Best MLOps Platforms
Machine learning operationalization (MLOps) platforms allow users to manage and monitor machine learning models as they are integrated into business applications. In addition, many of these tools facilitate the deployment of these models. With these tools, businesses can implement machine learning models and algorithms built by data scientists and developers. MLOps software automates deployment, monitors models' health, performance, and accuracy, and iterates on those models. Some tools provide features for doing this collaboratively. This enables businesses to scale machine learning across the company and make a tangible business impact.
Additionally, these products may provide security, provisioning, and governing capabilities to ensure that only those authorized to make version changes or deployment adjustments can do so. The tools can differ regarding what part of the machine learning journey or workflow they focus on, including explainability, hyper optimization, feature engineering, model risk, model selection, model monitoring, and experiment tracking.
These tools are usually language agnostic, so they can be successfully deployed no matter how an algorithm is built. However, some may focus specifically on languages like R or Python, among others. Some of these products are dedicated to tracking machine learning experiments to better understand the performance of models. In addition, some products provide the ability to augment users’ training datasets in order to improve model training.
Some MLOps solutions offer a way to manage all machine learning models across the entire business in a single location. Although similar to data science and machine learning platforms, this software differs since it focuses on the maintenance and monitoring of models instead of deployment.
To qualify for inclusion in the MLOps Platforms category, a product must:
Featured MLOps Platforms At A Glance
G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, a
31,775,247 Twitter followers
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Data
Databricks Data Intelligence Platform is a unified data engineering platform for lakehouse architecture with cloud integration, designed to accommodate business and official data for detailed analytics and future growth planning. Users frequently mention the platform's data governance capabilities, its support for machine learning applications, and its helpful autofilling features, as well as its seamless integration with other tools like Power BI for reporting. Users mentioned challenges such as the complexity of fine-tuning the platform to specific business use cases, the need for a team of professionals to handle large data, and the financial investment involved in using the platform.
88,223 Twitter followers
Microsoft Fabric is a comprehensive, AI-powered data analytics platform that unifies various data management and analysis tools into a single, integrated environment. It combines the capabilities of M
13,088,482 Twitter followers
Roboflow has everything you need to build and deploy computer vision applications. Over 1,000,000 users from businesses of every size — from startups to public companies — use the company's end-to-
12,710 Twitter followers
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI
708,744 Twitter followers
Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applic
223 Twitter followers
SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and e
SAS Viya is a cloud-native platform that provides detailed keyword and sentiment analysis, and allows users to customize categories for analysis. Reviewers appreciate SAS Viya's scalability, seamless integration of data preparation, advanced analytics, and machine learning within a single platform, and its user-friendly UI combined with powerful statistical capabilities. Users mentioned that SAS Viya has a steep learning curve for new users, especially when transitioning from open-source ecosystems like Python, and its cost structure could be improved.
61,064 Twitter followers
Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools an
2,220,162 Twitter followers
Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently
13,088,482 Twitter followers
Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimen
Dataiku is a data science and machine learning platform that centralizes and organizes data, supports collaboration, and manages the full data lifecycle from preparation to deployment. Users like Dataiku's user-friendly interface, strong collaboration features, and its ability to streamline building, training, and deploying AI models at scale, making generative AI projects faster and more reliable. Reviewers noted that Dataiku can be demanding on system resources, especially when working with large datasets, and its extensive features can be overwhelming for new users, leading to a steeper learning curve.
22,954 Twitter followers
Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineer
Ilum is a data platform that functions as a data warehouse and a data science platform with data ops capabilities, operating seamlessly both on-premise and in the cloud. Users like the flexibility of Ilum, its user-friendly interface, quick setup process, excellent customer support, and its ability to integrate well with other tools, making daily operations easier and more organized. Reviewers mentioned that Ilum could benefit from additional modules focused on ETL, more visual options for customizing dashboards, and that it requires some basic knowledge of K8S to start with, which can be challenging for some users.
19 Twitter followers
Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic
66,058 Twitter followers
SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thou
704 Twitter followers
TrueFoundry provides an enterprise-grade AI Gateway that encompasses an LLM Gateway, MCP Gateway, and Agent Gateway, enabling enterprises to securely connect, observe, and govern access to models, too
Edge Impulse is an end-to-end platform for edge AI application development. We enable developers to use their own sensor, audio and vision data to train AI models for classification, regression an
441,322 Twitter followers
Learn More 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: 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: 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: 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: 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: 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: 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.
