Practical Machine Learning On Databricks

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Practical Machine Learning on Databricks

Author : Debu Sinha
Publisher : Packt Publishing Ltd
ISBN 13 : 1801818290
Total Pages : 244 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Practical Machine Learning on Databricks by : Debu Sinha

Download or read book Practical Machine Learning on Databricks written by Debu Sinha and published by Packt Publishing Ltd. This book was released on 2023-11-24 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Building the Data Lakehouse

Author : Bill Inmon
Publisher : Technics Publications
ISBN 13 : 9781634629669
Total Pages : 256 pages
Book Rating : 4.6/5 (296 download)

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Book Synopsis Building the Data Lakehouse by : Bill Inmon

Download or read book Building the Data Lakehouse written by Bill Inmon and published by Technics Publications. This book was released on 2021-10 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.

Spark: The Definitive Guide

Author : Bill Chambers
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912294
Total Pages : 594 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Spark: The Definitive Guide by : Bill Chambers

Download or read book Spark: The Definitive Guide written by Bill Chambers and published by "O'Reilly Media, Inc.". This book was released on 2018-02-08 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

Practical Automated Machine Learning on Azure

Author : Deepak Mukunthu
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492055549
Total Pages : 190 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Automated Machine Learning on Azure by : Deepak Mukunthu

Download or read book Practical Automated Machine Learning on Azure written by Deepak Mukunthu and published by "O'Reilly Media, Inc.". This book was released on 2019-09-23 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

Machine Learning Using R

Author : Karthik Ramasubramanian
Publisher : Apress
ISBN 13 : 1484223349
Total Pages : 580 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning Using R by : Karthik Ramasubramanian

Download or read book Machine Learning Using R written by Karthik Ramasubramanian and published by Apress. This book was released on 2016-12-22 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download. This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.. What You'll Learn Use the model building process flow Apply theoretical aspects of machine learning Review industry-based cae studies Understand ML algorithms using R Build machine learning models using Apache Hadoop and Spark Who This Book is For Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

Practical Deep Learning at Scale with MLflow

Author : Yong Liu
Publisher : Packt Publishing Ltd
ISBN 13 : 1803242221
Total Pages : 288 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Practical Deep Learning at Scale with MLflow by : Yong Liu

Download or read book Practical Deep Learning at Scale with MLflow written by Yong Liu and published by Packt Publishing Ltd. This book was released on 2022-07-08 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow Key Features • Focus on deep learning models and MLflow to develop practical business AI solutions at scale • Ship deep learning pipelines from experimentation to production with provenance tracking • Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility Book Description The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework. What you will learn • Understand MLOps and deep learning life cycle development • Track deep learning models, code, data, parameters, and metrics • Build, deploy, and run deep learning model pipelines anywhere • Run hyperparameter optimization at scale to tune deep learning models • Build production-grade multi-step deep learning inference pipelines • Implement scalable deep learning explainability as a service • Deploy deep learning batch and streaming inference services • Ship practical NLP solutions from experimentation to production Who this book is for This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.

Pragmatic AI

Author : Noah Gift
Publisher : Addison-Wesley Professional
ISBN 13 : 0134863917
Total Pages : 720 pages
Book Rating : 4.1/5 (348 download)

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Book Synopsis Pragmatic AI by : Noah Gift

Download or read book Pragmatic AI written by Noah Gift and published by Addison-Wesley Professional. This book was released on 2018-07-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Machine Learning Engineering with MLflow

Author : Natu Lauchande
Publisher : Packt Publishing Ltd
ISBN 13 : 1800561695
Total Pages : 249 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Machine Learning Engineering with MLflow by : Natu Lauchande

Download or read book Machine Learning Engineering with MLflow written by Natu Lauchande and published by Packt Publishing Ltd. This book was released on 2021-08-27 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.

Practical Machine Learning with Spark

Author : Gourav Gupta
Publisher : BPB Publications
ISBN 13 : 9391392083
Total Pages : 501 pages
Book Rating : 4.3/5 (913 download)

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Book Synopsis Practical Machine Learning with Spark by : Gourav Gupta

Download or read book Practical Machine Learning with Spark written by Gourav Gupta and published by BPB Publications. This book was released on 2022-04-28 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the cosmic secrets of Distributed Processing for Deep Learning applications KEY FEATURES ● In-depth practical demonstration of ML/DL concepts using Distributed Framework. ● Covers graphical illustrations and visual explanations for ML/DL pipelines. ● Includes live codebase for each of NLP, computer vision and machine learning applications. DESCRIPTION This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark. The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes. Towards the end of the book, readers will learn about several deep learning frameworks, such as TensorFlow and PyTorch. Readers will also learn to execute distributed processing of deep learning problems using the Spark programming language WHAT YOU WILL LEARN ●Learn how to get started with machine learning projects using Spark. ● Witness how to use Spark MLib's design for machine learning and deep learning operations. ● Use Spark in tasks involving NLP, unsupervised learning, and computer vision. ● Experiment with Spark in a cloud environment and with AI pipeline workflows. ● Run deep learning applications on a distributed network. WHO THIS BOOK IS FOR This book is valuable for data engineers, machine learning engineers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Apache Spark Environment Setup and Configuration 3. Apache Spark 4. Apache Spark MLlib 5. Supervised Learning with Spark 6. Un-Supervised Learning with Apache Spark 7. Natural Language Processing with Apache Spark 8. Recommendation Engine with Distributed Framework 9. Deep Learning with Spark 10. Computer Vision with Apache Spark

Learning Spark

Author : Jules S. Damji
Publisher : O'Reilly Media
ISBN 13 : 1492050016
Total Pages : 400 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Learning Spark by : Jules S. Damji

Download or read book Learning Spark written by Jules S. Damji and published by O'Reilly Media. This book was released on 2020-07-16 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1835089534
Total Pages : 374 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2024-01-30 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.

Active Machine Learning with Python

Author : Margaux Masson-Forsythe
Publisher : Packt Publishing Ltd
ISBN 13 : 1835462685
Total Pages : 176 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Active Machine Learning with Python by : Margaux Masson-Forsythe

Download or read book Active Machine Learning with Python written by Margaux Masson-Forsythe and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fields Key Features Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs Gain profound insights within your data while achieving greater efficiency and speed Apply your knowledge to real-world use cases and solve complex ML problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionBuilding accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.What you will learn Master the fundamentals of active machine learning Understand query strategies for optimal model training with minimal data Tackle class imbalance, concept drift, and other data challenges Evaluate and analyze active learning model performance Integrate active learning libraries into workflows effectively Optimize workflows for human labelers Explore the finest active learning tools available today Who this book is for Ideal for data scientists and ML engineers aiming to maximize model performance while minimizing costly data labeling, this book is your guide to optimizing ML workflows and prioritizing quality over quantity. Whether you’re a technical practitioner or team lead, you’ll benefit from the proven methods presented in this book to slash data requirements and iterate faster. Basic Python proficiency and familiarity with machine learning concepts such as datasets and convolutional neural networks is all you need to get started.

Data-Centric Machine Learning with Python

Author : Jonas Christensen
Publisher : Packt Publishing Ltd
ISBN 13 : 1804612413
Total Pages : 378 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Data-Centric Machine Learning with Python by : Jonas Christensen

Download or read book Data-Centric Machine Learning with Python written by Jonas Christensen and published by Packt Publishing Ltd. This book was released on 2024-02-29 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.

Machine Learning Infrastructure and Best Practices for Software Engineers

Author : Miroslaw Staron
Publisher : Packt Publishing Ltd
ISBN 13 : 183763694X
Total Pages : 346 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Machine Learning Infrastructure and Best Practices for Software Engineers by : Miroslaw Staron

Download or read book Machine Learning Infrastructure and Best Practices for Software Engineers written by Miroslaw Staron and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.

Beginning Apache Spark Using Azure Databricks

Author : Robert Ilijason
Publisher : Apress
ISBN 13 : 1484257812
Total Pages : 281 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Beginning Apache Spark Using Azure Databricks by : Robert Ilijason

Download or read book Beginning Apache Spark Using Azure Databricks written by Robert Ilijason and published by Apress. This book was released on 2020-06-11 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloudGet started with Databricks using SQL and Python in either Microsoft Azure or AWSUnderstand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.

Practical Guide to Applied Conformal Prediction in Python

Author : Valery Manokhin
Publisher : Packt Publishing Ltd
ISBN 13 : 1805120913
Total Pages : 240 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Practical Guide to Applied Conformal Prediction in Python by : Valery Manokhin

Download or read book Practical Guide to Applied Conformal Prediction in Python written by Valery Manokhin and published by Packt Publishing Ltd. This book was released on 2023-12-20 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting. Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications Explore cutting-edge methods to measure and manage uncertainty in industry applications Understand how Conformal Prediction differs from traditional machine learning Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.

Data Stewardship in Action

Author : Pui Shing Lee
Publisher : Packt Publishing Ltd
ISBN 13 : 1837638128
Total Pages : 272 pages
Book Rating : 4.8/5 (376 download)

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Book Synopsis Data Stewardship in Action by : Pui Shing Lee

Download or read book Data Stewardship in Action written by Pui Shing Lee and published by Packt Publishing Ltd. This book was released on 2024-02-16 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your organization's data maturity to the next level by operationalizing data governance Key Features Develop the mindset and skills essential for successful data stewardship Apply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardship Follow a step-by-step program to develop a data operating model and implement data stewardship effectively Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement—it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency. From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You’ll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You’ll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you’ll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship. By the end of this book, you’ll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learn Enhance your job prospects by understanding the data stewardship field, roles, and responsibilities Discover how to develop a data strategy and translate it into a functional data operating model Develop an effective and efficient data stewardship program Gain practical experience of establishing a data stewardship initiative Implement purposeful governance with measurable ROI Prioritize data use cases with the value and effort matrix Who this book is for This book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started.