Automation DevOps Explainability ML Engineer 
The MLOps Blog
JANUARY 23, 2023
Lived through the DevOps revolution. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. I don’t see what special role ML and MLOps engineers would play here. –
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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IBM Journey to AI blog
AUGUST 11, 2023
It advances the scalability of ML in real-world applications by using algorithms to improve model performance and reproducibility. The paper suggested creating a systematic “MLOps” process that incorporated CI/CD methodology commonly used in DevOps to essentially create an assembly line for each step. What is MLOps?
AWS Machine Learning Blog
DECEMBER 19, 2023
Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Machine learning operations (MLOps) applies DevOps principles to ML systems. It’s much more than just automation.
AWS Machine Learning Blog
FEBRUARY 27, 2024
However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products.
Heartbeat
FEBRUARY 1, 2023
Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. They may also be involved in the deployment process’s automation.
AWS Machine Learning Blog
JANUARY 5, 2024
This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.
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