (148 reviews) - Intuitive (132 reviews) - Easy Learning (102 reviews) - Efficiency (102 reviews) **Cons:** - Expensive (88 reviews) - Learning Curve (80 reviews) - Missing Features (62 reviews) - Learning Difficulty (55 reviews) - Slow Performance (41 reviews) ### 21. [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews) Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact. **Average Rating:** 4.6/5.0 **Total Reviews:** 490 **User Satisfaction Scores:** - **Application:** 8.3/10 (Category avg: 8.5/10) - **Managed Service:** 8.2/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 7.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.4/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7) - **Company Website:** https://www.altair.com/ - **Year Founded:** 1985 - **HQ Location:** Troy, MI - **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (3,169 employees on LinkedIn®) - **Ownership:** NASDAQ:ALTR **Reviewer Demographics:** - **Who Uses This:** Student, Data Scientist - **Top Industries:** Higher Education, Education Management - **Company Size:** 43% Small-Business, 30% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (9 reviews) - Machine Learning (8 reviews) - AI Integration (6 reviews) - AI Technology (5 reviews) - Automation (5 reviews) **Cons:** - Complexity (4 reviews) - Large Dataset Handling (3 reviews) - Slow Performance (3 reviews) - Complexity Issues (2 reviews) - Complex Usage (2 reviews) ### 22. [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews) Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems. **Average Rating:** 4.3/5.0 **Total Reviews:** 85 **User Satisfaction Scores:** - **Application:** 8.8/10 (Category avg: 8.5/10) - **Managed Service:** 8.9/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 8.7/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.3/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,090,464 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 39% Enterprise, 34% Small-Business #### Pros & Cons **Pros:** - Ease of Use (3 reviews) - Features (3 reviews) - Customer Support (2 reviews) - Data Management (2 reviews) - Efficiency (2 reviews) **Cons:** - Learning Curve (3 reviews) - Difficult Navigation (2 reviews) - UX Improvement (2 reviews) - Complex Interface (1 reviews) - Difficult Learning (1 reviews) ### 23. [Cloudera Data Engineering](https://www.g2.com/products/cloudera-data-engineering/reviews) Cloudera Data Engineering is a comprehensive, cloud-native service designed to empower enterprise data teams to securely build, automate, and scale data pipelines across diverse environments, including public clouds, on-premises data centers, and hybrid setups. By leveraging open-source technologies such as Apache Spark, Apache Iceberg, and Apache Airflow, it provides a flexible and efficient platform for managing complex data workflows. Key Features and Functionality: - Containerized Apache Spark on Iceberg: Facilitates scalable and governed data pipelines by running Spark workloads on Iceberg within containerized environments, ensuring flexibility and portability. - Self-Service Orchestration with Apache Airflow: Enables users to design and automate complex workflows through a user-friendly interface, simplifying task management and dependency control. - Interactive Sessions and External IDE Connectivity: Supports on-demand interactive sessions for rapid testing and development, with seamless integration to external Integrated Development Environments (IDEs) like VSCode and Jupyter Notebook. - Built-in Change Data Capture (CDC): Ensures data freshness by capturing and processing row-level changes from source systems, facilitating continuous updates to downstream applications. - Metadata Management and Lineage: Provides comprehensive visibility into data pipelines with integrated metadata management and lineage tracking, enhancing governance and compliance. - Rich APIs and Visual Troubleshooting: Offers robust APIs for automation and integration, along with visual tools for real-time monitoring and performance tuning, aiding in efficient troubleshooting. Primary Value and Problem Solving: Cloudera Data Engineering addresses the challenges of managing complex data pipelines by offering a unified platform that enhances productivity, ensures data integrity, and optimizes resource utilization. It empowers data teams to: - Accelerate Data Pipeline Development: By automating workflows and providing intuitive tools, it reduces the time and effort required to build and deploy data pipelines. - Ensure Data Quality and Governance: Integrated metadata management and lineage tracking provide transparency and control, ensuring data accuracy and compliance. - Optimize Costs and Resources: Features like workload-level observability, autoscaling, and zero-ETL data sharing help in monitoring and optimizing pipeline costs, leading to a lower total cost of ownership. By unifying structured and unstructured data processing with open standards, Cloudera Data Engineering enables organizations to harness the full potential of their data assets, driving informed decision-making and innovation. **Average Rating:** 4.7/5.0 **Total Reviews:** 23 **User Satisfaction Scores:** - **Application:** 9.3/10 (Category avg: 8.5/10) - **Managed Service:** 9.0/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 9.6/10 (Category avg: 8.3/10) - **Ease of Admin:** 9.5/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera) - **Year Founded:** 2008 - **HQ Location:** Palo Alto, CA - **Twitter:** @cloudera (106,568 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,387 employees on LinkedIn®) - **Phone:** 888-789-1488 **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Mid-Market, 35% Enterprise ### 24. [Domo](https://www.g2.com/products/domo/reviews) Domo's AI and Data Products Platform empowers organizations to turn data into actionable insights and solutions. It allows users to seamlessly connect diverse data sources, prepare data for use, and generate dynamic reports and visualizations—all within a single interface. With built-in AI and automation capabilities, teams can easily build and use AI agents, streamline workflows, and create tailored solutions. **Average Rating:** 4.3/5.0 **Total Reviews:** 983 **User Satisfaction Scores:** - **Application:** 5.9/10 (Category avg: 8.5/10) - **Managed Service:** 6.4/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 5.8/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Domo](https://www.g2.com/sellers/domo) - **Company Website:** https://www.domo.com - **Year Founded:** 2010 - **HQ Location:** American Fork, UT - **Twitter:** @Domotalk (63,693 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/25237/ (1,334 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Analyst, Business Analyst - **Top Industries:** Computer Software, Marketing and Advertising - **Company Size:** 49% Mid-Market, 29% Enterprise #### Pros & Cons **Pros:** - Ease of Use (248 reviews) - Data Visualization (116 reviews) - Intuitive (95 reviews) - Easy Integrations (93 reviews) - Integrations (88 reviews) **Cons:** - Learning Curve (66 reviews) - Missing Features (59 reviews) - Data Management Issues (55 reviews) - Expensive (45 reviews) - Complexity (43 reviews) ### 25. [IBM SPSS Modeler](https://www.g2.com/products/ibm-spss-modeler/reviews) The IBM SPSS Modeler is a leading, visual data science and machine learning solution. It helps enterprises accelerate time to value and desired outcome by speeding the operational tasks for data scientists. Leading organizations worldwide rely on IBM for data discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets. The IBM SPSS Modeler empowers organizations to tap data assets and modern applications with complete, out-of-box algorithms and models, suited for hybrid, multi-cloud environments with robust governance and security posture. • Take advantage of open source based innovation including R or Python • Empower data scientists of all skills – programmatic and visual • Exploit multi-cloud approach - on-prem, public or private clouds • Start small and scale to enterprise-wide, governed approach **Average Rating:** 4.0/5.0 **Total Reviews:** 128 **User Satisfaction Scores:** - **Application:** 7.5/10 (Category avg: 8.5/10) - **Managed Service:** 7.6/10 (Category avg: 8.2/10) - **Natural Language Understanding:** 6.4/10 (Category avg: 8.3/10) - **Ease of Admin:** 8.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Top Industries:** Higher Education, Education Management - **Company Size:** 53% Enterprise, 24% Mid-Market #### Pros & Cons **Pros:** - Analysis Capabilities (1 reviews) - Analytics (1 reviews) - Data Access (1 reviews) - Data Management (1 reviews) - Data Visualization (1 reviews) **Cons:** - Expensive (1 reviews) - Expensive Licensing (1 reviews) ## Parent Category [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## Related Categories - [Predictive Analytics Software](https://www.g2.com/categories/predictive-analytics) - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) --- ## Buyer Guide ### What You Should Know About Data Science and Machine Learning Platforms ### What are data science and machine learning (DSML) platforms? 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 data science, of which [artificial intelligence (AI)](https://www.g2.com/articles/what-is-artificial-intelligence) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions. One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning. ### Types of DSML platforms Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy [machine learning models](https://www.g2.com/articles/what-is-machine-learning). However, they differ in terms of the data types supported and the method and manner of deployment.  **Cloud** **data science and machine learning platforms** With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both 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 that have been deployed. **On-premises** **data science and machine learning platforms** 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 several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as [HIPAA](https://www.g2.com/glossary/hipaa-definition), require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary. **Edge** **platforms** Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of [data centers](https://www.g2.com/glossary/data-center-definition) that process and store data locally before being sent to a centralized storage center or cloud. [Edge computing](https://learn.g2.com/trends/edge-computing) optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. ** ** ### What are the common features of data science and machine learning solutions? The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models. **Data preparation:** Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices. Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for [data cleansing](https://www.g2.com/articles/data-cleaning) and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start. **Model training:** Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data. Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are [supervised learning and unsupervised learning](https://www.g2.com/articles/supervised-vs-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 that 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. **Model deployment:** The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more. ### What are the benefits of using DSML engineering platforms? Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them 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 can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork. **Simplify and scale data science:** Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets. **Experimentation:** 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. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for [deep learning](https://www.g2.com/articles/deep-learning), which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation. ### Who uses data science and machine learning products? Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include 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 them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that 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:** 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 data science and machine learning platforms to bring AI into their organizations. **Professional data scientists:** Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and 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 data science and machine learning platforms? Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely: [AI & machine learning operationalization software](https://www.g2.com/categories/ai-machine-learning-operationalization) **:** Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models. [Machine learning software](https://www.g2.com/categories/machine-learning) **:** Data science and machine learning platforms are great for the full-scale development of models, whether that be for [computer vision](https://learn.g2.com/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. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, [Bayesian networks](https://www.g2.com/articles/artificial-intelligence-terms#:~:text=Bayesian%20network%3A%20also%20known%20as%20the%20Bayes%20network%2C%20Bayes%20model%2C%20belief%20network%2C%20and%20decision%20network%2C%20is%20a%20graph%2Dbased%20model%20representing%20a%20set%20of%20variables%20and%20their%20dependencies.%C2%A0), clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions. ### **Software and services related to data science and machine learning engineering platforms** Related solutions that can be used together with DSML 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 data science and machine learning 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 many 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, which allows business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data ingested by data science and machine learning platforms. [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](https://www.g2.com/articles/natural-language-processing) 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](https://www.g2.com/articles/voice-recognition) and [natural language generation (NLG)](https://www.g2.com/categories/natural-language-generation-nlg), which converts data into understandable human language. Some examples of NLP uses include [chatbots](https://www.g2.com/categories/chatbots), translation applications, and [social media monitoring tools](https://www.g2.com/categories/social-media-listening-tools) that scan social media networks for mentions. ### Challenges with DSML platforms Software solutions can come with their own set of challenges.  **Data requirements:** A great deal of data is required for most AI algorithms to learn what is needed. 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 necessary actions. 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 by 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 DSML engineering 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:** AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to 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 choose the best data science and machine learning (DSML) platform #### Requirements gathering (RFI/RFP) for DSML platforms If a company is just starting out and looking to purchase its 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, it needs to 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 deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful. #### Compare DSML products **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 a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition. #### Selection of DSML platforms **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, 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 to recommend 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. ### Cost of data science and machine learning platforms As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs.  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 frequently not have as many features and may have usage caps. DSML 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 data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is 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 data science and machine learning platforms **How are DSML software tools 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 that be 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 DSML platform implementation?** It may require many people or 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, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management. **What is the implementation process for data science and machine learning products?** In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better 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 is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and 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. **When should you implement DSML tools?** As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, 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.  ### Data science and machine learning 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 is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it. Using embedded AI inside software like [CRM](https://www.g2.com/categories/crm), [marketing automation](https://www.g2.com/categories/marketing-automation), and [analytics solutions](https://www.g2.com/categories/analytics-tools-software) allows us 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 same way cloud deployment and mobile capabilities have over the past decade. 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 provide [MLaaS](https://www.g2.com/articles/machine-learning-as-a-service) for other enterprises. Developers quickly take advantage of these prebuilt algorithms and solutions by feeding them their 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 companies continue to rely on these microservices and the need for AI increases. **Explainability** When it comes to machine learning algorithms, especially deep learning, it may be 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. Data science and machine learning platforms are increasingly including tools for explainability, which helps users build explainability into their models and help them meet data explainability requirements in legislation such as the European Union's privacy law and the GDPR.