Deploy Machine Learning Models To Production

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Deploy Machine Learning Models to Production

Pramod Singh
Deploy Machine Learning Models to Production Book PDF

Author : Pramod Singh
Publisher : Apress
Page : 150 pages
File Size : 48,9 Mb
Release : 2020-12-15
Category : Computers
ISBN : 1484265459

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Deploy Machine Learning Models to Production by Pramod Singh Pdf

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers

Deploy Machine Learning Models to Production

Pramod Singh
Deploy Machine Learning Models to Production Book PDF

Author : Pramod Singh
Publisher : Unknown
Page : 161 pages
File Size : 41,5 Mb
Release : 2021
Category : Computer programming
ISBN : 1484265475

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Deploy Machine Learning Models to Production by Pramod Singh Pdf

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. You will: Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks.

Building Machine Learning Pipelines

Hannes Hapke,Catherine Nelson
Building Machine Learning Pipelines Book PDF

Author : Hannes Hapke,Catherine Nelson
Publisher : "O'Reilly Media, Inc."
Page : 398 pages
File Size : 54,7 Mb
Release : 2020-07-13
Category : Computers
ISBN : 9781492053149

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Building Machine Learning Pipelines by Hannes Hapke,Catherine Nelson Pdf

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Learn Amazon SageMaker

Julien Simon
Learn Amazon SageMaker Book PDF

Author : Julien Simon
Publisher : Packt Publishing Ltd
Page : 490 pages
File Size : 43,8 Mb
Release : 2020-08-27
Category : Computers
ISBN : 9781800203594

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Learn Amazon SageMaker by Julien Simon Pdf

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Become well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and NLP models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

Introducing MLOps

Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann
Introducing MLOps Book PDF

Author : Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann
Publisher : "O'Reilly Media, Inc."
Page : 171 pages
File Size : 46,9 Mb
Release : 2020-11-30
Category : Computers
ISBN : 9781098116422

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Introducing MLOps by Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann Pdf

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Practical Machine Learning with AWS

Himanshu Singh
Practical Machine Learning with AWS Book PDF

Author : Himanshu Singh
Publisher : Apress
Page : 530 pages
File Size : 44,9 Mb
Release : 2021-02-15
Category : Computers
ISBN : 1484262212

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Practical Machine Learning with AWS by Himanshu Singh Pdf

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning—Specialty certification exam. What You Will Learn Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS Who This Book Is For Machine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification

Keras to Kubernetes

Dattaraj Rao
Keras to Kubernetes Book PDF

Author : Dattaraj Rao
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 41,7 Mb
Release : 2019-05-07
Category : Computers
ISBN : 9781119564836

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Keras to Kubernetes by Dattaraj Rao Pdf

Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, were seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. em style="box-sizing: border-box;"Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms. • Find hands-on learning examples • Learn to uses Keras and Kubernetes to deploy Machine Learning models • Discover new ways to collect and manage your image and text data with Machine Learning • Reuse examples as-is to deploy your models • Understand the ML model development lifecycle and deployment to production If youre ready to learn about one of the most popular DL frameworks and build production applications with it, youve come to the right place!

Building Machine Learning Powered Applications

Emmanuel Ameisen
Building Machine Learning Powered Applications Book PDF

Author : Emmanuel Ameisen
Publisher : "O'Reilly Media, Inc."
Page : 267 pages
File Size : 40,8 Mb
Release : 2020-01-21
Category : Computers
ISBN : 9781492045069

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Building Machine Learning Powered Applications by Emmanuel Ameisen Pdf

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 building 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. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. 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 quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment

Pragmatic Machine Learning with Python

Avishek Nag
Pragmatic Machine Learning with Python Book PDF

Author : Avishek Nag
Publisher : BPB Publications
Page : 365 pages
File Size : 48,8 Mb
Release : 2020-04-30
Category : Computers
ISBN : 9789389845365

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Pragmatic Machine Learning with Python by Avishek Nag Pdf

An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations KEY FEATURESÊ - A balanced combination of underlying mathematical theories & practical examples with Python code - Coverage of latest topics like multi-label classification, Text Mining, Doc2Vec, Word2Vec, XMeans clustering, unsupervised outlier detection, techniques to deploy ML models in production-grade systemsÊ with PMML, etc - Coverage of sufficient & relevant visualization techniques specific to any topic DESCRIPTIONÊ This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on Ôscikit-learn,Õ but other Python libraries like ÔGensimÕ or ÔPyTorchÕ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models.Ê WHAT WILL YOU LEARNÊ - Get familiar with practical concepts of Machine Learning from ground zero - Learn how to deploy Machine Learning models in production - Understand how to do ÒData Science StorytellingÓÊ - Explore the latest topics in the current industry about Machine Learning WHO THIS BOOK IS FORÊÊ This book would be ideal for experienced Software Professionals who are trying to get into the field of Machine Learning. Anyone who wishes to Learn Machine Learning concepts and models in the production lifecycle. TABLE OF CONTENTS 1. Introduction to Machine Learning & Mathematical preliminaries 2. Classification 3. Regression 4. Clustering 5. Deep Learning & Neural Networks 6. Miscellaneous Unsupervised Learning 7. Text Mining 8. Machine Learning models in production 9. Case Studies & Data Science Storytelling

Deep Learning for Coders with fastai and PyTorch

Jeremy Howard,Sylvain Gugger
Deep Learning for Coders with fastai and PyTorch Book PDF

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 47,8 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

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Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Machine Learning Model Serving Patterns and Best Practices

Md Johirul Islam
Machine Learning Model Serving Patterns and Best Practices Book PDF

Author : Md Johirul Islam
Publisher : Packt Publishing Ltd
Page : 336 pages
File Size : 54,8 Mb
Release : 2022-12-30
Category : Computers
ISBN : 9781803242538

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Machine Learning Model Serving Patterns and Best Practices by Md Johirul Islam Pdf

Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key FeaturesLearn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook Description Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model. This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples. By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques. What you will learnExplore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is for This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.

Engineering MLOps

Emmanuel Raj
Engineering MLOps Book PDF

Author : Emmanuel Raj
Publisher : Packt Publishing Ltd
Page : 370 pages
File Size : 50,8 Mb
Release : 2021-04-19
Category : Computers
ISBN : 9781800566323

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Engineering MLOps by Emmanuel Raj Pdf

Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.

Kubeflow for Machine Learning

Trevor Grant,Holden Karau,Boris Lublinsky,Richard Liu,Ilan Filonenko
Kubeflow for Machine Learning Book PDF

Author : Trevor Grant,Holden Karau,Boris Lublinsky,Richard Liu,Ilan Filonenko
Publisher : "O'Reilly Media, Inc."
Page : 264 pages
File Size : 42,8 Mb
Release : 2020-10-13
Category : Computers
ISBN : 9781492050070

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Kubeflow for Machine Learning by Trevor Grant,Holden Karau,Boris Lublinsky,Richard Liu,Ilan Filonenko Pdf

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production

Learn TensorFlow 2.0

Pramod Singh,Avinash Manure
Learn TensorFlow 2 0 Book PDF

Author : Pramod Singh,Avinash Manure
Publisher : Apress
Page : 177 pages
File Size : 45,5 Mb
Release : 2019-12-17
Category : Computers
ISBN : 9781484255582

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Learn TensorFlow 2.0 by Pramod Singh,Avinash Manure Pdf

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.

Machine Learning Bookcamp

Alexey Grigorev
Machine Learning Bookcamp Book PDF

Author : Alexey Grigorev
Publisher : Simon and Schuster
Page : 470 pages
File Size : 40,9 Mb
Release : 2021-11-23
Category : Computers
ISBN : 9781638351054

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Machine Learning Bookcamp by Alexey Grigorev Pdf

Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills! What's inside Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Deploy ML models to a production-ready environment About the reader Python programming skills assumed. No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow