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Practical Machine Learning On Databricks

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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.

Practical Machine Learning With Spark

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Author : Gourav Gupta
language : en
Publisher: BPB Publications
Release Date : 2022-04-28

Practical Machine Learning With Spark written by Gourav Gupta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-28 with Computers categories.

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

Practical Automated Machine Learning On Azure

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Author : Deepak Mukunthu
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-23

Practical Automated Machine Learning On Azure written by Deepak Mukunthu and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-23 with Computers categories.

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.

Practical Deep Learning At Scale With Mlflow

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Author : Yong Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-08

Practical Deep Learning At Scale With Mlflow written by Yong Liu 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 2022-07-08 with Computers categories.

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.

Practical Machine Learning From Pictures To The Cloud 2025

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Author : AUTHOR:1-Praneet Amul Akash Cherukuri AUTHOR:2-Dr. Santosh Kumar Henge
language : en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date :

Practical Machine Learning From Pictures To The Cloud 2025 written by AUTHOR:1-Praneet Amul Akash Cherukuri AUTHOR:2-Dr. Santosh Kumar Henge and has been published by RAVEENA PRAKASHAN OPC PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The past decade has moved machine learning from academic curiosity to an invisible utility pulsing through every photograph we snap and every swipe we make. A face unlocks a phone, a drone inspects a bridge, a doctor consults an algorithm before a diagnosis—all powered by models that see, learn, and act in real time. Yet for students and engineers stepping into the field, the journey from inquisitive “Hello-world” notebook to a production-grade model running on an edge device or a cloud endpoint can feel disjointed and opaque. Practical Machine Learning: From Pictures to the Cloud was born of that gap. In our classrooms and industry collaborations at S R University, we watched learners master isolated concepts—convolutional layers, hyper-parameter tuning, REST APIs—without a blueprint that tied them together. This book offers that blueprint. We start with raw pixels, guide you through feature engineering and modern deep-learning architectures, and then scale the conversation outward: how to train responsibly, deploy at cloud scale, monitor for drift, and govern for fairness and privacy. What makes the text “practical” is its bias toward end-to-end reproducibility. Every chapter couple’s theory with hands-on labs drawn from real engagements in health care, smart cities, retail, and autonomous systems. Code examples ship as containerised notebooks; pipeline diagrams map directly to the managed services of AWS, Google Cloud, Azure, and open-source stacks like Kubeflow and Feast. Whether your workstation is a laptop or a GPU cluster, you can follow the same lifecycle we use in production. Equally vital is the ethical lens threaded throughout. As image models grow more capable, they also magnify risks—bias, surveillance, ecological cost. You will find checklists, case studies, and policy references alongside optimisation tricks, because robustness and responsibility are no longer optional extras; they are success criteria. The book is organised in three movements: 1. Seeing – fundamentals of image data, classical vision, and modern convolutional/transformer networks. 2. Learning – advanced training techniques, transfer learning, hyper-parameter tuning, and explainability. 3. Serving – scalable pipelines, cloud deployment, edge inference, monitoring, cost governance, and compliance. Our intended audience spans senior undergraduates, graduate students, and practitioners who know basic Python and linear algebra but want to take the leap into full-stack machine-learning engineering. We owe gratitude to our students, whose incisive questions shaped the narrative, and to industry partners who opened their architectures for case studies. Any errors that remain are ours alone. We hope this book becomes your desk companion as you turn pixels into insights and models into value—responsibly, reproducibly, and at scale. Authors

Practical Automated Machine Learning On Azure

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Author : Deepak Mukunthu
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-23

Practical Automated Machine Learning On Azure written by Deepak Mukunthu 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 2019-09-23 with Computers categories.

Spark The Definitive Guide

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Author : Bill Chambers
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-02-08

Spark The Definitive Guide written by Bill Chambers and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-08 with Computers categories.

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

Data Science On Aws

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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07

Data Science On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Modern Time Series Forecasting With Python

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Author : Manu Joseph
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-24

Modern Time Series Forecasting With Python written by Manu Joseph 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 2022-11-24 with Computers categories.

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Machine Learning Engineering With Mlflow

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Author : Natu Lauchande
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-08-27

Machine Learning Engineering With Mlflow written by Natu Lauchande 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 2021-08-27 with Computers categories.

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 On Databricks

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