# Best MLOps Platforms - Page 5 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* 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. ## How Many MLOps Platforms Products Does G2 Track? **Total Products under this Category:** 251 ### Category Stats (May 2026) - **Average Rating**: 4.5/5 (↑0.01 vs Apr 2026) - **New Reviews This Quarter**: 55 - **Buyer Segments**: Small-Business 54% │ Mid-Market 31% │ Enterprise 14% - **Top Trending Product**: Cloudera Data Platform (+0.155) *Last updated: May 18, 2026* ## How Does G2 Rank MLOps Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 6,700+ Authentic Reviews - 251+ 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. ## Which MLOps Platforms Is Best for Your Use Case? - **Leader:** [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - **Highest Performer:** [ILUM](https://www.g2.com/products/ilum-ilum/reviews) - **Easiest to Use:** [Roboflow](https://www.g2.com/products/roboflow/reviews) - **Top Trending:** [Arize AI](https://www.g2.com/products/arize-ai/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### Alteryx 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. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1910&secure%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bplacement_resource_ids%5D%5B%5D=684&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&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%3Fpage%3D11%26source%3Dsearch&secure%5Btoken%5D=733f5fe248797fbe2ad720402b1d9f4e6cce7a97ef9836d3e6da385facedad30&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial) --- ## What Are the Top-Rated MLOps Platforms Products in 2026? ### 1. [Model Share](https://www.g2.com/products/model-share/reviews) AI innovation faster & easier than ever. Model Share AI’s mission is to give AI innovators superpowers in a few lines of code. Create your first Model Playground to deploy, track, and improve ML models faster and easier than ever - at an unbeatable price. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Model Share?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 10.0/10 (Category avg: 8.7/10) - **Framework Flexibility:** 10.0/10 (Category avg: 8.7/10) **Who Is the Company Behind Model Share?** - **Seller:** [Model Share](https://www.g2.com/sellers/model-share) - **Year Founded:** 2022 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/model-share-ai/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 2. [Modelshop](https://www.g2.com/products/modelshop-inc-modelshop/reviews) Modelshop is a platform that allows organizations to quickly create analytic applications. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Modelshop?** - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind Modelshop?** - **Seller:** [Modelshop](https://www.g2.com/sellers/modelshop) - **Year Founded:** 2015 - **HQ Location:** Newark, US - **Twitter:** @modelshopinc (68 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/modelshop-inc/ (8 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 3. [Modular](https://www.g2.com/products/modular/reviews) Modular is an AI software developer platform that unifies the development and deployment of AI for everyone. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Modular?** - **Ease of Use:** 6.7/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 8.3/10 (Category avg: 8.7/10) - **Framework Flexibility:** 10.0/10 (Category avg: 8.7/10) **Who Is the Company Behind Modular?** - **Seller:** [Modular](https://www.g2.com/sellers/modular) - **Year Founded:** 2022 - **HQ Location:** Everywhere, US - **LinkedIn® Page:** https://linkedin.com/company/modular-ai (268 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 4. [NeoPulse AI Studio](https://www.g2.com/products/neopulse-ai-studio/reviews) The NeoPulse Framework enables organizations to manage their entire AI workflow and infrastructure from one place. This means that DevOps, data engineers and ML engineers work from one interface instead of using separate applications. Using NeoPulse, a data engineer can assemble training data sets. The machine learning engineer can create AI models. The DevOps engineer can deploy and manage the solution without ever leaving the NeoPulse environment. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate NeoPulse AI Studio?** - **Ease of Use:** 6.7/10 (Category avg: 8.8/10) **Who Is the Company Behind NeoPulse AI Studio?** - **Seller:** [AI Dynamics](https://www.g2.com/sellers/ai-dynamics) - **Year Founded:** 2015 - **HQ Location:** Bellevue, US - **LinkedIn® Page:** https://www.linkedin.com/company/aidynamics/ (16 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise #### What Are NeoPulse AI Studio's Pros and Cons? **Pros:** - Capabilities (1 reviews) - Data Analytics (1 reviews) - Ease of Use (1 reviews) - Easy Integrations (1 reviews) - Model Management (1 reviews) **Cons:** - Lack of Features (1 reviews) - Missing Features (1 reviews) - Model Limitations (1 reviews) ### 5. [ParallelM MLOps](https://www.g2.com/products/parallelm-mlops/reviews) ParallelM's MCenter helps Data Scientists deploy, manage and govern ML models in production. Just import your existing model from your favorite notebook and then create data connections or a REST endpoint for model serving with the drag-and-drop pipeline builder. Advanced monitoring automatically creates alerts when models are not operating as expected due to changing data. With built-in model governance, every action is controlled and tracked including model versioning and who can promote models into production to ensure compliance with regulations. **Average Rating:** 3.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind ParallelM MLOps?** - **Seller:** [ParallelM](https://www.g2.com/sellers/parallelm) - **HQ Location:** Sunnyvale, US - **Twitter:** @ParallelM_AI (139 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/9369269 (3 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 6. [Payatu AI/ML Security Audit](https://www.g2.com/products/payatu-ai-ml-security-audit/reviews) Testing AI/ML systems requires domain knowledge. At Payatu, our AI/ML domain experts have orchestrated ways to help you secure your intelligent application against esoteric and potentially severe security and privacy threats. ML Security assessment coverage 1)Understanding the Application a)Use-case b)Product Capabilities c)Implementations 2)Attack Surface Identification a)Understanding the ML Pipeline b)Gather Test Cases If Any 3)Threat Modeling a)Actors and Entity Boundaries b)Possible Attacks identification on Exposed endpoints c)Possible attack vectors 4)Model Endpoints a)Understand ways with which end users communicate with model b)Simulate end user interaction 5)Adversarial Learning Attack a)Craft inputs to bypass fool classifiers b)Use custom built tools c)Automated generation of theoretically infinite zero day samples as possible 6)Model Stealing Attack a)Model deployed locally or remotely b)Reverse engineer deployed application Custom built scripts for black-box model stealing attacks 7)Model Skewing and Data poisoning Attack a)Simulate Feedback loops abused by attackers b)Quantify the skewness of model 8)Model Inversion and inference a)Get access to model via valid or compromised communication channels b)Infer sensitive samples from training dataset from model 9)Framework/ Network/Application assessment a)Identify traditional vulnerabilities in application b)Leverage them for above attacks 10)Reporting and Mitigation a)Comprehensive Mitigation Proposal b)Work With Developer/SME for implementations **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Payatu AI/ML Security Audit?** - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) - **Scalability:** 8.3/10 (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 Payatu AI/ML Security Audit?** - **Seller:** [Payatu](https://www.g2.com/sellers/payatu) - **Year Founded:** 2011 - **HQ Location:** Pune, IN - **LinkedIn® Page:** https://www.linkedin.com/company/payatu (135 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 7. [Plainsight](https://www.g2.com/products/plainsight/reviews) Plainsight is the leader in proven vision AI. Providing the unique combination of AI strategy, a vision AI platform, and deep learning expertise, Plainsight develops, implements, and oversees transformative computer vision solutions for enterprises. Through the widest breadth of managed services and a vision AI platform for centralized processes and standardized pipelines, Plainsight makes computer vision repeatable and accountable across all enterprise vision AI initiatives. Plainsight solves problems where others have failed and empowers businesses across industries to realize the full potential of their visual data with the lowest barriers to production, fastest value generation, and monitoring for long-term success. For more information, visit https://plainsight.ai. **Average Rating:** 4.5/5.0 **Total Reviews:** 5 **How Do G2 Users Rate Plainsight?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Plainsight?** - **Seller:** [Plainsight](https://www.g2.com/sellers/plainsight) - **Year Founded:** 2024 - **HQ Location:** Greater Seattle Area, US - **Twitter:** @PlainsightAI (1,460 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/plainsightai/ (22 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 80% Small-Business, 20% Mid-Market #### What Are Plainsight's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - AI Integration (1 reviews) - AI Modeling (1 reviews) - AI Technology (1 reviews) - Innovation (1 reviews) **Cons:** - Required Expertise (1 reviews) - Required Knowledge (1 reviews) ### 8. [PoplarML](https://www.g2.com/products/poplarml/reviews) PoplarML enables the deployment of production-ready scalable ML systems with minimal engineering effort. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate PoplarML?** - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (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 PoplarML?** - **Seller:** [PoplarML](https://www.g2.com/sellers/poplarml) - **Year Founded:** 2022 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/poplarml/ (2 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are PoplarML's Pros and Cons? **Pros:** - Model Management (1 reviews) **Cons:** - Missing Features (1 reviews) ### 9. [Qwak](https://www.g2.com/products/qwak/reviews) Qwak is a fully managed, accessible, and reliable AI Platform that contains everything you and your team need to build high-quality AI applications simply. The platform delivers a production focused approach that enables ML engineers and data science practitioners to deliver models into production faster than ever before. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Qwak?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Qwak?** - **Seller:** [Qwak](https://www.g2.com/sellers/qwak) - **Year Founded:** 2020 - **HQ Location:** New York, US - **Twitter:** @Qwak_ai (208 Twitter followers) - **LinkedIn® Page:** https://linkedin.com/company/qwakai (48 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 10. [Replicate](https://www.g2.com/products/replicate-replicate/reviews) Replicate runs and fine-tune open-source models. Deploy custom models at scale. All with one line of code. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Replicate?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) **Who Is the Company Behind Replicate?** - **Seller:** [Replicate](https://www.g2.com/sellers/replicate) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Replicate's Pros and Cons? **Pros:** - AI Capabilities (1 reviews) - Ease of Use (1 reviews) **Cons:** - Model Limitations (1 reviews) ### 11. [Robovision](https://www.g2.com/products/robovision/reviews) Robovision is a compute-vision company and a pioneer in 3D deep learning. With over a decade of experience, Robovision has developed the Robovision Platform and Robovision Edge to solve the challenge of managing changes in dynamic environments. Companies can use its end-to-end solution to quickly develop, deploy and retrain AI without technical dependency. This enables them to move AI into production at scale. With a global partner network of system integrators, machine builders, device builders and service providers, Robovision can accelerate customer transformation and drive co-innovation in a variety of use cases. For instance, Robovision helps build the world’s first fully automated machine that recognises, picks and pots plant cuttings. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Robovision?** - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind Robovision?** - **Seller:** [Robovision](https://www.g2.com/sellers/robovision) - **Year Founded:** 2012 - **HQ Location:** Gent, BE - **LinkedIn® Page:** https://be.linkedin.com/company/robovisionai (115 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 12. [SolasAI AI Bias Detection & Mitigation Library](https://www.g2.com/products/solasai-ai-bias-detection-mitigation-library/reviews) SolasAI is software that detects and removes bias & discrimination from a customer's decisioning models. It works in credit & insurance underwriting, predictive marketing, healthcare, and employment to name a few use cases. We use AI to fix AI. We provide trust & transparency into artificial intelligence, machine learning, and standard statistical models. We have over 45 years experience doing this, so our software is designed by the people that large banks, insurers, and healthcare providers trust to help them identify and lower their risk. If you are tired of paying expensive experts who can't seem to agree, and then leave the hard part of fixing the problems to your expensive and over worked data scientists, then SolasAI is for you. We follow the latest decisions and signals from courts, regulators, and law makers, as well as the latest and greatest technology trends for AI and fairness as a whole. This is built into SolasAI so you don't have to figure it out yourself. SolasAI is also flexible enough to adjust to your business without exposing you to unnecessary risks. We help you identify the fairest and best performing alternatives, and then create just the right amount of data and justification for you to communicate with auditors, regulators, or other stakeholders. We believe in fairness, but we are practical problem solvers. Bad decisions are bad decisions. We help you make the best decisions while expanding your business and uncovering new possibilities through fairness. Modelers, are you tired of tools trying to replace you or call your 'babies ugly?' Well SolasAI is your co-pilot to help take your great data science and insights and ensure you also achieve amazing fairness. We solve this together using cool data science. We are your partner and not your competition! **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind SolasAI AI Bias Detection & Mitigation Library?** - **Seller:** [SolasAI, Inc](https://www.g2.com/sellers/solasai-inc) - **Year Founded:** 2021 - **HQ Location:** Philadelphia, US - **Twitter:** @solas_ai (76 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/solasai/ (18 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 13. [Tecton](https://www.g2.com/products/tecton/reviews) A feature store is a data platform that makes it easy to build, deploy, and use features for machine learning. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Tecton?** - **Seller:** [Tecton](https://www.g2.com/sellers/tecton) - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/tectonai/ (167 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 14. [Tenyks](https://www.g2.com/products/tenyks/reviews) Tenyks.ai is an innovative MLOps platform that empowers computer vision teams to expedite the development of production-ready models. With its state-of-the-art features, Tenyks.ai enables users to host data securely in their enterprise cloud storage and expedite the loading of datasets. By visualizing, identifying, and correcting data issues, users can ensure the integrity and quality of their datasets. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Tenyks?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 10.0/10 (Category avg: 8.7/10) - **Framework Flexibility:** 10.0/10 (Category avg: 8.7/10) **Who Is the Company Behind Tenyks?** - **Seller:** [Tenyks](https://www.g2.com/sellers/tenyks) - **Year Founded:** 2019 - **HQ Location:** Cambridge, GB - **LinkedIn® Page:** https://www.linkedin.com/company/tenyks/ (16 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 15. [The Researcher Workspace](https://www.g2.com/products/the-researcher-workspace/reviews) Built on Neuralith, RSpace gives R&D teams a lab-friendly cockpit where they can search literature, patents, and experimental data, launch multi-step agent workflows, and receive fully cited answers. It brings the power of the digital workforce to deep-knowledge discovery and innovation. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate The Researcher Workspace?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) - **Scalability:** 10.0/10 (Category avg: 9.0/10) - **Metrics:** 10.0/10 (Category avg: 8.7/10) - **Framework Flexibility:** 10.0/10 (Category avg: 8.7/10) **Who Is the Company Behind The Researcher Workspace?** - **Seller:** [Iris.ai](https://www.g2.com/sellers/iris-ai-081b600a-b52d-44ed-922c-0d5c43382dff) - **Year Founded:** 2015 - **HQ Location:** Norway , NO - **LinkedIn® Page:** https://linkedin.com/company/iris-ai (35 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are The Researcher Workspace's Pros and Cons? **Pros:** - Ease of Use (1 reviews) ### 16. [Widget Brain](https://www.g2.com/products/widget-brain/reviews) Widget Brain is the leading provider of AI services for workforce management in the fields of retail, hospitality and industry. Widget Brain's word-class algorithms and endless scalability deliver the best schedules ever made that ensures compliance with local laws, improves business performance and creates happy employees. Widget Brain's AI services for workforce optimization include: - Capacity Planning: Assess and match long-term supply and demand to prepare for future resource requirements and operational adjustments. - Demand Forecasting: Predict future demand for strategic staffing and planning for the impact of holidays, seasonality, and events. - Shift Creation: Minimize costs, centralize scheduling, and create optimal shifts while maintaining desired service levels. - Shift Filling: Assign shifts to employees. Reduce overtime and maximize compliance with labor laws and employee preferences. - Compliance Checking: Check schedule versus actuals to determine premium payments and flag potential compliance issues, considering attestation data. Widget Brain’s AI services and approach to planning allow you to establish and execute your company standards as a balanced mix of costs, performance and employee happiness across all locations. This not only results in more consistent and predictable results but it also enables your GM’s and managers to reallocate the time and energy to refocus on managing your business and staff. In turn, this also reduces their stress and empowers them to run your business and lead your staff, better. User's of Widget Brain's services report 100% compliance with labor laws and regulations, 5-10% labor cost reduction and 76% of employees that confirm fairer schedules. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind Widget Brain?** - **Seller:** [WidgetBrain](https://www.g2.com/sellers/widgetbrain) - **Year Founded:** 2005 - **HQ Location:** Stockholm, Stockholm County, Sweden - **LinkedIn® Page:** https://linkedin.com/company/quinyx (301 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 17. [Yields for Performance](https://www.g2.com/products/yields-for-performance/reviews) Chiron App is an award-winning data science platform and a model validation tool specifically designed to equip model risk managers, data scientists, and model validators with an AI-driven tool that has all the functionalities to efficiently manage model risk. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Yields for Performance?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Yields for Performance?** - **Seller:** [Yields.io](https://www.g2.com/sellers/yields-io) - **Year Founded:** 2017 - **HQ Location:** Ghent, BE - **LinkedIn® Page:** https://www.linkedin.com/company/yields (45 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 18. [0Ptikube](https://www.g2.com/products/0ptikube/reviews) 0ptikube is a powerful and intuitive visualization tool designed to help users understand and manage their Kubernetes clusters with ease. By providing real-time monitoring and resource optimization capabilities, 0ptikube enables users to identify bottlenecks and enhance infrastructure performance using AI-driven insights. Key Features: - Real-time Monitoring: Monitor your Kubernetes cluster in real-time with a custom dashboard, offering immediate insights into system performance. - Display Modes: Visualize resource usage at the pod level or obtain a comprehensive cluster overview, facilitating better resource management. - Resource Optimization: Identify resource bottlenecks and optimize your infrastructure for improved performance using AI-driven analysis. The primary value of 0ptikube lies in its ability to simplify Kubernetes cluster management through enhanced visualization and monitoring. By offering real-time insights and AI-powered optimization, it addresses the challenges of resource bottlenecks and performance inefficiencies, enabling users to maintain a robust and efficient infrastructure. **Who Is the Company Behind 0Ptikube?** - **Seller:** [0ptikube](https://www.g2.com/sellers/0ptikube) - **Year Founded:** 2024 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/0ptikube/ (3 employees on LinkedIn®) ### 19. [3D Continuum](https://www.g2.com/products/3d-continuum/reviews) 3D Continuum is a technology company dedicated to enhancing operational efficiency and safety within the heavy industry sector through advanced artificial intelligence (AI) solutions. By integrating human expertise with machine intelligence, 3D Continuum offers a cloud-based platform that delivers real-time strategic insights and actionable intelligence, enabling industrial companies to optimize production processes and reduce costs. Key Features and Functionality: - AI-Driven Data Analytics: The platform processes vast amounts of industrial data, uncovering insights that are often inaccessible through traditional methods. - Real-Time Strategic Insights: Users receive immediate, actionable intelligence to enhance productivity and operational decision-making. - Bias Reduction: By leveraging AI, the system minimizes human biases, leading to more objective and effective strategies. - Data Integration: The platform consolidates data from various sources, breaking down silos and providing a comprehensive view of operations. Primary Value and Solutions: 3D Continuum addresses the critical issue of unstructured and underutilized data in the heavy industry, which contributes to significant financial losses and safety risks. By transforming raw data into reliable, actionable insights, the platform empowers companies to improve production efficiency, enhance safety measures, and achieve substantial cost savings. This innovative approach not only optimizes current operations but also positions businesses for sustainable growth in an increasingly data-driven world. **Who Is the Company Behind 3D Continuum?** - **Seller:** [3D Continuum](https://www.g2.com/sellers/3d-continuum) - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/3dcontinuum/ (1 employees on LinkedIn®) ### 20. [525K Global Solutions](https://www.g2.com/products/525k-global-solutions/reviews) 525K Global Solutions offers a cutting-edge supply chain optimization platform designed to help logistics operators of all sizes enhance their operations efficiently. Recognizing that there are only 525,000 minutes in a year, the company emphasizes rapid optimization to maximize productivity within this limited timeframe. Key Features and Functionality: - Supply Chain Visibility: Provides comprehensive insights into the entire supply chain, enabling businesses to monitor and manage operations effectively. - Automated Process Controls: Implements automated controls tailored to each partner's specific logistics workflows, ensuring seamless and efficient operations. - Actionable Insights: Translates data into actionable strategies aligned with partners' key performance indicators (KPIs), facilitating informed decision-making. Primary Value and Solutions: 525K empowers businesses to take ownership and control of their supply chain processes and costs. By integrating 525K's advanced technology, companies can align their supply chain processes with best-in-class tech, bringing visibility and connecting missing links seamlessly. The platform's automated process controls focus on each partner's customized logistics flows, translating insights into actions managed by the platform and compliant with partners' KPIs visible in their command and control centers. This approach leads to significant cost savings and operational efficiencies, as evidenced by improvements of over $65 million in cost and efficiency for major businesses in the MENA region between 2020 and 2022. **Who Is the Company Behind 525K Global Solutions?** - **Seller:** [525K Global Solutions](https://www.g2.com/sellers/525k-global-solutions) - **Year Founded:** 2017 - **HQ Location:** Riyadh, SA - **LinkedIn® Page:** https://www.linkedin.com/company/525k (24 employees on LinkedIn®) ### 21. [Ai-OPs](https://www.g2.com/products/ai-ops/reviews) Ai-OPs is a U.S.-based software company specializing in AI-driven solutions tailored for heavy industry and complex operations. Their flagship platform, Koios, is designed to integrate seamlessly with existing control systems, providing real-time optimization, predictive insights, and anomaly detection to enhance operational efficiency and profitability. Key Features and Functionality: - Seamless AI Integration: Koios installs on most off-the-shelf hardware or virtual appliances and can deploy AI models across any control network. It supports standard industrial protocols, ensuring compatibility with existing systems. - Real-Time AI Engine: The platform offers a real-time AI engine capable of executing models with high availability and redundancy, providing immediate insights and control adjustments. - Flexible AI Options: Ai-OPs provides custom AI modeling services and allows for the integration of existing AI models into the Koios platform, preserving prior investments and facilitating tailored solutions. - Industry Expertise: Koios is applicable across various sectors, including manufacturing, power generation, petrochemical, agriculture, utilities, renewables, wastewater, oil & gas, chemical, food & beverage, pharma, and pulp & paper industries. Primary Value and Solutions: Ai-OPs' Koios platform addresses critical challenges in heavy industry by transforming data into actionable intelligence. It enhances process stability, optimizes energy consumption, and increases equipment uptime, leading to significant cost savings and improved operational performance. By empowering operators with AI-driven tools, Koios enables rapid return on investment, with deployments measured in weeks rather than quarters. In summary, Ai-OPs' Koios platform offers a comprehensive AI solution for heavy industries, delivering real-time optimization and predictive insights to drive operational excellence and profitability. **Who Is the Company Behind Ai-OPs?** - **Seller:** [Ai-OPs](https://www.g2.com/sellers/ai-ops) - **Year Founded:** 2020 - **HQ Location:** Mobile, US - **LinkedIn® Page:** https://www.linkedin.com/company/ai-op (9 employees on LinkedIn®) ### 22. [AIShield - AI Security Product](https://www.g2.com/products/aishield-ai-security-product/reviews) Secure your AI workloads against AI attacks and security threats with industry-first patented technology. Track and automatically respond to any attack incidents in realtime by leveraging Splunk, Microsoft Sentinel SIEM integration. AIShield is an AI-security product designed to protect AI-powered devices in the face of emerging security threats. It provides automated hacker-level vulnerability analysis and end-point protection to harden AI systems against newer vulnerabilities such as model theft/extraction, data poisoning, algorithm evasion & model/data inference attacks. AIShield easily integrates into your existing workflows such as AWS Sagemaker, Azure ML, etc making AI security accessible to developers and helping improve security posture. AIShield easily integrates with Splunk, Microsoft Sentinel to deliver real-time alerts. It protects the IP and brand of organizations against critical breaches and attacks on AI systems (devices, assets, workloads, models). Request a free trial here, https://www.boschaishield.com/trial-request/ Already a AWS customer, https://aws.amazon.com/marketplace/pp/prodview-ppbwtiryaohti **Who Is the Company Behind AIShield - AI Security Product?** - **Seller:** [AIShield](https://www.g2.com/sellers/aishield) - **Year Founded:** 2022 - **HQ Location:** Bengaluru, IN - **LinkedIn® Page:** https://www.linkedin.com/company/bosch-aishield (6 employees on LinkedIn®) ### 23. [Akira AI](https://www.g2.com/products/akira-ai/reviews) Akira Ai is the Taking AI experiments to Production and it provides solutions for Continuous Delivery For Machine learning and deep learning, Standardize AI and ML/DL operations, Quality Monitoring, Model Visualization and Model Catalog. We are Specialist to Build and Run On-Premises as well as EDGE **Who Is the Company Behind Akira AI?** - **Seller:** [Akira GenAI](https://www.g2.com/sellers/akira-genai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 24. [Algorithmia](https://www.g2.com/products/algorithmia-algorithmia/reviews) Algorithmia is a comprehensive AI platform designed to streamline the development, deployment, and management of machine learning models. It offers a unified environment that caters to data scientists, developers, and IT professionals, enabling them to collaborate effectively and accelerate the delivery of AI solutions. Key Features and Functionality: - Unified AI Development Environment: Provides a cohesive platform that integrates tools and resources for all stages of the AI lifecycle, from model creation to deployment. - Role-Specific Tools: Offers feature-rich tools tailored to the needs of data scientists, developers, and IT teams, facilitating efficient collaboration and productivity. - Scalable Deployment: Supports seamless scaling of AI models, ensuring they can handle varying workloads and performance requirements. - Governance and Compliance: Includes robust governance features to manage AI models, ensuring compliance with organizational policies and industry standards. Primary Value and User Solutions: Algorithmia addresses the challenges of managing complex AI tech stacks by providing a unified platform that simplifies the AI development process. It enables organizations to accelerate the deployment of AI solutions, reduce operational complexities, and ensure that AI initiatives are aligned with business objectives. By offering tools tailored to various roles within the AI team, Algorithmia fosters collaboration and enhances productivity, ultimately driving innovation and delivering meaningful impact across departments and industries. **Who Is the Company Behind Algorithmia?** - **Seller:** [Algorithmia](https://www.g2.com/sellers/algorithmia) - **Year Founded:** 2013 - **HQ Location:** N/A - **Twitter:** @DataRobot (19,264 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 25. [Applisafe](https://www.g2.com/products/applisafe/reviews) Applisafe is a comprehensive web application security solution designed to protect websites from a wide range of threats, including SQL injections, cross-site scripting (XSS), spam bots, and unauthorized access via proxies, VPNs, and TOR networks. By implementing Applisafe, website owners can ensure robust defense mechanisms are in place, safeguarding sensitive data and maintaining optimal website performance. Key Features and Functionality: - Advanced Threat Protection: Utilizes intelligent algorithms to detect and prevent SQL injections, XSS attacks, and other vulnerabilities that could compromise website security. - Anti-Spam Measures: Employs sophisticated spam protection modules to filter out unwanted messages and content in real-time, keeping the website free from spam bots and malicious links. - Access Control: Blocks visitors using proxies, VPNs, and TOR networks, ensuring that only legitimate users can access the website. - Automatic Input Sanitization: Sanitizes all incoming and outgoing requests and responses, reducing the risk of data breaches and other security threats. - DNSBL Integration: Integrates with leading spam databases to protect the website from malicious visitors and other threats. - Comprehensive Ban System: Allows blocking and redirecting visitors based on IP address, country, operating system, browser, and referrer, providing granular control over website access. - Real-Time Monitoring and Analytics: Offers live traffic monitoring, detailed threat logs, and visit analytics to provide insights into website security and user behavior. - Resource Efficiency: Optimized for performance, Applisafe operates with minimal system resource usage, ensuring that website speed and functionality are not compromised. Primary Value and User Solutions: Applisafe addresses the critical need for robust website security by offering a comprehensive suite of features that protect against a multitude of cyber threats. By implementing Applisafe, website owners can: - Enhance Security Posture: Safeguard their websites from common and sophisticated attacks, ensuring the integrity and confidentiality of sensitive data. - Maintain User Trust: Provide a secure browsing experience for visitors, fostering trust and credibility. - Ensure Compliance: Meet industry standards and regulations related to data protection and cybersecurity. - Optimize Performance: Prevent malicious activities that can slow down or disrupt website functionality, ensuring optimal performance for users. By integrating Applisafe, organizations can proactively manage and mitigate security risks, allowing them to focus on their core business objectives without the constant concern of potential cyber threats. **Who Is the Company Behind Applisafe?** - **Seller:** [Applisafe](https://www.g2.com/sellers/applisafe) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ## What Is MLOps Platforms? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to MLOps Platforms? - [Machine Learning Software](https://www.g2.com/categories/machine-learning) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [Data Labeling Software](https://www.g2.com/categories/data-labeling) --- ## 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.