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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

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. These can include statistical models ( regression analysis , for instance), rule-based systems and complex event processing models.

Big Data 266
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Achieve DevOps maturity with BMC AMI zAdviser Enterprise and Amazon Bedrock

AWS Machine Learning Blog

Although much of the focus around analysis of DevOps is on distributed and cloud technologies, the mainframe still maintains a unique and powerful position, and it can use the DORA 4 metrics to further its reputation as the engine of commerce. Using a Git-based SCM pulls these insight together seamlessly.

DevOps 119
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Top 6 Kubernetes use cases

IBM Journey to AI blog

Overview of Kubernetes Containers —lightweight units of software that package code and all its dependencies to run in any environment—form the foundation of Kubernetes and are mission-critical for modern microservices, cloud-native software and DevOps workflows.

DevOps 323
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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. Automation and remediation : Offers smart alerts, automatic event correlation, and proactive issue resolution.

DevOps 231
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OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

IBM Journey to AI blog

OpenTelemetry and Prometheus enable the collection and transformation of metrics, which allows DevOps and IT teams to generate and act on performance insights. Logs: Logs are a record of events that occur within a software or application component. What is OpenTelemetry?

DevOps 243
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Mastering MLOps : The Ultimate Guide to Become a MLOps Engineer in 2024

Unite.AI

MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. ML Operations : Deploy and maintain ML models using established DevOps practices.

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Best practices for hybrid cloud banking applications secure and compliant deployment across IBM Cloud and Satellite

IBM Journey to AI blog

To deploy applications onto these varying environments, we have developed a set of robust DevSecOps toolchains to build applications, deploy them to a Satellite location in a secure and consistent manner and monitor the environment using the best DevOps practices. DevSecOps workflows focus on a frequent and reliable software delivery process.

DevOps 280