While working on cognitive chatbots or automatic language translations, developers are increasingly integrating AI and machine learning technologies with cloud-native environments.
The new machine-learning, end-to-end pipeline starter kit by IBM simplifies the process and provides developers with everything they need to get started, which includes training and open-source support tools. This open-source Cloud-Native Toolkit allows developers to integrate artificial intelligence and machine learning applications to cloud-native environments and improve scalable, reliable deployments.
Developers can utilize the new toolkit as a starting point to transfer their ML and AI-powered applications from Jupyter notebooks to production environments using essential hybrid cloud technologies such as Kubernetes and Red Hat OpenShift. Providing a collection of opinionated methodologies and tools to ensure that projects operate properly and maximize business value during the process will help developers and data scientists speed up the creation, deployment, and innovation of projects.
With a set of opinionated approaches/tools, the starter kit accelerates development, deployment, and innovation. The kit would also save developers time by preventing them from becoming “bogged down” by the various components and responsibilities of shifting to cloud settings. The use of microservices has also accelerated technology integration.
To do so, developers can use the toolkit and design their models as microservices using the MAX* Framework. Then for continuous integration (using Jenkins & Tekton CI) and continuous delivery (using Argo CD), code analysis (using SonarQube), logging (using logDNA/sysdig), API support (using support), and health checks, developers can build and deploy on Red Hat OpenShift.
Here’s the link for getting started with this toolkit.
Further reading: IBM’s Blog.
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