« Back to Glossary IndexMachine learning operations (MLOps) are practices for deploying and maintaining machine learning models in production. By using these practices, teams that manage the machine learning lifecycle, such as operations professionals, data scientists, and IT, will collaborate and communicate more effectively.
MLOps is fairly new and derives its principles from DevOps—the practice of efficiently producing, deploying, and running software—but applied to machine learning projects.
Why Is MLOps Important?
MLOps are unique to each business since what works for one may not be the optimal plan for another. However, if done correctly, it will result in these advantages:
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Efficiency: Collaborating teams can develop ML/DL models, deliver accurate models, and deploy these in the shortest time possible, thanks to an efficient workflow based on the MLOps framework.
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Scalability: Professionals can manage and monitor models for continuous integration and delivery because of better collaboration among teams, allowing them to scale production quickly and easily.
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Cost Reduction: With MLOps, developers can be more agile in their decisions, lowering costs associated with prolonged operations.
What Are MLOps Best Practices?
Various components of the ML pipeline require different best practices. Below are a few to keep in mind:
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Data: A model’s accuracy depends on the data it was trained on, so prepare raw data by correcting or adjusting it. After cleaning the data, label it properly and store it where teams can access and share it.
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Model: Begin with a simple model to get the infrastructure correct. During training, regularly adjust the parameters to develop a model that best solves the problem. Getting the model trained right involves numerous iterations, so it’s essential to have a version control tool to track the data and model changes.
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Deployment: Although deployment is at the latter stage of an ML lifecycle, it is still essential and requires continuous monitoring. Keeping track of the activities of the model in production ensures it behaves as intended or may need new data to work more accurately.
MLOps & Pachyderm
MLOps aims to speed up the ML life cycle, but it can only do so much without a reliable pipeline. With Pachyderm in your stack, tracking every version of your data, models, and code becomes more straightforward and more streamlined. Its best-in-class version control and data lineage features let your team focus on taking ML models to production. Try Pachyderm for free today.
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