Build Your MLOps Tool Stack
Sometimes, the best way to move forward is to take a step back, which is exactly what we did. We’ve decided to start from scratch and rethink our entire machine learning infrastructure and operations.
Setting up an ML infrastructure is a challenging task. These resources should help you do it right.
You’ll find here everything you need to know about building your MLOps tool stack:
What is MLOps?
MLOps (Machine Learning Operations or Machine Learning Model Operationalization Management) is a set of practices for collaboration and communication between data scientists and operations professionals.
Applying these practices:
- increases the quality
- simplifies the management process
- and automates the deployment
of Machine Learning and Deep Learning models in large-scale production environments. It’s easier to align models with business needs, as well as regulatory requirements.
MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle – data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.
Read more about MLOps here:
MLOps tool stack
The range of MLOps tools is very wide. There are tools specific for only some parts of the MLOps pipeline and those that can help you manage the whole process.
Here are a few articles that can help you build your MLOps tool stack.
Examples of MLOps implementation
If you want to read about how different ML practitioners and industry teams built their MLOps tool stacks, check these case studies and examples.