What are MLOps Tools? MLOps tools help organizations apply DevOps practices to the process of creating and using AI and machine learning (ML) models. These tools are typically used by machine learning engineers, data scientists, and DevOps engineers. Since machine learning is broadly applicable to many different needs, MLOps tools aren’t limited to specific industries. MLOps tools were developed to help bridge the gap between creating ML models and generating business value from those models. A ...
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MLOps tools help organizations apply DevOps practices to the process of creating and using AI and machine learning (ML) models. These tools are typically used by machine learning engineers, data scientists, and DevOps engineers. Since machine learning is broadly applicable to many different needs, MLOps tools aren’t limited to specific industries.
MLOps tools were developed to help bridge the gap between creating ML models and generating business value from those models. A well-trained ML model can be useful on its own, but often provides much less value than a model that is fully integrated with existing business software and data. MLOps tools assist with this integration by offering tools to integrate the training, testing, and versioning of ML models with the overall DevOps pipeline.
MLOps tools often focus on managing and integrating ML pipelines with data pipelines and software deployment pipelines. The ML pipeline broadly covers the process of training, evaluating, testing, and exporting ML models.
To integrate data pipelines, MLOps tools often include functionality to configure, clean, and track the data used for training and testing ML models. To integrate models into deployment pipelines, MLOps tools commonly offer ML model packaging and deployment features. This functionality helps developers define consistent, reliable interactions between ML models and other software.
Although the terms look similar, MLOps tools are fundamentally different from AIOps tools. MLOps refers to the integration of ML models with DevOps processes to smoothly integrate ML models into other applications. AIOps tools apply artificial intelligence (AI) and ML models and algorithms to IT operations such as application performance management and incident response.
Most MLOps tools commonly include the following features:
When comparing and choosing MLOps tools, consider these key differentiators:
Developer Expertise: Many MLOps tools are open-source and designed for developers with existing machine learning knowledge and skills. Some tools, however, offer low-code functionality. These tools might be helpful for new ML engineers but restrictive for veteran developers.
Integrations: MLOps tools integrate with other development tools, including IDEs, storage solutions, and more. When choosing an MLOps tool, be sure to select one that integrates with all the tools you already use, as well as tools you plan to use in the future.
Data Governance: MLOps tools commonly include data governance and security features, but some solutions offer more robust data security than others. If you don’t already have a strong data security tool, consider selecting a MLOps tool with comprehensive data security capability.
Start a comparison of MLOps tools here
Many MLOps tools offer some limited free version, whether that includes partial feature access or access to a number of compute hours for free. Beyond free versions, most MLOps tools are offered as a service and charge on an hourly basis, with rates increasing as memory needs increase. Businesses should expect to pay at least $0.05 an hour, but understand that if the highest performance is needed, the price could increase to up to $6.00 an hour or more.
Most MLOps tools also include charge as you go pricing, so if your organization needs high performance for a single workload, most tools enable them to just pay more for that workload, without having to commit to a more expensive plan long term.
These resources can help you learn more about machine learning and MLOps:
Popular tools in this category include:
