Beginning Mlops With Mlflow
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Beginning Mlops With Mlflow
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Author : Sridhar Alla
language : en
Publisher:
Release Date : 2021
Beginning Mlops With Mlflow written by Sridhar Alla and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Cloud computing categories.
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. You will: Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud.
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.
Deploy Machine Learning Models To Production
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2020-12-15
Deploy Machine Learning Models To Production written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Computers categories.
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers
Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13
Building Machine Learning Pipelines written by Hannes Hapke 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 2020-07-13 with Computers categories.
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
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
Kubeflow For Machine Learning
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Author : Trevor Grant
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-13
Kubeflow For Machine Learning written by Trevor Grant 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 2020-10-13 with Computers categories.
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan 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 2020-10-15 with Computers categories.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Beginning Apache Spark 3
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Author : Hien Luu
language : en
Publisher: Apress
Release Date : 2021-10-23
Beginning Apache Spark 3 written by Hien Luu and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-23 with Computers categories.
Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. What You Will Learn Master the Spark unified data analytics engine and its various components Work in tandem to provide a scalable, fault tolerant and performant data processing engine Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using MLflow Who This Book Is For Data scientists, data engineers and software developers.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.