Kubernetes: Simplifying Machine Learning Workflows
Kubernetes: Simplifying Machine Learning Workflows


You know Kubernetes is a great platform for the applications you’re running today. Most of the applications you’ll be excited about tomorrow are intelligent applications, which collect data and rely on machine learning to support essential functionality. These capabilities often seem like magic to users, but building applications and services that leverage artificial intelligence is more accessible than you might think. This hands-on lab will show how Kubernetes, the most popular open source container orchestration platform, can increase collaboration and decrease time to value for machine learning workflows. Data scientists will be able to create workflows with ease, deploy models as micro-services and monitor performance to understand when retraining is needed. We’ll focus on the open source infrastructure, tools and processes that will help you to get meaningful results from application intelligence and show why Kubernetes is the best place for data science workloads. You’ll leave having solved a real business problem interactively with powerful machine learning techniques and Kubernetes.


Anish Asthana is an experienced Software Engineer with a demonstrated history of working in the computer software industry. Skilled in Python, C++, and cloud technologies such as Kubernetes, Prometheus, and the ELK stack. Strong information technology professional with a Master of Science in Electrical and Computer Engineering Specializing in Data Analytics from Boston University College of Engineering.

Open Data Science




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