MLOps platforms

MLOps platforms, or machine learning and operations platforms, are software systems and services that enable the end-to-end development and deployment of machine learning models. MLOps focuses on streamlining model development, training, and deployment, as well as managing the infrastructure that executes the models and serves their output.

An MLOps platform typically consists of several components including a model development and training environment, a scaling platform for large-scale training, an automated model management platform, and a deployment environment.

Model development and training is typically done using some combination of IDEs, version control systems, cloud-based platforms, and toolsets. Users can code, debug, optimize, and even automate model development. These tools allow models to be tested, trained, improved, and deployed incrementally.

The scaling platform allows users to manage and execute the training of models on a large scale. Data centers with high-performance computing resources are made available for large-scale training, extremely large datasets, and greater accuracy.

An MLOps platform also often includes an automated model management platform. This platform allows models to be tracked and monitored as they move through the development lifecycle. It can help teams track the quality of their models, flag any issues that may arise along the way, and facilitate sharing of models between stakeholders.

Finally, an MLOps platform includes a deployment environment for deploying, managing, and monitoring models in production. This environment typically combines an orchestration platform to define the model-execution pipeline with a cloud-based and/or on-premises deployment solution to deploy the models.

MLOps platforms are designed to simplify the process of developing, scaling, managing, and deploying models, from initial development to production use. They can provide benefits such as speeding up development cycles, reducing barriers to scalability, and even increasing the robustness of a model’s performance. As such, MLOps platforms are becoming increasingly popular, and are an essential part of the modern machine learning workflow.