Ready to bridge the gap between deep learning models and production? Dive into this hands-on workshop where we combine neural networks with MLOps best practices using MLFlow! Whether you're a data scientist, ML engineer, or developer, this workshop recording will equip you with tools to build, track, deploy, and scale models efficiently.
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Github Repository : https://github.com/aissm-deeplearning/deep-learning.git
Dataset Used : https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri
What You’ll Learn:
Day 1: Deep Learning Fundamentals & MLFlow Basics
1. Set up your deep learning environment (TensorFlow/Keras).
2. Build and train neural networks for image and text data.
3. Track experiments, parameters, and metrics with MLFlow.
4. Best practices for reproducibility in deep learning workflows.
Day 2: MLOps with MLFlow
1. Model packaging, versioning, and deployment.
2. Scale model inference using MLFlow’s serving capabilities.
3. Monitor models in production and manage lifecycle.
4. Integrate MLFlow with cloud platforms (AWS/GCP/Azure).
🚀 Don’t forget to STAR the GitHub repo 🌟 to access all materials before the workshop!
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Email : info@xerxez.in
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