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YouTube Excerpt: What does an MLOps stack look like? With an ever-increasing number of tools in the MLOps landscape, it helps to group them into stack components and then study available tool options for each. In this video, I translate MLOps concepts from a previous video into concrete stack components along with their corresponding tools that you can use while building your ML applications. There's also information on a few learning resources that I think are super helpful when learning more about each of these components, how they work and for choosing the right tools for your job! 0:00 ✅ What we'll cover in this video 1:37 ✅ Data Versioning Component 3:10 ✅ Feature Store 5:35 ✅ Experiment Tracker 7:26 ✅ Model Registry 8:55 ✅ Compute Environment Component 11:13 ✅ Data Validator 13:17 ✅ Model Deployer 15:54 ✅ Monitoring Component 18:09 ✅ Artifact Store 20:08 ✅ Orchestrator 22:08 ✅ MLOps Paper 23:52 ✅ ZenML Documentation 24:33 ✅ Deep dives coming next! Resources 📝: 1. ZenML Documentation on MLOps Stacks (https://docs.zenml.io/mlops-stacks/categories) 2. [Paper] Machine Learning Operations (MLOps): Overview, Definition, and Architecture (https://arxiv.org/ftp/arxiv/papers/2205/2205.02302.pdf) 👋 Connect with me: bio.link/wjayesh
What does an MLOps stack look like? With an ever-increasing number of tools in the MLOps landscape, it helps to group them into stack components...
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