The session will discuss and present how ML Models can be integrated with MLFlow for Tracking, Deployment, Versioning, etc. It will cover in detail how ML Models and its artifacts are logged to SQL Server and MLFlow for deploying efficiently. We will also see how a model can be dockerized successfully and pushed to DockerHub so that end users can use it as a Docker Container.

All this will be done through a live demo of a project of which all the artifacts and final model will be logged to the MLFlow and then dockerized.

Target Audience: AI and Software Engineers
Prerequisites: Basics of ML
Level: Basic

Extended Abstract:
Building a great Machine Learning model is just the beginning – the real magic happens when you can seamlessly track, version, and deploy it like a pro. In this session, we’ll unlock the full potential of MLflow to manage the entire ML lifecycle, transforming your models from development to production with precision and efficiency.

• Expect to dive deep into:
  Model Tracking – Log every experiment, artifact, and hyperparameter tweak directly into SQL Server and MLflow, ensuring nothing gets lost in the abyss of iterations.
• Versioning & Deployment – Ensure your models evolve gracefully with automated version control, enabling smooth rollbacks and updates.
• Dockerization Done Right – We’ll walk through the process of packaging your ML model into a Docker container, ready to deploy anywhere. By the end, you'll know how to push your model to DockerHub, making it accessible to anyone with a simple docker pull.

Through a live demo, we’ll bring theory to life – taking you step by step from model training to logging, versioning, and finally deploying a fully dockerized solution.

Whether you're an ML engineer, data scientist, or someone curious about deploying models at scale, this session will give you the tools to streamline your ML pipelines and bring your models to production faster, smarter, and with confidence.