Agile Machine Learning

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Agile Artificial Intelligence In Pharo
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Author : Alexandre Bergel
language : en
Publisher: Apress
Release Date : 2020-06-20
Agile Artificial Intelligence In Pharo written by Alexandre Bergel and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-20 with Computers categories.
Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains. Along the way, you’ll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You’ll be able to take this source code and apply it to your own projects. What You Will Learn Use neurons, neural networks, learning theory, and more Work with genetic algorithms Incorporate neural network principles when working towards neuroevolution Include neural network fundamentals when building three Pharo-based applications Who This Book Is For Coders and data scientists who are experienced programmers and have at least some prior experience with AI or deep learning. They may be new to Pharo programming, but some prior experience with it would be helpful.
Agile Machine Learning
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Author : Eric Carter
language : en
Publisher: Apress
Release Date : 2019-08-21
Agile Machine Learning written by Eric Carter and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-21 with Computers categories.
Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.
Learning Agile
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Author : Andrew Stellman
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-11-12
Learning Agile written by Andrew Stellman 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 2014-11-12 with Computers categories.
Learning Agile is a comprehensive guide to the most popular agile methods, written in a light and engaging style that makes it easy for you to learn. Agile has revolutionized the way teams approach software development, but with dozens of agile methodologies to choose from, the decision to "go agile" can be tricky. This practical book helps you sort it out, first by grounding you in agile’s underlying principles, then by describing four specific—and well-used—agile methods: Scrum, extreme programming (XP), Lean, and Kanban. Each method focuses on a different area of development, but they all aim to change your team’s mindset—from individuals who simply follow a plan to a cohesive group that makes decisions together. Whether you’re considering agile for the first time, or trying it again, you’ll learn how to choose a method that best fits your team and your company. Understand the purpose behind agile’s core values and principles Learn Scrum’s emphasis on project management, self-organization, and collective commitment Focus on software design and architecture with XP practices such as test-first and pair programming Use Lean thinking to empower your team, eliminate waste, and deliver software fast Learn how Kanban’s practices help you deliver great software by managing flow Adopt agile practices and principles with an agile coach
Agile Machine Learning With Datarobot
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Author : Bipin Chadha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-12-24
Agile Machine Learning With Datarobot written by Bipin Chadha 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 2021-12-24 with Computers categories.
Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key FeaturesGet well-versed with DataRobot features using real-world examplesUse this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycleMake use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML modelsBook Description DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors. What you will learnUnderstand and solve business problems using DataRobotUse DataRobot to prepare your data and perform various data analysis tasks to start building modelsDevelop robust ML models and assess their results correctly before deploymentExplore various DataRobot functions and outputs to help you understand the models and select the one that best solves the business problemAnalyze a model's predictions and turn them into actionable insights for business usersUnderstand how DataRobot helps in governing, deploying, and maintaining ML modelsWho this book is for This book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. The book assumes a basic understanding of machine learning.
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso 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 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Agile Data Science 2 0
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Author : Russell Jurney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-07
Agile Data Science 2 0 written by Russell Jurney 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 2017-06-07 with Computers categories.
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
Machine Learning
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Author : Robert Keane
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-12-12
Machine Learning written by Robert Keane and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-12 with categories.
This Book Includes 2 Manuscripts Machine Learning Master The Three Types Of Machine Learning Machine learning is vital to the world of information technology. While many people may have no idea what machine learning is, they have probably used it sometime in their daily lives. For example, if you have ever done a search query on a search engine, you have worked with one form of machine learning. The program to do your search query has been trained to find the best results based on what you are looking for and it will also learn from the choices that you make. In this book you will find: Understanding the Basics of Machine Learning Why should I Use Machine Learning? Machine Learning Applications How Artificial Intelligence and Machine Learning are Different Statistics and Probability Theory The Building Blocks of Machine Learning Formal Statistical Learning Framework PAC Learning Strategies Generalization Models in Machine Learning Supervised Machine Learning Unsupervised Machine Learning Support Vector Machines Issues That Can Come Up In Machine Learning Agile Project Management Focus On Continuous Improvement, Scope Flexibility, Team Input, And Delivering Essential Quality Products Agile Project Management has grown in popularity over the past several years. Change is occurring so fast that many organizations are unable to keep up with the demands of a changing global world. Your ability to quickly change and adapt to your environment will make or break, not only your career but could be the deciding factor as to whether your company survives in the coming years. Those that have implemented the Agile strategies you will learn in this book are the ones that are succeeding and will be around for years to come. Look around at your peers. How many of them are looking to take that next step? The answer is probably very few but not you. You are an action taker. The fact that you are looking for a book like this says so. Here is some of what you will learn: The Benefits of Agile for you and your organization Agile strategy and making Agile work within an organization What is Scrum and how to implement it Explanation of ITIL and how it relates to Agile Tools of the trade Case Studies to show you Agile in action And an added BONUS - THE SECRET WEAPON Become An Expert TODAY! Everything You Need To Know About Machine Learning AND Agile Project Management Inside This Amazing TWO Book Bundle! Scroll Up And Click The "BUY" Button!
The Art Of Agile Development
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Author : James Shore
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2008
The Art Of Agile Development written by James Shore 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 2008 with Computers categories.
For those considering Extreme Programming, this book provides no-nonsense advice on agile planning, development, delivery, and management taken from the authors' many years of experience. While plenty of books address the what and why of agile development, very few offer the information users can apply directly.
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.
Agile 2
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Author : Cliff Berg
language : en
Publisher: John Wiley & Sons
Release Date : 2021-03-09
Agile 2 written by Cliff Berg and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-09 with Business & Economics categories.
Agile is broken. Most Agile transformations struggle. According to an Allied Market Research study, "63% of respondents stated the failure of agile implementation in their organizations." The problems with Agile start at the top of most organizations with executive leadership not getting what agile is or even knowing the difference between success and failure in agile. Agile transformation is a journey, and most of that journey consists of people learning and trying new approaches in their own work. An agile organization can make use of coaches and training to improve their chances of success. But even then, failure remains because many Agile ideas are oversimplifications or interpreted in an extreme way, and many elements essential for success are missing. Coupled with other ideas that have been dogmatically forced on teams, such as "agile team rooms", and "an overall inertia and resistance to change in the Agile community," the Agile movement is ripe for change since its birth twenty years ago. "Agile 2" represents the work of fifteen experienced Agile experts, distilled into Agile 2: The Next Iteration of Agile by seven members of the team. Agile 2 values these pairs of attributes when properly balanced: thoughtfulness and prescription; outcomes and outputs, individuals and teams; business and technical understanding; individual empowerment and good leadership; adaptability and planning. With a new set of Agile principles to take Agile forward over the next 20 years, Agile 2 is applicable beyond software and hardware to all parts of an agile organization including "Agile HR", "Agile Finance", and so on. Like the original "Agile", "Agile 2", is just a set of ideas - powerful ideas. To undertake any endeavor, a single set of ideas is not enough. But a single set of ideas can be a powerful guide.