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.
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.
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.
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.
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.
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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