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Unleashing real-time insights: Monitoring SAP BTP cloud-native applications with IBM Instana

IBM Journey to AI blog

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. Auto-discovery and dependency mapping : Automatically discovers and maps services and their interdependencies.

DevOps 231
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Modernizing child support enforcement with IBM and AWS

IBM Journey to AI blog

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.

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Application modernization overview

IBM Journey to AI blog

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. Ease of integration of APIs with channel front-end layers.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

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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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

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.

DevOps 114
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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. Limitations This solution has the following limitations: The model provides high-accuracy completions for English language. Mateusz Zaremba is a DevOps Architect at AWS Professional Services.

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Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform

AWS Machine Learning Blog

IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible while following best practices in the area of development operations (DevOps). Later, the auto-shutdown script will run the s3 cp command to download the extension file from the S3 bucket on Jupyter Server start-ups.