Combining DataOps, MLOps and DevOps: Outperform Analytics and Software Development with Expert Practices on Process Optimization and Automation
- Length: 398 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2022-05-16
- ISBN-10: 9355511914
- ISBN-13: 9789355511911
- Sales Rank: #414741 (See Top 100 Books)
Accelerate the delivery of software, data, and machine learning
Key Features
- Each chapter harmonizes the DevOps, Data Engineering, and Optimized Machine Learning cultures.
- Equips readers with AGILE skills to continuously re-prioritize production backlogs.
- Containerization, Docker, Kubernetes, DataOps, and MLOps are all rolled together.
Description
This book instructs readers on how to operationalize the creation of systems, software applications, and business information using the best practices of DevOps, DataOps, and MLOps, among other things.
From software unit packaging code and its dependencies to automating the software development lifecycle and deployment, the book provides a learning roadmap that begins with the basics and progresses to advanced topics. This book teaches you how to create a culture of cooperation, affinity, and tooling at scale using DevOps, Docker, Kubernetes, Data Engineering, and Machine Learning. Microservices design, setting up clusters and maintaining them, processing data pipelines, and automating operations with machine learning are all topics that will aid you in your career. When you use each of the xOps methods described in the book, you will notice a clear shift in your understanding of system development.
Throughout the book, you will see how every stage of software development is modernized with the most up-to-date technologies and the most effective project management approaches.
What you will learn
- Learn about the Packaging code and all its dependencies in a container.
- Utilize DevOps to automate every stage of software development.
- Learn how to create Microservices that are focused on a specific issue.
- Utilize Kubernetes to containerize applications in a variety of settings.
- Using DataOps, you can align people, processes, and technology.
Who this book is for
This book is meant for the Software Engineering team, Data Professionals, IT Operations and Application Development Team with prior knowledge in software development.
Cover Page Title Page Copyright Page About the Authors About the Reviewers Acknowledgement Preface Errata Table of Contents 1. Container – Containerization is the New Virtualization Introduction Structure Objectives Introducing containers Working of containers Virtual machines and containers Hypervisor Hosted hypervisor Bare metal hypervisor Virtual machine Container OCI container Container runtime Container-as-a-Service Container host Container in J2EE Applications based on container Comparing container and virtual machine Benefits of container Docker and the rise of MicroServices Monolithic architecture Drawbacks “Microservices” come to the rescue! Technological evolution Docker Docker advantages Designing a microservices architecture with Docker containers Microservices: what are they? Building a microservice architecture – the challenges Microservices rescued by Docker Patterns to enable your architecture Moving to microservices cgroups and namespaces in Linux Containerization and its benefits Conclusion Key terms Questions 2. Docker with Containers for Developing and Deploying Software Introduction Structure Objectives Docker and its use Is Docker a VM? The standards of a container and its industry leadership rancher Swarm Amazon ECS Build tool Engine Client Daemon File Image Container and container image—the differences The Union File System Volumes Containers in Docker Container implementation Namespaces Explaining Control Groups Docker application programming interface Docker’s purpose Client in Docker Remote API in Docker DevOps Docker Benefits of Docker How do these benefits benefit the users? What has Docker done in helping create this foundation? Formation of OCI and evolution of Docker’s role Installation of Docker on Linux Docker version Docker support for Windows The ToolBox for Docker Docker support for Windows The ToolBox Setup for Docker Working with the Toolbox for Docker Hub Images Displaying the images Images downloading in Docker Removal of images The Docker inspect command Containers in Docker Running a container Container listing Working with containers in Docker Container lifecycle in Docker The architecture of Docker Conclusion Key terms Questions 3. DevOps to Build at Scale a Culture of Collaboration, Affinity, and Tooling Introduction Structure Objectives DevOps essentials Benefits of DevOps Introducing DevOps Development and operations, agile and DevOps, and waterfall model Dev and Ops Agile for DevOps Software development with waterfall model The challenges of the waterfall model Tools of DevOps Lifecycle of DevOps Principles of DevOps Roles, responsibilities, and skills of a DevOps Engineer Evolution of the DevOps methodology Understanding DevOps—the Key The position of a DevOps engineer DevSecOps Future scope of DevOps Database DevOps Conclusion Key terms Questions 4. Docker Containers for Microservices Architecture Design Introduction Structure Objectives Introducing microservices Defining microservices Optimized for DevOps and CI/CD microservices is the way to build apps Migrating to microservices architecture: the reasons Microservices benefits Microservices with Docker Benefits of Docker for microservices Comparing Docker and virtual machines How are virtual machines beaten by Docker? Architecture of Docker Docker client Docker daemon Docker registry Images in Docker Volumes in Docker Registries or repositories of images Use Docker to extend the architecture of a microservices-based app Using container Orchestration systems for managing Docker-based apps Service mesh Working of service mesh Optimizing communication with service mesh Future plan Service mesh role Service mesh load balancing Service discovery in a service mesh Proxy sidecar Service mesh monitoring Tooling for service mesh A case study of Pik-n-Pay grocer for microservices architecture Introduction of Pik-n-Pay grocer The scenario of business Solution based on microservices architecture Process of order-delivery Decomposition of receive order sub-process PnP framework for MSA governance PnP’s microservices reference architecture Microservice for account login Microservice for receive order Provision of an estimate microservice Microservice for confirm order Results of PnP’s choice A taxi app based on microservices An application of shopping cart: a use-case Architecture of microservices Features of microservices Microservices advantages Designing microservices: the best practices Microservices examples Java microservices Docker microservices Cloud microservices Microservices architecture: a hands-on Configuration and setup of the system Architecture of application Conclusion Key terms Questions 5. Kubernetes –The Cluster Manager for Container Introduction Structure Objectives Introducing Kubernetes An era of traditional/virtualized/container deployment What Kubernetes is NOT? Architecture of Kubernetes Components master server The etcd Kube-apiserver Kube-controller-manager Kube-scheduler Cloud-controller-manager Components node server A container runtime Kubelet Kube-proxy Service with selector Node controller Objects and workloads of Kubernetes Pods Pod types Pod single container Pod multi-container Replication controllers and sets Deployments Deployment change Strategies of deployment Deployment creation Stateful sets Daemon set Jobs and cron jobs Other components of Kubernetes Services Service types Volumes and persistent volumes Kubernetes volume types Persistent volume and persistent volume claim Labels and annotations Secrets Policy of network The need for Kubernetes and what it does? Components of Kubernetes Components of control plane Kube-apiserver The etcd Kube-scheduler Kube-controller-manager Cloud-controller-manager Components of node Kubelet Kube-proxy Runtime of container Add-ons DNS Web UI (dashboard) Monitoring of container resource Logging cluster-level An app creation By downloading From Docker file Deployment of app Autoscaling Dashboard setup Monitoring Sematext Docker agent Why does Kubernetes matter to you? Rebel foods case study Conclusion Keywords Questions 6. Data Engineering with DataOps Introduction Structure Objectives Introducing DataOps DataOps practice goals DataOps definition(s) DataOps need People and processes Technology Getting started with DataOps Make people a priority Manage the processes, take on the tools DataOps case building Get going! DataOps adoption: lessons for leaders Data architecture of DataOps DataOps architecture breakup Data architecture of multi-location DataOps DataOps built into an existing data architecture Principles of DataOps DataOps maturity model Benefits and barriers of DataOps maturity Data analytics DataOps Key business benefits of DataOps DataOps implementation Intellectual heritage of DataOps DevOps and DataOps—the human factor DevOps and DataOps—process differences DevOps and DataOps—development and deployment processes Duality of Orchestration in DataOps Duality of testing in DataOps The complexity of sandbox management in DataOps The complexity of test data management in DataOps Connecting the organization in two ways using DataOps Comparing freedom and centralization An enterprise example of the data analytics lifecycle complexity Implementation of DataOps Conclusion Keywords Questions 7. MLOps—Engineering Machine Learning Operations Introduction Structure Objectives Introducing Machine Learning Operations (MLOps) Background of MLOps MLOps—what it is? Continuous integration/delivery/training CI, CD, and CT Comparing DevOps and MLOps Machine learning life cycle of MLOps—the context Advantages of MLOps MLOps phases Pipelines Data pipeline ML pipeline Process framework of MLOps The MLOps vision and its delivery Customer Churn as an MLOps scenario The data environment and configuration of the model Creating pipelines for training and inference MLOps maturity model Delivering on MLOps maturity Level 0 Level 1 Level 2 Level 3 Level 4 Key challenges that MLOps addresses MLOps best practices Use cases in the real world TransLink Microsoft office Johnson controls Conclusion Keywords Questions 8. xOps Best Practices Introduction Structure Objectives Best practices of DevOps Active participation of stakeholders Testing to be automated Configuration management to be integrated Change management to be integrated Integration in a continuous way Deployment planning to be integrated Deployment in a continuous way Support for production Monitoring of application Dashboards that are automated It is a culture that DevOps is about? The choice of the right DevOps tools The tools picking DevOps tools—major categories Complexity unwinding How to set up a CI/CD pipeline Hands-on: building a CI/CD pipeline using Docker and Jenkins DevOps for quantum computing Hybrid Quantum applications—the DevOps practices Continuous integration (CI) Automated testing Continuous delivery (CD) Application monitoring The outer DevOps loop The inner DevOps loop Best practices of DataOps For DataOps the best of data management practices DevOps and DataOps: how advantageous it is to implement? Best practices of DevOps and DataOps Best practices of MLOps The need for MLOps The best MLOps tools Data and pipeline versioning Run Orchestration Experiment tracking and organization Hyperparameter tuning Model serving Production model monitoring Live projects Project # 1: a website Project # 2: create and run a CI/CD pipeline for an application Project # 3: deploy an application (with high availability) with a database Project # 4: create a monitoring dashboard for an application Project # 5: deploy a containerized application Project # 6: create an app with an API and deploy it to Kubernetes Conclusion Key terms Questions Further reading Index
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