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Databricks Ml In Action

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Author : Stephanie Rivera
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-17

Databricks Ml In Action written by Stephanie Rivera and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-17 with Computers categories.

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. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, 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.

Data Mesh In Action

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Author : Jacek Majchrzak
language : en
Publisher: Simon and Schuster
Release Date : 2023-03-21

Data Mesh In Action written by Jacek Majchrzak and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Computers categories.

Revolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size. In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both small startups and large enterprises. You won’t need any new technology—this book shows you how to start implementing a data mesh with flexible processes and organizational change. You’ll explore both an extended case study and multiple real-world examples. As you go, you’ll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You’ll start by building a minimum viable data product, which you’ll expand into a self-service data platform, chapter-by-chapter. You’ll love the book’s unique “sliders” that adjust the mesh to meet your specific needs. You’ll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the reader For data professionals. Requires no specific programming stack or data platform. About the author Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Table of Contents PART 1 FOUNDATIONS 1 The what and why of the data mesh 2 Is a data mesh right for you? 3 Kickstart your data mesh MVP in a month PART 2 THE FOUR PRINCIPLES IN PRACTICE 4 Domain ownership 5 Data as a product 6 Federated computational governance 7 The self-serve data platform PART 3 INFRASTRUCTURE AND TECHNICAL ARCHITECTURE 8 Comparing self-serve data platforms 9 Solution architecture design

Machine Learning Engineering In Action

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Author : Ben Wilson
language : en
Publisher: Simon and Schuster
Release Date : 2022-05-17

Machine Learning Engineering In Action written by Ben Wilson and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-17 with Computers categories.

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow

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Author : Manoj Kumar
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-30

Mastering Data Engineering And Analytics With Databricks A Hands On Guide To Build Scalable Pipelines Using Databricks Delta Lake And Mlflow written by Manoj Kumar and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.

Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges Key Features● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow.● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action.● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines.● Offers proven strategies to optimize workflows and avoid common pitfalls. Book DescriptionIn today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. What you will learn● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.● Build and deploy real-time data processing solutions for timely and actionable insights.● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. Table of ContentsSECTION 11. Introducing Data Engineering with Databricks2. Setting Up a Databricks Environment for Data Engineering3. Working with Databricks Utilities and ClustersSECTION 24. Extracting and Loading Data Using Databricks5. Transforming Data with Databricks6. Handling Streaming Data with Databricks7. Creating Delta Live Tables8. Data Partitioning and Shuffling9. Performance Tuning and Best Practices10. Workflow Management11. Databricks SQL Warehouse12. Data Storage and Unity Catalog13. Monitoring Databricks Clusters and Jobs14. Production Deployment Strategies15. Maintaining Data Pipelines in Production16. Managing Data Security and Governance17. Real-World Data Engineering Use Cases with Databricks18. AI and ML Essentials19. Integrating Databricks with External Tools Index

Practical Machine Learning On Databricks

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Author : Debu Sinha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-11-24

Practical Machine Learning On Databricks written by Debu Sinha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-24 with Computers categories.

Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.

Mastering Data Engineering And Analytics With Databricks

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Author : Manoj Kumar
language : en
Publisher: Sextil Online LLC
Release Date : 2024-09-30

Mastering Data Engineering And Analytics With Databricks written by Manoj Kumar and has been published by Sextil Online LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.

Databricks Certified Machine Learning Associate Certification Practice 300 Questions Answer

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Author : Rashmi Shah
language : en
Publisher: QuickTechie.com | A career growth machine
Release Date :

Databricks Certified Machine Learning Associate Certification Practice 300 Questions Answer written by Rashmi Shah and has been published by QuickTechie.com | A career growth machine this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

This book serves as a comprehensive guide for individuals preparing for the Databricks Certified Machine Learning Associate certification exam. It is meticulously designed to cover the entire scope of the examination, which assesses an individual's proficiency in leveraging Databricks for fundamental machine learning tasks. The certification validates the ability to understand and effectively utilize Databricks' machine learning capabilities, including advanced features like AutoML, Unity Catalog, and select functionalities of MLflow. Furthermore, it evaluates skills in data exploration, feature engineering, model building (encompassing training, tuning, and evaluation), model selection, and the crucial aspect of deploying machine learning models. Passing this certification signifies an individual's capability to execute basic machine learning tasks proficiently using Databricks and its integrated toolset. The examination's content is structured across key domains, with specific weightages: Databricks Machine Learning: 38% ML Workflows: 19% Model Development: 31% Model Deployment: 12% A detailed breakdown of the exam outline, which this book thoroughly addresses, includes: Section 1: Databricks Machine Learning This section delves into the core aspects of MLOps strategies, emphasizing best practices and the advantages of using ML runtimes. It covers how AutoML facilitates model and feature selection, highlighting its benefits in the model development process. A significant focus is placed on Unity Catalog, including the advantages of creating account-level feature store tables versus workspace-level, the practical steps to create and write data to a feature store table, and how to train and score models using features from these tables. The differences between online and offline feature tables are also explored. MLflow's role is extensively covered, from identifying the best run using the MLflow Client API and manually logging metrics, artifacts, and models, to understanding the MLflow UI. The book details model registration in the Unity Catalog registry via the MLflow Client API, contrasting its benefits with the workspace registry. It also addresses scenarios for promoting code versus models and managing model versions through tags and aliases (e.g., promoting a challenger to a champion model). Section 2: Data Processing This part of the book focuses on essential data manipulation and preparation techniques within a Spark environment. It covers computing summary statistics on a Spark DataFrame using .summary() or dbutils data summaries, and methods for outlier removal based on standard deviation or IQR. Emphasis is placed on creating visualizations for both categorical and continuous features, and comparing feature types using appropriate methods. The book provides a comprehensive understanding of imputing missing values with mode, mean, or median, and the practical application of one-hot encoding for categorical features, including identifying appropriate scenarios for its use. It also discusses the relevance and application of log scale transformation. Section 3: Model Development This section guides the reader through the intricacies of model building. It covers selecting appropriate algorithms based on ML foundations for given scenarios and methods to mitigate data imbalance in training data. The book differentiates between estimators and transformers and provides guidance on developing robust training pipelines. Hyperparameter tuning is a key focus, detailing the use of Hyperopt's fmin operation, and exploring random, grid, or Bayesian search methods. It also addresses parallelizing single-node models for hyperparameter tuning. The benefits and downsides of cross-validation versus train-validation splits are discussed, along with practical application of cross-validation in model fitting and understanding the number of models trained during grid-search and cross-validation. The book extensively covers common classification metrics (F1, Log Loss, ROC/AUC) and regression metrics (RMSE, MAE, R-squared), guiding the reader in choosing the most appropriate metric for specific objectives. Finally, it addresses the need to exponentiate log-transformed variables before evaluation and interpreting predictions, and assessing the impact of model complexity and the bias-variance tradeoff on model performance. Section 4: Model Deployment The final section of the book is dedicated to deploying machine learning models. It differentiates between and highlights the advantages of various model serving approaches: batch, real-time, and streaming. Practical steps for deploying a custom model to a model endpoint are provided. The book covers using pandas for performing batch inference and explains how streaming inference is achieved with Delta Live Tables. It also details deploying and querying a model for real-time inference and splitting data between endpoints for real-time interference. Assessment Details: The Databricks Certified Machine Learning Associate exam is a proctored certification consisting of 48 multiple-choice questions. Candidates are allotted 90 minutes to complete the exam. The registration fee is $200. No test aids are permitted during the examination. The exam is available in English, Japanese, Brazilian Portuguese, and Korean, and is delivered via online proctoring. Prerequisites and Recommendations: While there are no formal prerequisites for taking the exam, related training is highly recommended. QuickTechie.com offers valuable resources and insights that can aid in preparing for this certification, ensuring a solid understanding of the concepts. A recommended experience level of 6+ months of hands-on experience performing the machine learning tasks outlined in the exam guide is suggested for optimal preparation. Validity and Recertification: The certification has a validity period of two years. To maintain certified status, recertification is required every two years by taking the current version of the exam. QuickTechie.com can be a useful reference for staying updated on the latest exam versions and preparation strategies for recertification. Unscored Content: It is important to note that the exam may include unscored items. These items are included to gather statistical information for future use and are not identified during the exam. They do not impact the candidate's score, and additional time is factored into the exam duration to account for their presence.

Mlops With Databricks

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Author : Maria Vechtomova
language : en
Publisher: O'Reilly Media
Release Date : 2026-08-04

Mlops With Databricks written by Maria Vechtomova and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-08-04 with Computers categories.

MLOps engineers have to deal with a glut of tools and SaaS applications, not to mention technical debt clogging the system. Such complexity requires a comprehensive approach. The Databricks Platform provides all the critical components for end-to-end MLOps and LLMOps in one place. This exhaustive book shows you how to use Databricks to build and manage a robust ML system that delivers on your business's needs. Maria Vechtomova guides you through MLOps principles and explains how Databricks handles the machine learning lifecycle holistically, from data preparation to model deployment and monitoring, and enables data engineers, data scientists, and MLOps engineers to collaborate seamlessly. To put all the pieces together, you'll navigate two ML projects: a real-time ML application and a RAG system that highlights LLM-specific Databricks features. Understand the Databricks components for MLOps Unpack ML model serving architectures Track your machine learning experiments and register your models Build an ML application that uses feature and model serving, and model serving with automatic feature lookup Deploy a real-time ML application and a RAG application Use Databricks to monitor ML applications for data and model drift

The Data Lakehouse Revolution

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Author : Rajaniesh Kaushikk
language : en
Publisher: Springer Nature
Release Date : 2025-12-02

The Data Lakehouse Revolution written by Rajaniesh Kaushikk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-02 with Computers categories.

We are racing toward a new kind of AI—faster, smarter, and more connected than ever. At the heart of it is the Data Lakehouse, and Databricks is the engine powering the transformation. Whether you're a data scientist training models, an engineer scaling pipelines, or an architect modernizing your stack, this book gives you what you need to stay ahead. Inside, you'll understand how to unlock the full potential of Machine Learning and Generative AI (GenAI) using Databricks—no fluff, just real tools, real strategies, and real results. From MLFlow and AutoML to Unity Catalog, Retrieval Augment Generation (RAG), and Vector Search, you'll get a complete blueprint for building intelligent systems that actually work in production. With step-by-step labs, industry case studies, and expert tips from someone who's lived through the entire evolution of enterprise AI, this book is your guide to mastering what's next. If you're serious regarding building AI that matters, this is where your journey begins. What You'll Learn Build full-stack ML and GenAI solutions on Databricks Train and track models with MLFlow, AutoML, and tuning strategies Secure and govern data with Unity Catalog Apply explainable, ethical AI techniques Deploy and monitor ML models in real-world pipelines Use RAG and vector search to power GenAI applications Gain confidence with hands-on labs and real enterprise use cases Who This Book Is For Azure administrators, data architects, and data engineers

Databricks Data Intelligence Platform

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Author : Nikhil Gupta
language : en
Publisher: Springer Nature
Release Date : 2024-10-12

Databricks Data Intelligence Platform written by Nikhil Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-12 with Computers categories.

This book is your comprehensive guide to building robust Generative AI solutions using the Databricks Data Intelligence Platform. Databricks is the fastest-growing data platform offering unified analytics and AI capabilities within a single governance framework, enabling organizations to streamline their data processing workflows, from ingestion to visualization. Additionally, Databricks provides features to train a high-quality large language model (LLM), whether you are looking for Retrieval-Augmented Generation (RAG) or fine-tuning. Databricks offers a scalable and efficient solution for processing large volumes of both structured and unstructured data, facilitating advanced analytics, machine learning, and real-time processing. In today's GenAI world, Databricks plays a crucial role in empowering organizations to extract value from their data effectively, driving innovation and gaining a competitive edge in the digital age. This book will not only help you master the Data Intelligence Platform but also help power your enterprise to the next level with a bespoke LLM unique to your organization. Beginning with foundational principles, the book starts with a platform overview and explores features and best practices for ingestion, transformation, and storage with Delta Lake. Advanced topics include leveraging Databricks SQL for querying and visualizing large datasets, ensuring data governance and security with Unity Catalog, and deploying machine learning and LLMs using Databricks MLflow for GenAI. Through practical examples, insights, and best practices, this book equips solution architects and data engineers with the knowledge to design and implement scalable data solutions, making it an indispensable resource for modern enterprises. Whether you are new to Databricks and trying to learn a new platform, a seasoned practitioner building data pipelines, data science models, or GenAI applications, or even an executive who wants to communicate the value of Databricks to customers, this book is for you. With its extensive feature and best practice deep dives, it also serves as an excellent reference guide if you are preparing for Databricks certification exams. What You Will Learn Foundational principles of Lakehouse architecture Key features including Unity Catalog, Databricks SQL (DBSQL), and Delta Live Tables Databricks Intelligence Platform and key functionalities Building and deploying GenAI Applications from data ingestion to model serving Databricks pricing, platform security, DBRX, and many more topics Who This Book Is For Solution architects, data engineers, data scientists, Databricks practitioners, and anyone who wants to deploy their Gen AI solutions with the Data Intelligence Platform. This is also a handbook for senior execs who need to communicate the value of Databricks to customers. People who are new to the Databricks Platform and want comprehensive insights will find the book accessible.

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Databricks Ml In Action

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