Best of MLOps — September 2024

1. 1
   Article

   Hugging Face·2y

   Llama can now see and run on your device - welcome Llama 3.2

   Llama 3.2, developed in collaboration with Meta and available on Hugging Face, includes both multimodal vision models and text-only models. The Vision models come in 11B and 90B sizes and feature strong visual reasoning capabilities. Text-only models are available in 1B and 3B sizes, optimized for on-device use. Llama 3.2 also introduces a new version of Llama Guard for input classification, including harmful prompt detection. Integration with Hugging Face Transformers and major cloud services is supported, and fine-tuning can be accomplished with a single GPU.

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   10
2. 2
   Video

   freeCodeCamp·2y

   End-to-End Machine Learning Project – AI, MLOps

   The post provides a comprehensive guide on undertaking an end-to-end machine learning project focused on house price prediction. It delves into core machine learning concepts, data analysis, feature engineering, and model implementation with robust testing. Additionally, it emphasizes MLOps integrations using tools like ZenML and MLFlow for experiment tracking and deployment. The tutorial also underscores the importance of writing scalable and readable code by employing design patterns such as Factory and Strategy patterns. The project aims to differentiate itself by focusing on thorough data understanding and robust implementation practices, promising to enhance one's data science portfolio and career prospects.

   76
3. 3
   Article

   GitLab·2y

   Build an ML app pipeline with GitLab Model Registry using MLflow

   This tutorial guides you through setting up an MLOps pipeline using GitLab Model Registry and MLflow. It explains the importance of MLOps in managing and automating machine learning models' lifecycle, highlighting GitLab's features like version control, CI/CD pipelines, and collaboration tools. The tutorial includes instructions for setting up environment variables, training and logging models, registering successful candidates, and deploying an ML app using Docker.

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