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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