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MLOps: Monitoring and Managing Drift
Editor’s note: Oliver Zeigermann is a speaker for ODSC West 2023 this Fall. Be sure to check out his talk, “MLOps: Monitoring and Managing Drift,” there! The trouble with machine learning starts after you put your model into production.  Typically, you want to bring something into production that gives you... Read more
Machine Learning Operations (MLOPs) with Azure Machine Learning
Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. The data science team is now... Read more
How MLOps Work in the Era of Large Language Models
Large language models (LLMs) and generative AI have taken the world by storm, allowing AI to enter the mainstream and show that AI is real and here to stay. However, a new paradigm has entered the chat, as LLMs don’t follow the same rules and expectations of traditional machine learning... Read more
Solve Your MLOps Problems with an Open Source Data Science Stack
Editor’s note: Dean Pleban is a speaker for ODSC East 2023. Be sure to check out his talk, “Solving MLOps from First Principles,” there! Data scientists have challenges and need tools to overcome them. It’s best to use open-source, best-of-breed, modular solutions. It’s also a good idea to think about... Read more
Kubeflow MLOps : Automatic Pipeline Deployment with CI/CD/CT
If you already have a functioning Kubernetes cluster with Kubeflow installed on it, you can directly follow this guide. If you don’t, I strongly recommend checking my previous article. This time, we’ll go a step further and : Make an advanced pipeline that contains pre-processing, model building, inference and performance... Read more
8 Exciting MLOps Sessions Coming to ODSC West
Your machine learning projects don’t only need to succeed and produce results, but it’s crucial to keep operations on time, on budget, and most importantly, on point. That’s why MLOps is how organizations keep their ML projects on the right path. But for many teams, you might not know where... Read more
MLOps V2 Solution Accelerator – Unifying MLOps at Microsoft
Article by Scott Donohoo and Setu Chokshi of Microsoft. MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable, and reliable approach. Machine Learning Operations (MLOps) is key to accelerating how data scientists and ML engineers... Read more
5 Can’t-Miss MLOps Sessions Coming to ODSC APAC
ODSC APAC is just days away! On September 7th and 8th, some of the best and brightest minds and data scientists from around the world will gather to discuss the topics and tools shaping their industry. Check out a few of the sessions you can expect to see below. Key... Read more
MLOps for Musicians
Editor’s note: Akira is a speaker for ODSC APAC this September 7th-8th. Be sure to check out his talk, “MLOps for Musicians,” there! Just 5 years ago, thinking about how to deploy and reliably run AI models in a critical operation, known as MLOps, seemed like a stretch, when many... Read more
A Brief History of MLOps
Data is becoming more complex, and so are the approaches designed to process it. Companies have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. Machine learning has stepped in to fill the gap. However, the lifecycle falls... Read more