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Continuous Machine Learning with Kubeflow
Publisher:
BPB Publications
| Author:
Aniruddha Choudhury
| Language:
English
| Format:
Paperback
Publisher:
BPB Publications
Author:
Aniruddha Choudhury
Language:
English
Format:
Paperback
₹809
Save: 10%
In stock
Ships within:
1-4 Days
In stock
ISBN:
Page Extent:
330
Continuous Machine Learning with Kubeflow’ introduces you to the modern machine learning infrastructure, which includes Kubernetes and the Kubeflow architecture. This book will explain the fundamentals of deploying various AI/ML use cases with TensorFlow training and serving with Kubernetes and how Kubernetes can help with specific projects from start to finish.
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.
After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies.
Description
Continuous Machine Learning with Kubeflow’ introduces you to the modern machine learning infrastructure, which includes Kubernetes and the Kubeflow architecture. This book will explain the fundamentals of deploying various AI/ML use cases with TensorFlow training and serving with Kubernetes and how Kubernetes can help with specific projects from start to finish.
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.
After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies.
About Author
Aniruddha Choudhury has more than 5 years of IT professional experience in providing Artificial Intelligence development solutions, MLOPS Kubeflow, Multi-Cloud GCP, AWS, Azure and is passionate about Data Science, Data Engineering, and MLOPS complex solutions provider in Machine Learning, Deep learning, and solving with cutting edge tech. Currently, he is working with Publicis Sapient as a Full stack Senior Data Scientist for more than 1 year and 6 months, working in Multiple Artificial Intelligence engineering product development in various domain-related machine learning craft and alongside expertise in MLops in production for AI use-cases with multiple technologies with Kubeflow, etc. in various Clouds. Before that, he worked previously with Incture Technology as a Senior Scientist worked for 2 years across the energy domain industry for AI with SAP and AWS tech stacks. After that, he worked with Wells Fargo Bank for 2 years as a Data Scientist in R & D for solving various diversity of financial products AI solutions on various lines of business. with Artificial Intelligence.
Alongside, he is a speaker in the community for multiple forums for MLops, Data Science, and technical bloggers on various platforms. He is a certified data scientist from Coursera (Michigan University) and has various cloud Architect certificates in Azure, Google Cloud Platform. He is an active contributor in various Github open source community projects like Kubeflow (Google).
He is a master in building Artificial Intelligence Solutions and finding complex patterns from research papers to gain optimal solutions to the current product development alongside a self-innovative mind.
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