Introduction to MLOps: Six proven techniques to amplify AI maturity
Learn how to leverage MLOps to amplify AI and data science productivity and maturity through six proven techniques from the pros.
If you are working in AI or studying data science, then you might have heard about MLOps and be wondering what the buzz is all about. Data science is a rapidly evolving and maturing space. New techniques and technologies are emerging all the time to help amplify the productivity and maturity of data scientists’ work. MLOps is all about this.
MLOps is the natural extension of DevOps applied to AI and Machine Learning, adapting the advanced automation techniques of the pros to the new learnings and technologies that drive artificial intelligence and machine learning systems.
In this webinar, we will review how adopting a few key MLOps techniques can help you get the most out of your data science projects. We will examine some of the most popular MLOps techniques adopted by the leading industry players, and how you can adopt them too:
- Machine Learning pipelines
- Continuous deployment
- Inference services
- Blue-green deployments
- Automated drift detection
- Feature stores
Want to learn more?
- Learn more about AI/ML on Ubuntu
- Visit Charmed Kubeflow website