DevOps ML Engineer Software Development 
IBM Journey to AI blog
AUGUST 12, 2024
MLOps is a set of practices that combines machine learning (ML) with traditional data engineering and DevOps to create an assembly line for building and running reliable, scalable, efficient ML models.
O'Reilly Media
OCTOBER 19, 2021
While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments.
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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.
IBM Journey to AI blog
AUGUST 11, 2023
Because ML systems require significant resources and hands-on time from often disparate teams, problems arose from lack of collaboration and simple misunderstandings between data scientists and IT teams about how to build out the best process. How to use ML to automate the refining process into a cyclical ML process.
Unite.AI
JUNE 19, 2024
Understanding MLOps Before delving into the intricacies of becoming an MLOps Engineer, it's crucial to understand the concept of MLOps itself. The MLOps Lifecycle The MLOps lifecycle involves three primary phases: Design, Model Development, and Operations. ML Pipeline Automation : Automate model training and validation.
NOVEMBER 24, 2023
With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.
AWS Machine Learning Blog
JULY 8, 2024
TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. ML model experimentation is one of the sub-components of the MLOps architecture. We encourage to you to get started with Amazon SageMaker today.
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