# Best MLOps Platforms - Page 10 *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:** 249 ### 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 - 249+ 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:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [SuperAnnotate](https://www.g2.com/products/superannotate/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) ## What Are the Top-Rated MLOps Platforms Products in 2026? ### 1. [Superunit](https://www.g2.com/products/superunit/reviews) Superunit is an advanced AI-powered platform designed to streamline and enhance the development of artificial intelligence applications. It offers a comprehensive suite of tools and services that cater to both novice and experienced developers, enabling them to build, deploy, and manage AI models efficiently. By providing an intuitive interface and robust infrastructure, Superunit simplifies the complexities associated with AI development, allowing users to focus on innovation and problem-solving. Key Features and Functionality: - User-Friendly Interface: Superunit's platform is designed with simplicity in mind, offering an intuitive interface that makes AI development accessible to users of all skill levels. - Comprehensive Toolset: The platform provides a wide range of tools for building, training, and deploying AI models, covering various aspects of the development lifecycle. - Scalable Infrastructure: Superunit offers scalable computing resources, ensuring that applications can handle varying workloads and grow as needed. - Pre-Built Models and Templates: Users have access to a library of pre-built models and templates, accelerating the development process and reducing time-to-market. - Collaboration Features: The platform supports team collaboration, allowing multiple users to work on projects simultaneously and share insights effectively. - Integration Capabilities: Superunit seamlessly integrates with existing workflows and other tools, facilitating a smooth development experience. Primary Value and Problem Solved: Superunit addresses the challenges of AI development by providing a streamlined, all-in-one platform that simplifies the process of building and deploying AI applications. It eliminates the need for extensive technical expertise and reduces the time and resources required to develop AI solutions. By offering scalable infrastructure and a comprehensive toolset, Superunit empowers organizations to innovate rapidly and efficiently, making AI development more accessible and manageable for businesses of all sizes. **Who Is the Company Behind Superunit?** - **Seller:** [Superunit](https://www.g2.com/sellers/superunit) - **HQ Location:** Los Angeles, US - **LinkedIn® Page:** https://www.linkedin.com/company/superunit-ai (2 employees on LinkedIn®) ### 2. [Synapses](https://www.g2.com/products/synapses/reviews) Synapses.studio is an advanced AI-powered platform designed to provide users with access to a wide array of artificial intelligence technologies. It features tools like ChatGPT and GPT-4 for text generation, as well as DALL·E 3 for image creation. Tailored to meet the diverse needs of professionals, students, and enthusiasts, Synapses.studio enables users to integrate AI seamlessly into their work, studies, and personal projects. Key Features and Functionality: - Extensive AI Library: Offers over 1,000 AI tools across more than 200 categories, ensuring a broad selection for various applications. - User-Friendly Interface: Designed for intuitive navigation, making it easy for users to discover and utilize AI tools. - Free AI Tool Submission: Allows users to submit their own AI tools at no cost, fostering community engagement and expanding the platform's offerings. Primary Value and User Solutions: Synapses.studio democratizes access to cutting-edge AI technologies, enabling users to enhance productivity, creativity, and innovation. By providing a comprehensive suite of AI tools in a single platform, it simplifies the process of integrating AI into various projects, thereby solving the challenge of accessing and utilizing diverse AI resources efficiently. **Who Is the Company Behind Synapses?** - **Seller:** [Synapses.studio](https://www.g2.com/sellers/synapses-studio) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 3. [Teract](https://www.g2.com/products/teract/reviews) Teract is an advanced AI platform designed to streamline and enhance the development and deployment of machine learning models. It offers a comprehensive suite of tools that facilitate the entire machine learning lifecycle, from data preprocessing to model training and deployment. Teract's user-friendly interface and robust infrastructure enable data scientists and engineers to build, test, and scale AI solutions efficiently. Key Features and Functionality: - Integrated Development Environment: Provides a cohesive workspace for coding, testing, and debugging machine learning models. - Automated Data Processing: Simplifies data cleaning and transformation, reducing the time spent on data preparation. - Model Training and Evaluation: Supports various algorithms and frameworks, allowing for flexible model development and performance assessment. - Scalable Deployment: Facilitates seamless deployment of models into production environments, ensuring scalability and reliability. - Collaboration Tools: Enables team collaboration through shared projects, version control, and real-time feedback mechanisms. Primary Value and User Solutions: Teract addresses the common challenges faced by data science teams, such as fragmented workflows, time-consuming data preparation, and complex deployment processes. By offering an all-in-one platform, it enhances productivity, accelerates time-to-market for AI solutions, and ensures consistency across projects. Users benefit from reduced operational overhead, improved collaboration, and the ability to focus more on innovation rather than infrastructure management. **Who Is the Company Behind Teract?** - **Seller:** [Teract LinkedIn AI Extension](https://www.g2.com/sellers/teract-linkedin-ai-extension) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 4. [The Forecasting Company](https://www.g2.com/products/the-forecasting-company/reviews) The Forecasting Company offers advanced forecasting solutions powered by its proprietary model, `t_0`, designed to deliver precise predictions across any time series data. By integrating various contextual variables, `t_0` provides instant, accurate forecasts without the need for extensive training, enabling businesses to make informed decisions swiftly. Key Features and Functionality: - Retrocast Platform: A browser-based interface allowing users to upload any time series data and generate forecasts instantly. - API Integration: Seamless incorporation of `t_0` into existing workflows through a robust API, facilitating model inference and back-testing. - Versatile Applications: Applicable across various industries, including logistics, retail, manufacturing, energy, and pharmaceuticals, to predict demand, optimize supply chains, and enhance operational efficiency. Primary Value and Solutions Provided: The Forecasting Company addresses the challenges of unreliable predictions and the resource-intensive nature of traditional forecasting methods. By offering a plug-and-play system that requires no specialized training, it empowers businesses to achieve high-accuracy forecasts rapidly. This capability enhances inventory management, optimizes logistics, and supports proactive maintenance scheduling, ultimately leading to cost savings and improved customer satisfaction. **Who Is the Company Behind The Forecasting Company?** - **Seller:** [The Forecasting Company](https://www.g2.com/sellers/the-forecasting-company) - **Year Founded:** 2024 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/the-forecasting-company (3,547 employees on LinkedIn®) ### 5. [TimeComplexity.ai](https://www.g2.com/products/timecomplexity-ai/reviews) TimeComplexity.ai analyzes runtime complexity, providing crucial insights to optimize your code performance. **Who Is the Company Behind TimeComplexity.ai?** - **Seller:** [TimeComplexity.ai](https://www.g2.com/sellers/timecomplexity-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 6. [TrainLoop](https://www.g2.com/products/trainloop/reviews) TrainLoop is an advanced platform designed to streamline and optimize the machine learning model training process. It offers a comprehensive suite of tools that facilitate efficient model development, training, and deployment, catering to both novice and experienced data scientists. Key Features and Functionality: - Automated Workflow Management: Simplifies the setup and execution of complex training pipelines, reducing manual intervention and potential errors. - Scalable Infrastructure: Supports distributed training across multiple GPUs and cloud environments, enabling faster model convergence and scalability. - Hyperparameter Optimization: Provides built-in tools for automated hyperparameter tuning, enhancing model performance without extensive manual experimentation. - Experiment Tracking: Offers robust tracking and visualization of experiments, allowing users to monitor progress and compare results effectively. - Integration with Popular Frameworks: Seamlessly integrates with leading machine learning frameworks such as TensorFlow, PyTorch, and Keras, ensuring flexibility and ease of use. Primary Value and User Solutions: TrainLoop addresses the challenges of managing and optimizing machine learning training processes by providing an intuitive and efficient platform. It reduces the time and effort required for model development, enhances reproducibility through comprehensive experiment tracking, and improves model performance via automated optimization tools. By offering scalable infrastructure and seamless integration with popular frameworks, TrainLoop empowers data scientists and organizations to accelerate their machine learning initiatives and achieve superior results. **Who Is the Company Behind TrainLoop?** - **Seller:** [TrainLoop](https://www.g2.com/sellers/trainloop) - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/trainloopai (672 employees on LinkedIn®) ### 7. [Tridiagonal.ai](https://www.g2.com/products/tridiagonal-ai/reviews) Tridiagonal.ai's AI Life Cycle Management (AILCM) solution is designed to streamline the development, deployment, and maintenance of industrial AI models, ensuring they deliver consistent and reliable performance throughout their operational life. By integrating domain expertise with advanced AI methodologies, AILCM addresses the unique challenges faced by process industries in managing AI applications at scale. Key Features and Functionality: - Model Development and Deployment: Facilitates the creation and implementation of AI models tailored to specific industrial processes, ensuring seamless integration with existing systems. - Continuous Monitoring and Maintenance: Provides tools for real-time monitoring of AI model performance, enabling proactive maintenance and timely updates to maintain accuracy and efficiency. - Scalability: Supports the scaling of AI solutions across various operations, allowing organizations to expand their AI initiatives without compromising performance. - Compliance and Governance: Ensures that AI models adhere to industry standards and regulatory requirements, promoting transparency and accountability in AI-driven processes. Primary Value and Problem Solved: AILCM empowers process industries to effectively manage the entire lifecycle of their AI models, from development to decommissioning. By providing a structured framework for AI management, it mitigates risks associated with model degradation, enhances operational efficiency, and ensures that AI applications continue to deliver value over time. This solution addresses common challenges such as model drift, scalability issues, and compliance concerns, enabling organizations to harness the full potential of AI in their operations. **Who Is the Company Behind Tridiagonal.ai?** - **Seller:** [Tridiagonal.ai](https://www.g2.com/sellers/tridiagonal-ai) - **Year Founded:** 2021 - **HQ Location:** Singapore, SG - **LinkedIn® Page:** https://www.linkedin.com/company/tridiagonal-ai-1/ (24 employees on LinkedIn®) ### 8. [Truera](https://www.g2.com/products/truera/reviews) TruEra Research: Explainable ML A core research direction for TruEra is studying how to robustly explain models in order to understand, introspect, and trust them. TruEra solutions are based on years of explainability research conducted at Carnegie Mellon University. We continue to view explainability as the backbone for trust in ML systems. **Who Is the Company Behind Truera?** - **Seller:** [TruEra](https://www.g2.com/sellers/truera-60cc09ed-ea2d-4e7e-8eef-8531aceb6760) - **Year Founded:** 2019 - **HQ Location:** Redwood City, US - **LinkedIn® Page:** https://www.linkedin.com/company/truera (9 employees on LinkedIn®) ### 9. [TruEra Diagnostics](https://www.g2.com/products/truera-diagnostics/reviews) TruEra Diagnostics is an AI Quality solution that helps data scientists to analyze and optimize machine learning model performance, explain model function, and minimize algorithmic bias. With TruEra Diagnostics, data scientists can create high quality models, faster, as well as demonstrate to key stakeholders that their models are ready for production and meet customer or regulatory requirements. TruEra Diagnostics works across both custom models and models created with the most popular model development platforms, such as Data Robot, H20.ai, and Dataiku. It also works across a variety of model serving providers, and fits easily into the AI stack. **Who Is the Company Behind TruEra Diagnostics?** - **Seller:** [TruEra](https://www.g2.com/sellers/truera-60cc09ed-ea2d-4e7e-8eef-8531aceb6760) - **Year Founded:** 2019 - **HQ Location:** Redwood City, US - **LinkedIn® Page:** https://www.linkedin.com/company/truera (9 employees on LinkedIn®) ### 10. [TruEra Monitoring](https://www.g2.com/products/truera-monitoring/reviews) TruEra Monitoring helps you easily track and troubleshoot machine learning model performance. With unique explainability and model quality analytics, TruEra Monitoring goes beyond basic observability solutions by enabling faster root cause analysis and action. This saves ML ops and data scientist time, improves governance, and provides a more effective feedback loop to improve both models and business outcomes. **Who Is the Company Behind TruEra Monitoring?** - **Seller:** [TruEra](https://www.g2.com/sellers/truera-60cc09ed-ea2d-4e7e-8eef-8531aceb6760) - **Year Founded:** 2019 - **HQ Location:** Redwood City, US - **LinkedIn® Page:** https://www.linkedin.com/company/truera (9 employees on LinkedIn®) ### 11. [TuplOS](https://www.g2.com/products/tuplos/reviews) TuplOS is an MLOps platform that was first implemented in the Telco industry, though it has already been successfully used in other industries such as Smart Agriculture or Smart Manufacturing.TuplOS® platform provides a simple, flexible, and scalable framework to create end-to-end automation applications relying on a series of UI-based utilities that facilitate transferring ideas into real products with no programming required, as it is designed for domain experts. TuplOS has three main components, one that deals with the control of resources (Infrastructure Layer), a second one in charge of Data processing and storage (Data Layer), and a Visualization layer that facilitates the creation of UI-based applications (UI Framework). **Who Is the Company Behind TuplOS?** - **Seller:** [Tupl](https://www.g2.com/sellers/tupl) - **Year Founded:** 2014 - **HQ Location:** Bellevue, US - **LinkedIn® Page:** http://www.linkedin.com/company/tupl-inc- (114 employees on LinkedIn®) ### 12. [Turbo0](https://www.g2.com/products/turbo0/reviews) Turbo0 is a comprehensive platform designed to streamline and enhance the efficiency of software development and deployment processes. It offers a suite of tools that facilitate continuous integration and continuous deployment (CI/CD), enabling development teams to automate workflows, reduce manual errors, and accelerate product delivery. Key Features and Functionality: - Automated CI/CD Pipelines: Turbo0 provides robust support for setting up and managing automated pipelines, ensuring seamless integration and deployment of code changes. - Scalability: The platform is built to handle projects of varying sizes, accommodating the needs of both small teams and large enterprises. - Security: Turbo0 incorporates advanced security measures to protect codebases and deployment environments from potential threats. - User-Friendly Interface: With an intuitive design, Turbo0 allows users to easily configure and monitor their development pipelines. Primary Value and Problem Solved: Turbo0 addresses the common challenges faced by development teams in managing complex workflows and ensuring rapid, reliable software releases. By automating the CI/CD process, it minimizes manual intervention, reduces the likelihood of errors, and significantly shortens the development cycle. This leads to faster time-to-market, improved product quality, and enhanced collaboration among team members. **Who Is the Company Behind Turbo0?** - **Seller:** [Turbo0](https://www.g2.com/sellers/turbo0) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 13. [Unweave](https://www.g2.com/products/unweave/reviews) Unweave is an open-source platform designed to streamline the development of machine learning (ML) environments by providing developers with instant access to serverless infrastructure. It enables users to SSH into GPU machines across various cloud providers, facilitating efficient training and deployment of ML models without the complexities of manual cloud configuration. Key Features and Functionality: - Rapid Setup: Install the Unweave Command Line Interface (CLI) with a single command and link your local directory to an Unweave project, minimizing setup time. - Seamless Cloud Integration: Launch and connect to GPU instances on preferred cloud providers using straightforward commands, eliminating the need for intricate cloud infrastructure management. - Transparent Pricing: Access clear and competitive hourly rates for various GPU types across multiple cloud providers, ensuring cost-effective resource utilization. - Flexible Development Tools: Utilize your preferred Integrated Development Environment (IDE) and manage projects through both the Unweave CLI and a user-friendly web dashboard. - Cross-Provider Compatibility: Search for the best GPU availability and pricing across different cloud providers, enhancing flexibility and resource optimization. Primary Value and User Solutions: Unweave addresses the challenges developers face in setting up and managing ML environments by offering a simplified, efficient, and cost-effective solution. By automating the configuration of cloud infrastructure and providing seamless integration with existing development tools, Unweave allows developers to focus on building and training models without the overhead of manual setup. This accelerates the development process, reduces operational complexities, and optimizes resource usage, ultimately enhancing productivity and innovation in machine learning projects. **Who Is the Company Behind Unweave?** - **Seller:** [Unweave](https://www.g2.com/sellers/unweave) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 14. [Usemosaic](https://www.g2.com/products/usemosaic/reviews) Mosaic is a comprehensive resource management and project planning software designed to enhance team productivity and project efficiency. It offers a suite of tools that enable organizations to effectively allocate resources, manage workloads, and gain real-time insights into project progress. Key Features and Functionality: - Resource Management: Provides a clear overview of team availability and workload, facilitating optimal resource allocation. - Project Planning: Enables the creation and adjustment of project timelines with intuitive drag-and-drop functionality. - Real-Time Analytics: Delivers up-to-date reports and dashboards for monitoring project performance and team utilization. - Collaboration Tools: Supports seamless communication and coordination among team members through integrated messaging and file-sharing capabilities. - Integration Capabilities: Offers compatibility with various third-party applications to streamline workflows and data synchronization. Primary Value and User Solutions: Mosaic addresses the challenges of resource mismanagement and project inefficiencies by providing a centralized platform for planning and monitoring. It empowers teams to make informed decisions, balance workloads, and meet project deadlines effectively. By offering real-time visibility into resource allocation and project status, Mosaic helps organizations optimize their operations, reduce bottlenecks, and enhance overall productivity. **Who Is the Company Behind Usemosaic?** - **Seller:** [Mosaic](https://www.g2.com/sellers/mosaic-94a904e3-e0df-4195-b394-58e6dbf22608) - **Year Founded:** 2024 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/mosaic-so/ (6 employees on LinkedIn®) ### 15. [Velvet](https://www.g2.com/products/velvet/reviews) Velvet was an AI gateway designed to help engineers analyze, evaluate, and monitor AI-powered features in production environments. By acting as a proxy, Velvet warehoused every request from AI models like OpenAI and Anthropic to a PostgreSQL database, enabling comprehensive analysis and optimization of AI applications. Key Features and Functionality: - AI-First SQL Editor: Allowed users to write complex SQL queries using natural language, facilitating easy data analysis. - Collaborative Data Tools: Enabled teams to turn queries into tables, graphs, and alerts, promoting shared insights and collaborative decision-making. - Real-Time Data Utilization: Provided interoperable API endpoints to build analytics and product features using live data. - Model Evaluations: Offered frameworks to run experiments on request logs, testing models, settings, and metrics to ensure AI features function as expected. - Data Retention and Archival: Implemented policies to maintain performance, reduce storage costs, and provide easy access to historical data for analysis. Primary Value and User Solutions: Velvet addressed the challenges of managing and optimizing AI features in production by providing tools for comprehensive data analysis, real-time monitoring, and collaborative development. It enabled product teams to understand usage patterns, troubleshoot issues, calculate costs, and evaluate models effectively. By warehousing AI requests and offering intuitive querying capabilities, Velvet empowered users to build more reliable and efficient AI applications. In 2025, Velvet was acquired by Arize, an enterprise platform specializing in AI evaluation and observability, to further enhance developer-first AI infrastructure. **Who Is the Company Behind Velvet?** - **Seller:** [Arize AI](https://www.g2.com/sellers/arize-ai) - **HQ Location:** Berkeley, US - **Twitter:** @arizeai (4,475 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/arizeai/about (160 employees on LinkedIn®) ### 16. [veroHQ.ai](https://www.g2.com/products/verohq-ai/reviews) VeroHQ is a comprehensive release management platform designed to help engineering teams plan, track, and ship software releases with confidence. By providing real-time insights and automating workflows, VeroHQ ensures that teams meet their deadlines efficiently. Key Features and Functionality: - Real-time Analytics: Monitor progress, velocity, and identify blockers through intuitive dashboards and insights. - Git Integration: Seamlessly connect with GitHub, GitLab, and Bitbucket to automate release tracking. - Team Collaboration: Enhance alignment with shared timelines, assignments, and updates. - AI-Powered Insights: Leverage intelligent recommendations and automated task breakdowns to optimize workflows. - Time Tracking: Compare estimated versus actual time to refine planning accuracy. - Enterprise Security: Benefit from bank-grade encryption, single sign-on (SSO), and compliance measures for data protection. Primary Value and User Solutions: VeroHQ addresses common challenges in software release management by reducing delays through improved planning, increasing team visibility and alignment, automating repetitive tasks, and enabling data-driven decision-making. This leads to faster release cycles and higher on-time delivery rates, empowering teams to scale effectively as they grow. **Who Is the Company Behind veroHQ.ai?** - **Seller:** [veroHQ.ai](https://www.g2.com/sellers/verohq-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 17. [Vespr](https://www.g2.com/products/vespr/reviews) Our software platform gives every team the ability to validate, monitor, and secure their AI. CalypsoAI’s Vespr is an integrated model accreditation and model risk management system (MRM) used to develop, test, and deploy models with validation and security built in. **Who Is the Company Behind Vespr?** - **Seller:** [Nasdaq](https://www.g2.com/sellers/nasdaq) - **Year Founded:** 1971 - **HQ Location:** New York - **Twitter:** @Nasdaq (182,046 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/nasdaq/ (8,338 employees on LinkedIn®) - **Ownership:** NASDAQ: NDAQ ### 18. [Vivity AI](https://www.g2.com/products/vivity-ai/reviews) Vivity AI is an innovative enterprise software platform dedicated to transforming the manufacturing, energy, and logistics sectors through advanced artificial intelligence technologies. Established in 2022 and headquartered in Silicon Valley, with additional offices in Seoul, South Korea, Vivity AI focuses on enhancing operational efficiency, sustainability, and intelligence within heavy industries. The platform addresses critical challenges such as process optimization, workplace safety, product quality, and risk management by seamlessly integrating with existing infrastructures or through retrofitting with Vivity-provided third-party hardware. Key Features and Functionality: - Vivity Safety AI: Utilizes cutting-edge AI to detect and prevent major industrial accidents in real-time, ensuring compliance with safety regulations and supporting the prevention of serious incidents. - Vivity Edge: An edge computing platform designed for real-time data observation and machine learning inference, enabling on-the-spot decision-making and rapid deployment without the need for extensive data collection pipelines. - Vivity Analytics: An AI-driven data analytics platform that provides comprehensive insights into manufacturing processes, facilitating better decision-making through multi-dimensional analysis and predictive analytics. Primary Value and Solutions: Vivity AI empowers heavy industry sectors by bridging the gap between digital and physical operations, uncovering deep insights, and reducing inefficiencies and mismanagement. By offering customizable deployment options—from cloud and micro-servers to on-premises solutions—the platform caters to various infrastructure needs. Its domain-specific AI modules are tailored for tasks such as workplace safety and critical equipment monitoring, enhancing operational efficiency and value proposition. Vivity AI's solutions are particularly beneficial for industries like petrochemicals, energy, shipbuilding, marine engineering, and heavy product manufacturing, aiming to improve operational efficiency, reduce equipment failures, and enhance workplace safety. **Who Is the Company Behind Vivity AI?** - **Seller:** [Vivity AI](https://www.g2.com/sellers/vivity-ai) - **Year Founded:** 2022 - **HQ Location:** 서울특별시, KR - **LinkedIn® Page:** https://www.linkedin.com/company/vivity-ai/mycompany (34 employees on LinkedIn®) ### 19. [Watch-Tower](https://www.g2.com/products/watch-tower/reviews) WatchTower is a comprehensive security platform designed to enhance the safety and efficiency of organizations by providing real-time monitoring, threat detection, and incident response capabilities. It integrates seamlessly with existing systems to offer a centralized view of security operations, enabling proactive management of potential risks. Key Features and Functionality: - Real-Time Monitoring: Continuously observes network and system activities to detect anomalies promptly. - Threat Detection: Utilizes advanced algorithms to identify and assess potential security threats. - Incident Response: Provides tools and protocols for swift action in the event of security incidents. - Integration Capabilities: Easily connects with existing infrastructure to enhance overall security posture. - User-Friendly Interface: Offers an intuitive dashboard for efficient management and reporting. Primary Value and Problem Solved: WatchTower addresses the critical need for organizations to maintain robust security measures in an increasingly complex digital landscape. By offering real-time insights and rapid response tools, it empowers businesses to proactively manage threats, minimize potential damages, and ensure the safety of their assets and data. **Who Is the Company Behind Watch-Tower?** - **Seller:** [WatchTower](https://www.g2.com/sellers/watchtower) - **Year Founded:** 2024 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/watchtower-db/ (4 employees on LinkedIn®) ### 20. [Weco](https://www.g2.com/products/weco/reviews) Weco is an AI-driven platform designed to automate and optimize machine learning (ML) experiments, enabling engineers to enhance their workflows efficiently. By leveraging large language models (LLMs), Weco systematically refines code against user-defined metrics, such as speed, accuracy, latency, or cost, without the need for constant supervision. This continuous, automatic process allows for the exploration of numerous targeted experiments, integrating each outcome into a live tree search to iteratively improve performance. Key Features and Functionality: - Automated Code Optimization: Weco's core engine, AIDE, employs a tree search approach guided by LLMs to iteratively explore and refine code, applying changes, running evaluation scripts, and proposing further improvements based on specified goals. - Versatile Application: The platform supports a wide range of tasks, including GPU kernel optimization, model development, and prompt engineering, accommodating various programming languages and frameworks. - Real-Time Dashboard: Users can monitor optimization progress through an interactive dashboard, providing visual tracking, solution tree exploration, and run management capabilities. - Credit-Based Pricing: Weco offers a simple credit-based pricing model, with a free tier providing 20 credits, approximately equivalent to 100 optimization steps on GPT-5, allowing users to get started without a credit card. Primary Value and User Solutions: Weco addresses common challenges in ML experimentation, such as time-consuming manual iterations, performance bottlenecks, and the need for expert-level code optimization. By automating the experimentation process, Weco enables engineers to focus on strategic decision-making and innovation, leading to faster breakthroughs and more efficient ML pipelines. Its ability to run code locally, test numerous variations, and identify real metric gains without guesswork empowers users to achieve superior results with reduced effort. **Who Is the Company Behind Weco?** - **Seller:** [AIDE by Weco](https://www.g2.com/sellers/aide-by-weco) - **Year Founded:** 2023 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/weco-ai (8 employees on LinkedIn®) ### 21. [WoolyAI](https://www.g2.com/products/woolyai/reviews) WoolyAI is a hardware-agnostic hypervisor designed to optimize machine learning (ML) infrastructure by enabling seamless execution of unmodified PyTorch and CUDA applications across heterogeneous GPU environments, including both NVIDIA and AMD hardware. By abstracting GPU dependencies, WoolyAI enhances resource utilization, simplifies development workflows, and accelerates the deployment of ML applications without necessitating code modifications. Key Features and Functionality: - Cross-Vendor CUDA Execution: Utilizes Just-In-Time (JIT) compilation to run unmodified PyTorch and CUDA applications on mixed GPU clusters, supporting both NVIDIA and AMD GPUs. - CPU-Side Development with GPU Execution: Allows developers to build and run PyTorch code on CPU-only workstations, while CUDA kernels execute on a centralized pool of GPUs, maintaining existing development environments and tools. - Unified CUDA Container: Provides a single CUDA container that operates seamlessly across NVIDIA and AMD GPUs, simplifying CI/CD pipelines and reducing the need for multiple base images. - Dynamic GPU Resource Management: Employs real-time allocation of GPU cores and memory, enabling concurrent execution of multiple ML workloads on a single GPU without static partitioning or time-slicing. - VRAM Deduplication and Multi-Adapter Concurrency: Shares base model weights in VRAM while isolating adapters, maximizing memory efficiency and throughput for evaluation and development tasks. Primary Value and Problem Solved: WoolyAI addresses the challenges of managing diverse GPU infrastructures by providing a unified platform that enhances GPU utilization, reduces operational complexity, and accelerates ML application deployment. It eliminates the need for code rewrites when transitioning between different GPU vendors, supports concurrent execution of multiple workloads on shared GPUs, and offers dynamic resource allocation to meet varying demands. This results in increased productivity for ML operations teams, cost-effective scaling of GPU resources, and improved performance consistency across ML workloads. **Who Is the Company Behind WoolyAI?** - **Seller:** [WoolyAI](https://www.g2.com/sellers/woolyai) - **Year Founded:** 2024 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/woolyaiinc/ (3 employees on LinkedIn®) ### 22. [Yukai](https://www.g2.com/products/yukai/reviews) Yukai Technologies specializes in providing expert consulting services for Amazon Web Services (AWS), assisting businesses in seamlessly transitioning to the cloud. By focusing on AWS infrastructure, Yukai enables clients to concentrate on delivering unique customer experiences while ensuring their cloud environments are scalable, secure, and cost-effective. Key Features and Functionality: - Cloud Architecture and Design: Crafts tailored AWS architectures that are scalable, secure, and high-performing, optimizing resource utilization for cost efficiency. - Big Data and Analytics: Develops scalable data pipelines and implements data lakes to extract valuable insights, enhancing data storage, processing, and analytics for actionable business intelligence. - Security and Compliance: Implements robust security measures, including identity and access management, encryption, and threat detection, ensuring data protection and compliance with industry standards. - AWS Migration Services: Facilitates smooth transitions of applications, data, and workloads to AWS, minimizing downtime and maximizing efficiency with proven migration strategies. - Serverless Architecture: Embraces serverless computing to enhance scalability and reduce operational overhead, allowing clients to focus on code and functionality without managing servers, and optimizing costs with a pay-as-you-go model. Primary Value and Solutions: Yukai Technologies empowers businesses to harness the full potential of AWS by offering customized consulting services that address unique operational needs. By managing the complexities of cloud infrastructure, Yukai allows clients to focus on their core competencies, ensuring a smooth and efficient transition to the cloud. This approach not only enhances operational efficiency but also drives innovation and growth in the digital landscape. **Who Is the Company Behind Yukai?** - **Seller:** [Yukai](https://www.g2.com/sellers/yukai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 23. [ZenML](https://www.g2.com/products/zenml/reviews) ZenML is an open-source MLOps framework that helps data teams deploy their machine learning pipelines. It abstracts the infrastructure complexity of MLOps without locking ML Engineers into any vendor. Stop wrestling with tool integrations, infrastructure mayhem, and manual workflows. Start developing machine learning pipelines that actually work. **Who Is the Company Behind ZenML?** - **Seller:** [ZenML](https://www.g2.com/sellers/zenml) - **Year Founded:** 2021 - **HQ Location:** Munich, DE - **LinkedIn® Page:** https://www.linkedin.com/company/zenml (17 employees on LinkedIn®) ### 24. [Zetane](https://www.g2.com/products/zetane/reviews) ZetaForge is an open-source AI platform designed to streamline the development and deployment of complex AI solutions. It enables users to rapidly assemble reusable, customizable, and containerized Blocks into highly visual AI Pipelines, facilitating efficient collaboration and integration of cutting-edge AI technologies. Key Features and Functionality: - Reusable Blocks and Pipelines: Utilize and customize community-contributed Blocks and Pipelines to accelerate AI development. - Containerized Execution: Each code repository runs in its own container, preventing library conflicts and simplifying integration. - Visual AI Pipelines: Assemble and visualize AI systems using an intuitive user interface, enhancing understanding and collaboration. - Kubernetes Compatibility: Run pipelines locally on Kubernetes or in the cloud, ensuring scalability and resilience. - AI-Assisted Coding: Leverage AI agents to assist in writing or modifying code, improving development efficiency. Primary Value and Problem Solved: ZetaForge addresses the challenges of integrating diverse AI repositories and managing dependency conflicts by providing a containerized, visual platform for AI development. It empowers developers to rapidly prototype, share, and deploy AI solutions, fostering collaboration and innovation while minimizing the time spent on setup and integration tasks. **Who Is the Company Behind Zetane?** - **Seller:** [Zetane](https://www.g2.com/sellers/zetane) - **Year Founded:** 2016 - **HQ Location:** Kirkland, CA - **LinkedIn® Page:** https://www.linkedin.com/company/zetane/ (14 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.