Deploy ML Models with Flask: MLOps Tutorial for Beginners
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Ready to deploy your machine learning models? This tutorial kicks off our MLOps series by showing you how to deploy an ML model using Flask, a lightweight and powerful Python web framework. We'll walk you through the entire process, from loading your model to creating a REST API endpoint that can serve predictions.
This video is perfect for beginners who are new to MLOps and want to learn practical skills for deploying machine learning models. We'll cover:
Setting up your Flask environment
Loading your pre-trained ML model (e.g., scikit-learn, TensorFlow, PyTorch)
Creating API endpoints for prediction
Handling input data and returning predictions
Testing your deployed model
This is the first video in a series focused on MLOps tools like MLflow, Streamlit, Airflow, and Gradio. We will then go on to cover more advanced deployment methods with Gradio, Hugging Face Spaces and monitor model performance with MLflow, and retrain model with Airflow.
Stay tuned for future videos covering model monitoring, retraining pipelines, and more advanced MLOps topics.
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