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5 top tips for getting your AI game to the next level.

On this page, we’ve detailed a few of our top tips for setting up your AI project for success. MLOps is the biggest unknown for most companies: getting ahead in this space will make you and your team unstoppable.

5 top tips for setting up your MLOps workflow

  1. Facilitate going beyond Jupyter Notebooks

    By Martijn, AI Lead at Lifely

    Data scientists’ comfort zone is working and experimenting in their Jupyter Notebooks. Don’t get me wrong, I do the same. However, a straightforward process is essential to facilitate an orderly process of going from these (sometimes messy) notebooks to production-ready code. Automating some of these steps is also highly recommended. There is a lot possible through GitHub actions or by using the visual editor for creating pipelines through UbiOps.

  2. Standardize but leave room for customization

    By Victor, CTO at UbiOps

    It is important to realize that the processes and tools needed to implement MLOps in your team or organization are not set in stone. Each team can have different needs which calls for a standard stack of interoperable niche tools. The MLOps space will see a lot of development and growth over the next few years as the reliable operation of ML within organizations becomes critical for delivering the expected return on investment for advanced analytics.

  3. Track your experiments

    By Peter, Technical Director & Partner at Lifely

    Developing and maintaining a data science or machine learning pipeline means experimenting by changing data preprocessing, adjusting network architecture or changing learning parameters. Every decision you make can impact your model’s metrics: always track your experiments and their results. We recommend using DVC, a version control system for machine learning projects.

  4. Maintain your models well

    By Anouk, Product Owner at UbiOps

    I advise you to think about what will happen after a model makes it to production. Model maintenance is very often an afterthought! But to make sure your models continue to perform as you planned, you will need to be able to quickly iterate on them, without having any downtime whenever you do so. UbiOps helps you keep an overview of all your models and quickly update what’s currently in production.

  5. Test your complete workflow

    By Dirk, Backend & DevOps at Lifely

    The general idea data scientists have is that because models will never produce the exact same thing all the time, testing their architecture is almost impossible. This is not the case: we highly recommend having a suite of tests to for example make sure data types are what we expect them to be, that certain metrics are above a defined level to combat drift, or that endpoints for inference are still available after deployment.

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