Machine Learning Pipelines on Kubernetes
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Machine Learning Pipelines on Kubernetes

Machine Learning Pipelines on Kubernetes
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Cloud native technologies, artificial intelligence (AI) and machine learning (ML) pipelines are converging to provide businesses with real-time data analysis, insight and IT operational efficiencies. Data streaming is the technology at the heart of this trend. Data and events are continuously collected and analyzed, resulting in a shift from batch to stream processing, and from data centers on premise to the cloud. Along with this shift, a new crop of cloud native technologies, such as Kubeflow and MLflow, has arisen to optimize data streaming for containerized applications built in a microservice architecture orchestrated by Kubernetes.

The New Stack’s Machine Learning Pipelines on Kubernetes ebook will examine some of the use cases and trends in AI/ML enabled by data streaming and cloud native technologies. We offer a practical analysis of the pros and cons of data streaming and other aspects of the ML pipeline as it exists today. This ebook will cover topics including:

  • How developers and application architects should consider state in containerized, microservices-based applications.

  • How best to store and process data in cloud native applications.

  • The latest advancements and innovations in data processing at scale, including new tools and technologies.

  • Use cases for DevOps teams that utilize data streaming with Kubernetes.

  • How new data processing techniques relate to cloud native technologies such as microservice monitoring and observability, real-time analytics and AI/ML applications.

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