Combining Dataops Mlops And Devops

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. TABLE OF CONTENTS 1. Container – Containerization is the New Virtualization 2. Docker with Containers for Developing and Deploying Software 3. DevOps to Build at Scale a Culture of Collaboration, Affinity, and Tooling 4. Docker Containers for Microservices Architecture Design 5. Kubernetes – The Cluster Manager for Container 6. Data Engineering with DataOps 7. MLOps: Engineering Machine Learning Operations 8. xOps Best Practices

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.

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years, there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term, called "analytics," is an overarching description of a compilation of methodologies. But artificial intelligence (AI), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? Extending on the foundations introduced in the first book, this book illustrates how professionals in healthcare, business, and government are applying these disciplines, methods, and technologies. The goal of this book is to get leaders and practitioners to start thinking about how they may deploy techniques outside their function or industry into their domain. Application of modern technology into new areas is one of the fastest, most effective ways to improve results. By providing a rich set of examples, this book fosters creativity in the application and use of AI and analytics in innovative ways.

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on Key Features Build machine learning solutions faster than peers only using documentation Enhance or refine your expertise with tribal knowledge and concise explanations Follow along with code projects provided in GitHub to accelerate your projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform. You’ll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You’ll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources. By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.What you will learn Set up a workspace for a data team planning to perform data science Monitor data quality and detect drift Use autogenerated code for ML modeling and data exploration Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects Communicate insights through Databricks SQL dashboards and Delta Sharing Explore data and models through the Databricks marketplace Who this book is for This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.

Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

Unleash DevOps excellence with Python and its ecosystem of tools for seamless orchestration on both local and cloud platforms, such as GCP, AWS, and Azure Key Features Integrate Python into DevOps for streamlined workflows, task automation, and improved collaboration Combine the principles of Python and DevOps into a unified approach for problem solving Learn about Python’s role in Infrastructure as Code (IaC), MLOps, networking, and other domains Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPython stands out as a powerhouse in DevOps, boasting unparalleled libraries and support, which makes it the preferred programming language for problem solvers worldwide. This book will help you understand the true flexibility of Python, demonstrating how it can be integrated into incredibly useful DevOps workflows and workloads, through practical examples. You'll start by understanding the symbiotic relation between Python and DevOps philosophies and then explore the applications of Python for provisioning and manipulating VMs and other cloud resources to facilitate DevOps activities. With illustrated examples, you’ll become familiar with automating DevOps tasks and learn where and how Python can be used to enhance CI/CD pipelines. Further, the book highlights Python’s role in the Infrastructure as Code (IaC) process development, including its connections with tools like Ansible, SaltStack, and Terraform. The concluding chapters cover advanced concepts such as MLOps, DataOps, and Python’s integration with generative AI, offering a glimpse into the areas of monitoring, logging, Kubernetes, and more. By the end of this book, you’ll know how to leverage Python in your DevOps-based workloads to make your life easier and save time.What you will learn Implement DevOps practices and principles using Python Enhance your DevOps workloads with Python Create Python-based DevOps solutions to improve your workload efficiency Understand DevOps objectives and the mindset needed to achieve them Use Python to automate DevOps tasks and increase productivity Explore the concepts of DevSecOps, MLOps, DataOps, and more Use Python for containerized workloads in Docker and Kubernetes Who this book is for This book is for IT professionals venturing into DevOps, particularly programmers seeking to apply their existing programming knowledge to excel in this field. For DevOps professionals without a coding background, this book serves as a resource to enhance their understanding of development practices and communicate more effectively with developers. Solutions architects, programmers, and anyone regularly working with DevOps solutions and Python will also benefit from this hands-on guide.