In this talk, we'll learn about deploying ML models. First, we'll see how to do it with TF-Serving and Kubernetes, and in the second part of the talk, we'll do it with KFServing and Kubeflow.
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Unlike traditional development workflows, ML workflows are difficult to run on a local machine (due to the lack of memory, more CPUs/GPUs, etc). Imagine, if you could run your ML workflows the very same way as you do it locally, but they would run in the cloud. And you
We at Metro.digital have been building data science products for years and have gathered a fair amount of experience, especially in the area of recommendation systems. Over the last few months, my team’s been focusing on an exciting new challenge: building from scratch a machine learning-based recommendation system
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