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Building on moving ground: Life as an MLOps engineer at Kensho
Over the past two years as an MLOps engineer at Kensho, I’ve come to appreciate both the challenges and the immense value of operating in an environment where implementation patterns, tools, and the surrounding ecosystem are constantly changing.
Distributed training with Kubernetes
Neural networks are more relevant than ever with the rise of GenAI, in particular large language models, and at Kensho we’ve been employing them in a wide range of applications. However, training these complex models can be computationally intensive and expensive. In collaboration with our ML Research team, Kensho’s infrastructure team tackled this challenge using a multi-node approach that could be easily integrated into our existing infrastructure.
Automating application deployments at Kensho
Kensho’s AI product offerings encompass about 20 applications, including core services as internal services like a debugging dashboard and a customer authentication tool. As our AI products promote efficiency for our customers, we are also always trying to automate internal workflows, such as deployments, as well. A six-member SRE — Site Reliability Engineering — team is responsible for working with development teams to deploy these services to production clusters, collectively known as Hydra.
ML Ops explained: Q&A With Senior ML Ops Engineer Matthew Theisen
This blog is part of our career stories series, which gives a glimpse of what it’s like to be a part of our team and share the career stories of Kenshins. Stay tuned for more articles on life at Kensho!
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