All Homework Data: NYC TRIP DATA
In this module I learned about what is MLOps and the maturity of MLOps. Not just that in this module I also learned about how to setup the environment for our machine (linux).
In this module I learned about how to track our experiment as a data scientist and machine learning engineer. Sometimes as a data scientist we do many of experiments before we get the best model, and we need to tracking those environments, therefore we use mlflow or weight and biases to help us tracking our environment an analyzed our experiment (accuracy, RMSE, parameters and so on)
In this module, I learned about how to use Prefect a orchestration tool for MLOps, where we can see our model's peformance every day or schedule our run deployment. To deploy this Prefect we need to push our script to Github. data .prefectignore deployment.yaml mlflow.db prefect.yaml and mlruns/ are the dapendencies for prefect deployment using Github, because when we want to deploy Prefect using data, prefect will see the directory from root directory of github. That is why I put all dependencies in main root directory of my github. You can see more of Prefect on Prefect - Docs
In this module, I learned about how to deploy model with several deployment types like, batch and online (streaming and web server)