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Moreover, the JuMa infrastructure, which is based on AWS serverless and managed services, helps reduce operational overhead for DevOps teams and allows them to focus on enabling use cases and accelerating AI innovation at BMW Group. More importantly, the use of these platforms was misaligned with BMW Group’s IT cloud-first strategy.
It initiates the collection, indexing, and analysis of machine-generated data in real-time. It helps harness the power of bigdata and turn it into actionable intelligence. Moreover, it allows users to ingest data from different sources. Additionally, Splunk can process and index massive volumes of data.
He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. Rahul Jani is a Data Architect with AWS Professional Service. He collaborates closely with enterprise customers building modern dataplatforms, generative AI applications, and MLOps.
The architecture maps the different capabilities of the ML platform to AWS accounts. The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake.
AI for DevOps and CI/CD: Streamlining the Pipeline Continuous Integration and Continuous Delivery (CI/CD) are essential components of modern software development, and AI is now helping to optimize this process. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time.
IBM merged the critical capabilities of the vendor into its more contemporary Watson Studio running on the IBM Cloud Pak for Dataplatform as it continues to innovate. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.
Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure dataplatforms in this diagram are neither exhaustive nor prescriptive.
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