Best MLOps Tools & Platforms for 2022 – CIO Insight

A variety of industries around the globe are beginning to invest more heavily in machine learning (ML). Teams of experts can get ML models off the ground and running initially, but one of the greatest challenges is applying those lessons learned to the next model so processes can be scaled. 
MLOps strategies are increasingly being applied to machine learning models and the teams that build them to optimize and standardize the procedures that go into model lifecycle management.
Read on to learn about some of the top MLOps tools and platforms on the market, and what they can do to simplify machine learning from a tool, developer, and procedural perspective.
Also read: AI Software Trends 
Table of Contents
Machine learning operations, typically called MLOps, is a strategy for establishing procedures, standards, and best practices for machine learning models. Instead of pouring extensive time and resources into machine learning development without a plan, MLOps works to ensure the full lifecycle of ML development — from ideation to deployment — is carefully documented and managed for optimized outcomes.
MLOps exists not only to improve the quality and security of ML models, but also to document best practices in a way that makes machine learning development more scalable for ML operators and developers.
Because MLOps effectively applies DevOps strategies to a more niche area of technical development, some call it DevOps for machine learning. This is a helpful way to view MLOps, because much like DevOps, it’s all about knowledge sharing, collaboration, and best practices across teams and tools; MLOps gives developers, data scientists, and operations teams a guide for working together and creating the most effective ML models as a result.
More on DevOps: Best DevOps Tools
MLOps tools can do a variety of tasks for an ML team, but typically, these tools can be divided into two categories: individual component management and platform management. While some MLOps tools specialize in one core area, like data or metadata management, other tools take a more holistic approach and offer an MLOps platform to manage several pieces of the ML lifecycle.
Whether you’re looking at a specialized or more general tool for MLOps, look for tools that help your team to manage these areas of ML development:
Learn more about AI, ML, tools, and use cases: AI vs Machine Learning: What Are Their Differences & Impacts?
Best for Model Monitoring and Drift Management
Amazon SageMaker is a leading MLOps platform for many reasons, but its focus on monitoring and drift management helps teams most. The platform gives teams alerts to models, algorithms, and data sets that need to be adjusted over time. Some core areas of focus for Amazon SageMaker include real-time model and concept drift tracking, as well as prediction accuracy monitoring and bias alerting.
Amazon SageMaker screenshot
Features:
Pricing: Find different pricing scenarios and examples from Amazon here.
Best for Collaboration and Research
Domino Data Lab‘s Domino Data Science Platform is a popular platform for teams that focus on data management, especially because it focuses on creating centralized storage and visualization spaces for MLOps data. Domino’s platform is a strong solution for teams that want to lean into data democratization because they offer so many learning and templating resources — such as their Knowledge Center and their Workbench.
Domino Data Lab screenshot
Features:
Pricing: Pricing offered directly by Domino sales team. A 14-day free trial is available.
Best for Full-Cycle Automation
Valohai offers its customers a variety of pipelines, workflows, and other automated deployment solutions that simplify lifecycle management for multiple ML models at once. Many customers also select Valohai because of how its open API allows the tool to flexibly integrate with outside hardware and tools, such as preexisting CI/CD pipelines.
Valohai screenshot
Features:
Pricing: Pro and Enterprise pricing packages are available. Pricing information is offered directly by the Valohai sales team.
Best for Feature Engineering
Iguazio includes many of the same features that other full-service MLOps platforms advertise, but it particularly shines with its feature engineering solutions. It simplifies collaborative feature engineering with real-time aggregation and streaming data. The tool also offers native feature store integration, low-code/no-code conversions, and graphing and data visualizations to help engineers continually manage the features they’ve created.
Iguazio screenshot
Features:
Pricing: A 14-day free trial is available. Pricing is offered directly by the Iguazio sales team.
Best for Container and Testing Environment Flexibility
H2O MLOps is one of many top-tier solutions provided by H2O for machine learning and artificial intelligence tooling. Many MLOps teams select this tool because of the flexibility of testing and deployment environments that work with the platform. Teams can construct several different environments for development, testing, and production. Further, the platform has the flexibility to work with cloud, on-premises, and container infrastructures.
H2O MLOps Screenshot
Features:
Pricing: 14 days of free access to the H2O AI Cloud. Pricing is available by request from the H2O sales team.
Best for Open-Source Integration Opportunities
MLflow is an open-source lifecycle management platform that allows for more customizations than many of its closed-source competitors. This tool also integrates with several other popular MLOps solutions, such as H2O.ai, Amazon SageMaker, Databricks, Google Cloud, Azure Machine Learning, Docker, and Kubernetes.
MLflow Screenshot
Features:
Pricing: Free and open-source version available. MLflow is also available as an add-on for select other MLOps tools.
Best for Metadata Storage and Management
Neptune.ai focuses on one key area of the MLOps lifecycle: metadata storage and management. Users can easily log, organize, search, catalog, and store all kinds of metadata for their ML models with this tool. Neptune’s strategic focus on in-depth metadata knowledge makes it a strong solution for teams that want to focus on research, experimentation, and more complicated builds requiring deeper data insights.
Neptune.ai screenshot
Features:
Pricing: Find SaaS and private infrastructure pricing options here.
Best for Shared Data Experience (SDX) Features
Cloudera Data Platform is a platform with several subcategories, such as Machine Learning and Shared Data Experience (SDX). The Machine Learning module offers several fundamental MLOps features, but it’s the SDX solution that sets Cloudera apart. SDX provides users with increased visibility and guided management for data security, compliance, and other data governance needs. Especially as several team members work with new and sensitive data, SDX helps companies to stay compliant and secure while building ML models.
Cloudera Machine Learning Screenshot
Features:
Pricing: Pricing differs based on the selected hosting platform and modular features. Find full pricing information here.
When selecting an MLOps tool for your organization, it’s important to see what features the tool offers in these key categories:
More on security and compliance: What Is Governance, Risk and Compliance (GRC)?
Because of the various resources they provide, MLOps tools are useful for a variety of corporate teams and machine learning use cases. Company teams in these scenarios should consider investing in an MLOps tool:
Getting MLOps teams organized: Guide to the 5 Types of Change Management
MLOps tools offer a slew of benefits when the strategies and tools are implemented correctly in an organization:
Also read: Key Machine Learning (ML) Trends 2022
MLOps offers exciting opportunities for advancement and standardizing best practices in machine learning development. However, like any other innovation in the tech and procedural space, there are several risks that new users should recognize and work to prevent.
Machine learning models can only work effectively if they have the right training and logging data stored in the system. Especially as data grows in quantity, data quality can become more challenging to check and improve. You also run the risk of collecting the wrong data or deleting important data for your models. 
“Make sure your team has an effective data management strategy before you dive into the deep end of machine learning development.”
While many MLOps tools offer built-in data quality solutions, they don’t all offer these kinds of safety nets, and the tools themselves can never fully account for all business use cases.
Consider looking into additional data quality, preparation, and cleansing tools to optimize your model’s initial development. Even more importantly, make sure your team has an effective data management strategy before you dive into the deep end of machine learning development.
MLOps tools offer communication, visualization, dashboards, and other democratization tools. These resources make it easier for teams with different professional backgrounds to collaborate on ML development. But collaborative tools only effectively break down silos when all teams equally understand and commit to the collaborative expectations of an MLOps strategy.
Before investing in an MLOps tool, make sure that all important stakeholders, across development and operations teams, understand the following features:
Another view on silo-busting: Why Data Democratization is Better Than Busting Down Silos
Machine learning is such an exciting new field for business process automation and other rising competitive advantages. However, some organizations jump directly into ML model building without a plan. This frequently results in significant time, money, and resources spent on a model that doesn’t actually create value for the business.
Businesses that are interested in creating ML models should not only focus on optimizing the models themselves through MLOps, but should also consider greater business initiatives and executive plans for the future.
It’s a good idea for your MLOps team(s) to sit down with executive leadership to create an ML roadmap. This meeting will make machine learning development and MLOps team goals better align with the resources and expectations from the executive level.
Read next: Top Cloud Computing Companies for 2022
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