From the course: MLOps and Data Pipeline Orchestration for AI Systems
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Install MLflow and prepare data for machine learning
From the course: MLOps and Data Pipeline Orchestration for AI Systems
Install MLflow and prepare data for machine learning
- [Instructor] In this demo, we'll train multiple models using ML Flow. Deploy those models to the ML Flow registry and use those models for predictions. We are going to be working with Python, so make sure you have a recent version of Python available. Python 3.8 and above. You can install ML Flow on your local machine with a pip install mlflow. This will install the latest version, which happens to be 2.2 1.3 at the time of this recording, my current working directory happens to be mlflow_project, and here you can see I have a notebook within which I'll be writing my code and a CSV file. That will be our training and evaluation data. Let's bring up the ML Flow UI. This is a web-based interface that lets users track and visualize machine learning experiments, including parameters, metrics, artifacts, and model versions. It should now be running on local host port 5,000. Let's head over and take a look and here is what the ML Flow UI looks like. ML Flow sets us up with the default…
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