Hallo Docker Learning Docker Containers By Doing Projects
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Hallo Docker: Learning Docker Containers by Doing Projects
"Hallo Docker: Learning Docker Containers by Doing Projects" is a hands-on lab book that guides readers through various Docker projects and teaches them how to work with Docker containers. The book starts by introducing the basics of Docker and containerization, and then progresses to more advanced topics such as networking and orchestration. Each chapter is focused on a specific project and includes step-by-step instructions, code examples, and explanations of the underlying concepts. Projects covered in the book include creating a web server, building a multi-container application with Docker Compose, and deploying a containerized application to a Docker Swarm. Overall, "Hallo Docker: Learning Docker Containers by Doing Projects" is a practical guide for anyone who wants to learn Docker by working on real-world projects. The hands-on approach of the book makes it easy for readers to follow along and gain practical experience with Docker containerization.
Data Science in Production
Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub.
Learn Docker - Fundamentals of Docker 18.x
Author: Gabriel N. Schenker
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-04-26
Enhance your software deployment workflow using containers Key Features ●Get up-and-running with basic to advanced concepts of Docker ●Get acquainted with concepts such as Docker containers, Docker images, orchestrators and so on. ●Practical test-based approach to learning a prominent containerization tool Book Description Docker containers have revolutionized the software supply chain in small and big enterprises. Never before has a new technology so rapidly penetrated the top 500 enterprises worldwide. Companies that embrace containers and containerize their traditional mission-critical applications have reported savings of at least 50% in total maintenance cost and a reduction of 90% (or more) of the time required to deploy new versions of those applications. Furthermore they are benefitting from increased security just by using containers as opposed to running applications outside containers. This book starts from scratch, introducing you to Docker fundamentals and setting up an environment to work with it. Then we delve into concepts such as Docker containers, Docker images, Docker Compose, and so on. We will also cover the concepts of deployment, orchestration, networking, and security. Furthermore, we explain Docker functionalities on public clouds such as AWS. By the end of this book, you will have hands-on experience working with Docker containers and orchestrators such as SwarmKit and Kubernetes. What you will learn ●Containerize your traditional or microservice-based application ●Share or ship your application as an immutable container image ●Build a Docker swarm and a Kubernetes cluster in the cloud ●Run a highly distributed application using Docker Swarm or Kubernetes ●Update or rollback a distributed application with zero downtime ●Secure your applications via encapsulation, networks, and secrets ●Know your options when deploying your containerized app into the cloud Who this book is for This book is targeted at system administrators, operations engineers, DevOps engineers, and developers or stakeholders who are interested in getting started with Docker from scratch. No prior experience with Docker Containers is required.