Build Your MLOps Tool Stack

    “We started like everyone else. We were having endless discussions, creating new notebooks, re-training, comparing the results. It worked for building a prototype, but we needed to organize it better. (…) We knew our ML operations needed to grow with the company, and we needed a setup that could handle it. (…)

    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.”

    Tymoteusz Wołodźko

    Tymoteusz Wołodźko

    Machine Learning Engineer at GreenSteam

    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:


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    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 cycle

    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.

    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.

    MLOps tools landscape
    MLOps tools landscape

    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. 

    Not sure how to start building your MLOps tool stack?

    Book a call with ML Teams Advocate