Towards Data Science’s Post

🚀 Excited to share my first blog on #MachineLearningDeployment! 🤖💻 Dive into the world of turning ML models into real-world solutions. From concept to implementation, discover the key steps and best practices and share your valuable feedbacks. 🌐✨ Check it out here: https://lnkd.in/gq_9vkdf #machinelearningengineer #machinelearning #Deployment #TechBlog #azurecloud #mlmodels #dockercontainer #docker

Happy to complete the #Devops, DataOps and MLOps course by Duke University. This course covers solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub and copilot to build solutions for machine learning (ML) and AI applications. It helps you create a great baseline of knowledge with great examples of how to use #ai in this capacity.

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

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

I just published my first technical article of the year. In this article, you will learn how to utilize Docker containers in deploying machine learning models. I hope you find it insightful. Your feedback will be highly appreciated. #machinelearning #mlops #docker #datascience

MLOps Tools #mlops #devops #ml

Supercharge Your #AI Career with MLOps! Ready to bring machine learning and #operations together? The MLOps Foundation Certification is your gateway to mastering #machine learning in production #environments. Learn from an expert and elevate your ML workflow skills. https://lnkd.in/dvWxbfUJ #MlOps #Certification #DataScience #integration #Governance #Monitoring #Tools #GitHub #Benefits #Operations

NoOps? -DevOps, FinOps, AppOps, MLOps, AIOps, GitOps, DataOps, SysOps ..etc...and Finally NoOps :) NoOps fits in rhythm :) but its different word. Let’s do simple math for fun. Say all above terms, are functions (f') So (f’) = (x' * Ops) If NoOps = No means x’ is null value so as per above function (f’) = (0'*Ops) = 0 which prove all above fancy x'Ops are nullified 😊 Remember, this is just some playful mathematics, not a real scientific formula. So, what is NoOps? Ops'(x) → 0 as Automation → ∞ = NoOps Which mean absolute automation of Ops so manual Ops is not required "Nirvana State” When No Operation is required that is NoOps Like Lambda or Azure function or K8S at some level We all know it just for fun published it 😊

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