DevOps Metadata Software Engineer 
Unite.AI
NOVEMBER 20, 2024
These agents perform tasks ranging from customer support to software engineering, navigating intricate workflows that combine reasoning, tool use, and memory. This is where AgentOps comes in; a concept modeled after DevOps and MLOps but tailored for managing the lifecycle of FM-based agents.
AWS Machine Learning Blog
MARCH 10, 2025
The use of multiple external cloud providers complicated DevOps, support, and budgeting. It also enables economies of scale with development velocity given that over 75 engineers at Octus already use AWS services for application development. These operational inefficiencies meant that we had to revisit our solution architecture.
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The MLOps Blog
JANUARY 23, 2023
Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork.
AWS Machine Learning Blog
DECEMBER 19, 2023
Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations.
AWS Machine Learning Blog
MARCH 5, 2025
The examples focus on questions on chunk-wise business knowledge while ignoring irrelevant metadata that might be contained in a chunk. He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. You can customize the prompt examples to fit your ground truth use case.
The MLOps Blog
JUNE 27, 2023
When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?
Unite.AI
JUNE 24, 2024
It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. As the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased. What is MLOps?
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