The machine learning models are valuable only when deployed, and we can take full advantage of our business use case. When we start machine learning model development, we mostly focus on which algorithms to use, feature engineering, and hyperparameters to make the model more accurate, but the model deployment is a most critical step in the machine learning pipeline.
In this workshop, we are going to learn about ML lifecycle from gathering data to the deployment of models. Researchers and Data Scientists can build a pipeline to log and deploy machine learning models. We will learn about machine learning models’ challenges in production and different toolkits to track and monitor these models once deployed.
FORMAT: Pre-recorded videos (More than 5.5 hours of content) & Colab notebooks