Description
This book covers topics such as data management techniques, building models using different machine learning (ML) algorithms and deployment methods, in relation to building end-to-end machine learning pipeline. Training ML models in a standalone environment for research or academic purpose is very different than training models and moving them into production such that products across enterprise could invoke them for predictions. In the enterprise setting, there is a need for data science, product engineering and IT teams to collaborate with each other for understanding the problem, gather data from different product data sources, train and test different models, move the best model in production and finally monitor the models at regular intervals. All this requires one to have a good understanding of different aspects of ML model development lifecycle. This is where this book will come handy for different stakeholders including data scientists, product managers, IT staff and business leaders.
Mr. Chandim sett
Mr. Chandim Sett works as a leading machine learning software architect for HighRadius, a pioneer in the area of SaaS based products for Financial Supply Chain Industry. He is a Computer Science graduate from Kalinga Institute of Industrial Technology, Bhubaneswar and has served as the key person in the deployment of machine learning pipelines for large scale enterprise applications. His areas of expertise include enablement of enterprise applications for Data Sciences, Devops implementation and Infrastructure automation.
Chandim has empowered Data sciences team at HighRadius by building a strong foundation in Data Sciences and Software Engineering in the company and by mentoring his peers. He has played a key role in designing and developing machine learning frameworks for deployments and monitoring by adapting the enterprise legacy systems at HighRadius and pushing towards modern cutting-edge technologies. He is an expert in designing and developing enterprise applications and micro services especially for Data Sciences and Machine Learning implementations at scale.
Ms. Samadrita Ghosh
Ms. Samadrita Ghosh works as a data scientist in a key role solving various machine learning problems in the FinTech sector related to accounts receivables such as collections, deductions, cash application etc. She is a Computer Science graduate from Kalinga Institute of Industrial Technology, Bhubaneswar and has served as the key person in the development of machine learning models for various collection related problems.
She has served as a technical author in Data Science for various B2B and Educational websites and has also served as the Leading Professor's aid for PGP-AI/ML programme in BITS Pilani and has enormous experience in guiding new talent in the AI/ML sector.
Dr. Srinivas Telukunta
Dr. Srinivas Telukunta has rich experience in Product Portfolio Project management (PPM), Product Development, Analytics & Machine Learning with Big Data suited for enterprise scale deployment. He is proficient in Technology consulting with multi-disciplinary experience across several industries and domains including Semi-conductor, Pharmaceutical, Finance and IT by virtue of corporate work experience in these domains.
He has a distinguished educational background with an MS, PhD (Cornell University), MBA (Indian School of Business), B-Tech (IIT Madras), LLB (Equal to J.D), PG in Public Accounting (Bharathiar University), Specialization in Data Science from University of Michigan and more than 50+ certifications in diverse domains (Project Management, Information Technology, Cloud Automation). He has wide consulting experience with 25+ global companies including Almarai (Saudi Arabia), ADP, CYIENT, Kantar Operations, Qualcomm, Dr. Reddy's Laboratories.
ISBN: 9789352673353
Publisher: Pothi Publications
Number of Pages: 371
Dimensions: A4
Interior Pages: B&W
Binding:
Paperback (Perfect Binding)
Availability:
In Stock (Print on Demand)