Architecting ML systems
1. Architecting ML systems
Person: Welcome to "Architecting ML Systems," the second module of the Production Machine Learning Systems course. In this module, we'll explore what makes up an architecture and why and how to make good systems design decisions. Let me ask you a question. You'll recall from earlier in this specialization, we showed how time is distributed among the different tasks necessary to launch an ML model, and surprisingly, modeling accounted for far less than most people expect. The same is true with respect to the code. So the answer is that ML model code typically accounts for about 5% of the overall code base. ML models account for such a small percentage because keeping a system running in production requires many more actions than just computing the model's outputs for a given set of inputs. In this module, you'll see what else a production ML system needs to do and how you can meet those needs. Upon completing this module, you should know how to choose an appropriate training and serving paradigm, serve ML models scalably, and design an architecture from scratch. And although our focus is on Google Cloud, it's important that you always try to reuse generic systems-- many of which are open-source frameworks-- when possible. What's true of software frameworks like TensorFlow, Spark, or Apache Beam is also true of the underlying infrastructure on which you execute them. Instead of spending time and effort provisioning infrastructure, you can use manage services such as Dataproc, AI Platform, or Dataflow to execute your Spark, TensorFlow, and Beam code.2. Let's practice!
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