What Is MLOps?
The term “MLOps” (a compound of Machine Learning and Operations) refers to the practice of deploying, managing, and monitoring machine learning models in production.
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I recently started a new job at a Machine Learning startup. I’ve given up trying to explain what I do to non-technical friends and family (my mum still just tells people I work with computers). For those of you who at least understand that “AI” is just an overused marketing term for Machine Learning, I can break it down for you using the latest buzzword in the field:
The term “MLOps” (a compound of Machine Learning and Operations) refers to the practice of deploying, managing, and monitoring machine learning models in production. It takes the best practices from the field of DevOps and utilizes them for the unique challenges that arise when running machine learning systems in production.
The term is relatively new and has grown rapidly in usage over the last year and is a direct result of a maturing Machine Learning landscape. As businesses get good at collecting data, designing and training ML models, their focus shifts towards integrating those models into their software estates. This brings all sorts of new challenges around infrastructure, scalability, performance, and monitoring that most data science teams are not traditionally equipped to deal with.
One approach is to segregate duties between Data Science and DevOps like so:
- Data Science: design, build, and evaluate the models
- DevOps: deploy, monitor, and manage the models
This seems like a good idea at first but we only need to start asking some questions to see where we might struggle:
- When do we retrain a model and deploy a new version?
- What are the expected input/output formats of the model? Do we need to validate them?
- Can the model performance be optimized by utilizing a GPU?
- How do we allow models to be continually tested?
Answering any of these questions requires knowledge of both the model itself and the complex environment it’s deployed in.
The reality is that the whole lifecycle of an ML system is tightly coupled and highly iterative in nature. Production ML is hard and requires expertise in Data Engineering, Data Science, and DevOps. The umbrella term “MLOps” provides an easy way to refer to the techniques, tools, and skilled engineers who inhabit the growing space between these disciplines.
So is MLOps just another buzzword? Absolutely! But for now, it’s the best we’ve got and it serves an important purpose.
Published at DZone with permission of Ed Shee. See the original article here.
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