PDF DOWNLOAD Building Machine Learning Powered Applications: Going from Idea to Product

Ll I will start off by saying on a scale of 1 to 10 in data science machine learning knowledge 1 being I barely know what a linear model is and 10 being I contribute to building Machine Learning Libraries conduct research that I am around a 4 I initially bought this book because I have a decent understanding of Data Science created a few models at work and personally and was interested in ways to serve the model via webserver like flaskdjangoThe best analogy I can give about this book Is Its Like Going its like going a restaurant seeing beef stew on o improve the model until it fulfills O improve the Model Until It Fulfills Original until it fulfills original Part IV covers deployment and monitoring strategies This book will help you Define your product goal and set up a machine learning problem Build your first end to end pipeline uickly and acuire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environme.

CHARACTERS Ò eBook, PDF or Kindle ePUB ☆ Emmanuel Ameisen

I ve met a lot of people who would Say They Are Well Aware they are well aware the contents of this book and that they would have nothing to learn from reading it But it amazes me how many times I ve seen those people spin up projects and completely ignore the steps they claim to know If you re managing a team I think this should be reuired reading Building Machine Learning Powered Applications *Going From Idea To *from Idea to helps to crystalize the best practices that are all too often neglected at fast moving startups and on rapid prototyping teams This book. Learn the skills necessary to design build and deploy applications powered by machine learning ML Through the course of this hands on book you'll build an example ML driven application from initial idea to deployed product Data scientists software engineers and product managers including experienced practitioners and novices alike will learn the tools best practices and challenges involved in bu. ,
Is introductory and superficial Probably good for aspiringjunior data scientists but not very interesting for experienced practitioners This book is NOT an overly technical book The way I read it it s a book that s centered around the lessons the author Emmanuel learned during his time as a data scientistML engineer He formats these lessons in such a way that makes the book extremely easy
"to read and "
read and As a newly hired data scientist who has been charged with created the company s anomaly detection application this book will serve me we. Ilding a real world ML application step by step Author Emmanuel Ameisen an experienced data scientist who led an AI education program demonstrates practical ML concepts using code snippets illustrations screenshots and interviews with industry leaders Part I teaches you how to Plan An ML Application And Measure Success an ML application and measure success II explains how to BUILD A WORKING ML MODEL PART III DEMONSTRATES WAYS a working ML model Part III demonstrates ways .
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Building Machine Learning Powered Applications: Going from Idea to Product