Towards Data Science’s Post

Sabrine Bendimerad introduces MLflow, an important tool for experiment tracking and model management in machine learning workflows. She demonstrates how to deploy and use MLflow within a Docker container to ensure portability and avoid issues related to dependencies. #Docker #MachineLearning

Thanks to Towards Data Science for publishing my last article on Deploying an MlFlow server on Azure (Using a docker container) to track ML experiments 🤩 #mlops #datascience #womentechmakers Women Techmakers

🔍 Did you know that managing compute resources for ML model development can be a challenge? Organizations are facing difficulties in scaling and controlling costs when training large-scale models. Have you encountered similar struggles in your work or projects? Share your thoughts below! https://lnkd.in/eq2mp3qS

Introduction to machine learning operations (MLOps) - https://lnkd.in/gi76HGht Machine learning operations (MLOps) applies DevOps principles to machine learning projects. Learn about which DevOps principles help in scaling a machine learning project from experimentation to production. Prerequisites Some familiarity with machine learning and Azure Machine Learning. https://lnkd.in/gi76HGht

Hello there, Here I have a compiled list of essential Microsoft Learn resources🥳 to help you explore a wide range of Microsoft Learn resources💻 to build expertise across multiple domains, including MLOps, AI, GitHub, cloud computing, and data analytics. Whether you’re just starting out or looking to deepen your knowledge, this learning modules will guide you through essential principles, tools, and workflows to enhance your skill set.Goodluck!! 🚀 1. Introduction to DevOps principles for machine learning:https://lnkd.in/exma66-w 2. Source control for machine learning projects: https://lnkd.in/eZUHu9hK 3. Automate machine learning workflows: https://lnkd.in/edyn6rEx 4. Continuous deployment for machine learning: https://lnkd.in/eBKm_V92 5. Get started building with Power BI: https://lnkd.in/er6cKDzC 6. Fundamentals of Generative AI: https://lnkd.in/eMSJgGUQ 7. Introduction to Git: https://lnkd.in/eU9VTf-G 8. Introduction to GitHub: https://lnkd.in/eEpkn_s8 9. Using GitHub Copilot with python: https://lnkd.in/ehhkzP2y 10. Describe Cloud Computing: https://lnkd.in/eEqBmJaa

Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course by Duke University covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML. By the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks. You will - Explore MLOps technologies and pre-trained models to solve problems for customers. - Apply ML and AI in practice through optimization, heuristics, and simulations. - Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github. - Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers. Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps.

Gang Scheduling in Kubernetes 🚀 First, let's understand the concept of gang scheduling. Gang scheduling is a scheduling technique used in distributed computing systems, particularly in high-performance computing environments. It involves scheduling a group (or "gang") of related tasks or processes to run simultaneously on multiple processors or nodes. One classic example is in deep learning workloads. Deep learning frameworks (Tensorflow, PyTorch etc) require all the workers to be running during the training process. In this scenario, when you deploy training workloads, all the components should be scheduled and deployed together to ensure the training works as expected. Or else it will end up in resoruce fragmentation and deadlocks (explanation covered in the blog) Gang scheduling is like making sure that all tasks in a group that depend on each other start running together at the same time, or they don't start at all—it's an all-or-nothing approach. In today's blog, we will explore a Gang scheduling concepts and how it can be implemented with Kubernetes custom schedulers and plugins. 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗕𝗹𝗼𝗴: https://lnkd.in/gsvwnZMk If you have any thoughts or knowledge to share, feel free to comment below or on the blog post. ♻️ PS: Repost and share with the community if it is helpful :) #DevOps #kubernetes #MLOPS

🌟 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 Workflow for End-to-End ML Lifecycle 🌟 In my previous posts, I explained the pipelines of an end-to-end ML project and the 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 within it. Now, let me walk you through the 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 workflow. 🚀 Overview This pipeline begins by taking artifacts from the 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 and progresses through three main components: 1️⃣ 𝑴𝒐𝒅𝒆𝒍 𝑻𝒓𝒂𝒊𝒏𝒊𝒏𝒈: - Trains the model using the training dataset. - Evaluates performance metrics on the test dataset. - Outputs both the trained model and evaluation metrics. 2️⃣ 𝑴𝒐𝒅𝒆𝒍 𝑬𝒗𝒂𝒍𝒖𝒂𝒕𝒊𝒐𝒏: - Compares the trained model against the existing model using pre-defined evaluation rules. - Determines whether the newly trained model is suitable for deployment. 3️⃣ 𝑴𝒐𝒅𝒆𝒍 𝑷𝒖𝒔𝒉𝒆𝒓: - If validation passes, the final model (including model.pkl and preprocessing.pkl) and all other artifacts from previous components are pushed to an Amazon S3 bucket. 🎯 Model Registry Using Amazon S3 In this setup, we leverage Amazon S3 as a 𝐦𝐨𝐝𝐞𝐥 𝐫𝐞𝐠𝐢𝐬𝐭𝐫𝐲. It handles versioning for every successful training by storing artifacts, metadata, and the final model with a timestamp. 🔍 Why S3? While tools like #SageMaker Model Registry or #MLflow could be used, this approach demonstrates the flexibility of using S3 for model and artifact versioning and registry purposes. 🔧 Next Steps In upcoming posts, I’ll discuss how deployment is orchestrated using a robust #Docker and CI/CD pipeline. Stay tuned! There, we will leverage #GitHub_Actions, #AWS_ECR, and #AWS_EC2 to seamlessly build, containerize, and deploy applications. Stay tuned! 💡 Feel free to share your thoughts or experiences in setting up training pipelines! #MachineLearning #MLOps #DataScience #AWS #ModelRegistry #AmazonS3 #AI #ArtificialIntelligence #CICD #ModelTraining #MLPipeline #DataEngineering #DeepLearning #DevOps #SageMaker #MLflow

A Comprehensive Guide on How to Build an MLOps Pipeline #MLOps #Pipeline

Check out the latest update from Medium.com ! MLOps: Bridging the Gap Between Machine Learning and DevOps Read more: https://lnkd.in/eQts_53n #News #Medium #DevOps