Auto-complete Blog DevOps 
IBM Journey to AI blog
OCTOBER 17, 2023
We also recommend reading the full article on the SAP Community blog site. This solution extends observability to a wide range of roles, including DevOps, SRE, platform engineering, ITOps and development. You can find a complete list of supported technologies for IBM Instana on this page.
AWS Machine Learning Blog
NOVEMBER 6, 2024
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IBM Journey to AI blog
JUNE 2, 2023
With its proven tools and processes, AIMM meets clients where they are in the legacy modernization journey, analyzing (auto-scan) legacy code, extracting business rules, converting it to modern language, deploying it to any cloud, and managing technology for transformational business outcomes. city agency serving 19M citizens.
AWS Machine Learning Blog
SEPTEMBER 4, 2024
DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring.
The MLOps Blog
JANUARY 23, 2023
Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Model monitoring tools will merge with the DevOps monitoring stack. Not a fork.
IBM Journey to AI blog
NOVEMBER 24, 2023
Application modernization is the process of updating legacy applications leveraging modern technologies, enhancing performance and making it adaptable to evolving business speeds by infusing cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on. The post Application modernization overview appeared first on IBM Blog.
AWS Machine Learning Blog
JANUARY 5, 2024
Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models.
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