Introducing the Machine Learning Engineering for Production (MLOps) Specialization

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2 min read
Pictures of Robert Crowe, Andrew Ng and Laurence Moroney (from left to right)

Dear friends,

So you’ve trained an accurate neural network model in a Jupyter notebook. You should celebrate! But . . . now what? Machine learning engineering in production is an emerging discipline that helps individual engineers and teams put models into the hands of users.

That’s why I’m excited that DeepLearning.AI is launching Machine Learning Engineering for Production Specialization (MLOps). I teach this specialization along with co-instructors Robert Crowe and Laurence Moroney from Google. It also draws on insights from my team at Landing AI, which has worked with companies in a wide range of industries.

The work of building and putting machine learning models into production is undergoing a dramatic shift from individually crafted, boutique systems to ones built using consistent processes and tools. This specialization will put you at the forefront of that movement.

I remember doing code version control by emailing C++ files to collaborators as attachments with a note saying, “I’m done, you can edit this now.” The process was laborious and prone to error. Thank goodness we now have tools and practices for version control that make team coding more manageable. And I remember implementing neural networks in C++ or Python and working on the first version of distbelief, the precursor to TensorFlow. Tools like TensorFlow and PyTorch have made building complex neural networks much easier.

Building and deploying production systems still requires a lot of manual work. Things like discovering and correcting data issues, spotting data drift and concept drift, managing training, carrying out error analysis, auditing performance, pushing models to production, and managing computation and scaling.

But these tasks are becoming more systematic. MLOps, or machine learning operations, is a set of practices that promise to empower engineers to build, deploy, monitor, and maintain models reliably and repeatably at scale. Just as git, TensorFlow, and PyTorch made version control and model development easier, MLOps tools will make machine learning far more productive.

For me, teaching this course was an unusual experience. MLOps standards and tools are still evolving, so it was exciting to survey the field and try to convey to you the cutting edge. I hope you will find it equally exciting to learn about this frontier of ML development, and that the skills you gain from this will help you build and deploy valuable ML systems.

Keep learning!



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