Machine Learning Operations (MLOps) brings DevOps principles to machine learning and simplifies workflows from model development to deployment and maintenance.
Explore the landscape of MLOps tools for various components of the ML lifecycle, including:
What types of MLOps solution providers are there?
Open source MLOps
Half of IT organizations use open source tools for AI and ML, and this number is expected to rise to about two-thirds by 2023. On GitHub alone, 65 million developers and 3 million organizations contribute to 200 million projects.
It is therefore no surprise that advanced open source toolkits exist in the AI and ML space. Open source tools focus on specific tasks within MLOps rather than providing end-to-end machine learning lifecycle management. These tools and platforms typically require Python and R development environments.
Startups offering MLOps
Similar to open source tools, most startups in the MLOps space offer tools for specific tasks within MLOps. Unlike open source, startups tend to offer tools aimed at non-technical users.
Tech giant offering MLOps
There are open source tools developed by tech giants that address specific use cases in MLOps practices. However, the landscape of end-to-end MLOps solutions (or MLOps platforms) is dominated by tech giants like Google, Microsoft, and Alibaba.
Comparative Analysis: Key Features of MLOps Platforms
MLOps tools typically fall into three categories:
- data management
- modeling
- Operationalization
Some tools are considered “MLOps platforms” that provide end-to-end machine learning lifecycle management.
We’ll step through the tools and MLOps platforms for individual tasks within the key areas.
Leading data management solutions
Top data labeling tools
Data labeling tools (also known as data annotation, tagging, or classification tools) are used to label large amounts of data such as text, images, and audio. Then use the labeled data to train a supervised machine learning algorithm to make predictions about the new unlabeled data. Examples of data labeling tools include:
For more information, see our article on how to choose a data label vendor. Also, don’t forget to check out our list of data annotation services.
Version control of upper-level data
Data versioning (also known as data versioning) tools allow you to manage different versions of datasets and store them in an easily accessible and organized manner. This provides data science teams with insights such as identifying how data changes impact model performance and understanding how datasets are evolving.
Common data versioning tools include:
modeling solution
Key feature engineering tools
Feature engineering tools automate the process of extracting useful features from raw datasets to create better training data for machine learning models. These tools can accelerate the process of feature engineering for common applications and common problems. However, you may want to use domain knowledge to improve the results of machine-generated feature engineering. Feature engineering tools include:
Popular experiment tracking tools
Developing a machine learning project involves running multiple experiments with different models, model parameters, or training data. Experiment tracking tools store all the necessary information about different experiments while training a model. This allows you to track versions and results of experiment components and allows comparisons between different experiments. Examples of experiment tracking tools include:
Top hyperparameter optimization tools
Hyperparameters are parameters in a machine learning model that model developers can adjust to achieve different results, such as the size of the neural network or the type of regularization. Hyperparameter tuning or optimization tools automate the process of finding and selecting hyperparameters that provide the best performance for your machine learning model. Common hyperparameter tuning tools include:
Top model version control tool
Model versioning tools help data scientists manage different versions of ML models. Information such as model configuration, provenance data, hyperparameters, validation loss scores, and other metadata is stored in an easily accessible model registry. This metadata store helps data scientists quickly identify the configuration used to build a particular model and avoid accidentally using the wrong or outdated model.
Model versioning systems also have a mechanism to capture the output of a model during training, providing a snapshot of how well a particular model performed at each iteration. Version control helps promote reproducibility, ensuring that published results can be verified in future iterations and investigations.
Some tools that allow model versioning are:
Operationalization solution
Top model deployment/delivery tools
Machine learning model deployment tools make it easy to integrate ML models into production environments and make predictions. Tools in this category include:
Top model monitoring
Monitoring machine learning models is critical to the success of ML projects because changes in input data can cause model performance to degrade over time. Monitoring tools detect data and model drift, or other anomalies, in real time and trigger alerts based on performance metrics. This allows data scientists and ML engineers to take actions such as retraining the model to maintain its effectiveness.
Model monitoring tools include:
Shortlisted MLOps Platforms
As mentioned earlier, there are also tools that cover the machine learning lifecycle end-to-end. These platforms are often provided by startups or tech giants, but there are also open source platforms. Common MLOps platforms include:
Explore a data-driven selection of leading MLOps platforms to find the best fit for your ML needs.
MLOps Assistant Tool
These tools are used to assist MLOps and LLMOps developers with specific aspects of MLOps and LLMOps deployment. These tools include:
- Function store:The feature store serves as a central hub for storing, managing, and distributing ML features. They facilitate the discovery and sharing of feature values and support both model training and serving. Key features include the ability to create feature engineering pipelines, efficient feature delivery, scalability, version control, validation, metadata management, and integration with ML workflows for improved reproducibility.
- Integration framework: These frameworks are useful for developing LLM applications such as document analyzers, code analyzers, and chatbots.
- Vector database (VD): Vector databases store complex, multidimensional data such as patient records that combine symptoms, test results, and behavioral patterns. VD can search and retrieve unstructured data (images, video, text, audio, etc.) by content rather than labels or tags. VD helps with model versioning and management in MLOps and LLMOps.
LLMOps
Large Language Models Operations is a specialized subset of Machine Learning Operations (MLOps) tailored for efficient development and deployment of Large Language Models (LLMs).
LLMOps provides the infrastructure and tools to ensure that model quality remains high and data quality is maintained throughout your data science project.
LLMOps includes platforms and utilities for managing LLMs, from fine-tuning and evaluation to deployment and monitoring. Check out our data-driven market guide to learn more about other LLMOps tools.
AI governance
AI governance establishes frameworks and policies that shape how AI technologies are developed, deployed, and regulated. The primary objective is to promote ethical AI practices and social benefits while reducing risks such as bias and unintended consequences.
Since AI governance is a critical aspect of any ML project, an end-to-end MLOP platform provides AI governance capabilities. Read our comprehensive market guide to discover other AI governance tools.
Figure 1: MLOPs Tools Market Map displays subcategories of MLOPs such as LLMOP and related areas.
FAQ
MLOps applies repeatable practices to make ML development, deployment, and monitoring more efficient and reliable.
If you still have questions about MLOps tools and vendors, or artificial intelligence in general, we’d love to help you:
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principal analyst
Sem Dilmegani
principal analyst
Cem’s work has been cited by major global publications such as Business Insider, Forbes, and the Washington Post, global companies such as Deloitte and HPE, NGOs such as the World Economic Forum, and supranational organizations such as the European Commission. See more reputable companies and resources that reference AIMultiple.
Throughout his career, Cem has worked as a technology consultant, technology buyer, and technology entrepreneur. He has spent more than a decade advising companies on technology decisions at McKinsey & Company and Altman Solon. He also presented a McKinsey report on digitalization.
Reporting to the CEO, he led the communications company’s technology strategy and procurement. He also led the commercial growth of deep tech company Hypatos, from 7-figure annual recurring revenue and 0 to 9-digit valuation within two years. Cem’s work at Hypatos has been featured in major technology publications such as TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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