This book will help demonstrate how to use Kubeflow components, deploy them in GCP, and serve them in production using real-time data prediction. With Kubeflow KFserving, we'll look at serving techniques, build a computer vision-based user interface in streamlit, and then deploy it to the Google cloud platforms, Kubernetes and Heroku. Next, we also explore how to build Explainable AI for determining fairness and biasness with a What-if tool. Backed with various use-cases, we will learn how to put machine learning into production, including training and serving.

This is the first part of a series of blog posts that describe how to use Data Version Control (DVC), and Continuous Machine Learning (CML) when developing conversational AI assistants using the Rasa framework. This post is mostly an introduction to these three components, in the next post I'll delve into the code, and how to get everything connected for Rasa MLOps bliss. If you've not heard of Data Version Control (DVC), you've been missing out. DVC is an exciting tool from iterative.ai DVC extends git's functionality to cover your data wherever you want to store it, whether that is locally, on a cloud platform like AWS S3, or a Hadoop File System. Like git, DVC is language agnostic.

CI/CD is a key concept that is becoming increasingly popular and widely adopted in the software industry nowadays. Incorporating continuous integration and deployment for a software project that doesn't contain a machine learning component is fairly straightforward because the stages of the pipeline are somewhat standard, and it is unlikely that the CI/CD pipeline will change a lot over the course of development. But, when the project involves a machine learning component, this may not be true. As opposed to traditional software development, building a pipeline for a machine learning components may involve a lot of changes over time, mostly in response to observations made during past iterations of development. Therefore, for ML projects, notebooks are widely used to get started with the project, and once a stable foundation (base code for different stages of the ML pipeline) is available to build upon, the code is pushed to a version control system, and the pipeline is migrated to a CI/CD tool such as Jenkins or TravisCI.

Totally, open-source, and made by the same development team that created the popular DVC (data version control) library, CML (Continous Machine Learning) library is a great tool that can be used to automate Machine learning workflows, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. This brings the power of DevOps to ML or MLOps. CML is built with the objective of bringing ML projects, and MLOps practices in a way such that it should be built on top of traditional engineering tools and not as a separate stack. This could be the future of MLOps.