The Business of Machine Learning

A Technical Decision Maker's Guide to Communication and Strategy
1. Auflage 2022
ISBN: 978-1-4842-3542-3
Verlag: APRESS L.P.

Buch, Englisch, 230 Seiten, Book, Format (B × H): 155 mm x 235 mm

Successfully and proactively take charge of your machine learning strategy. Machine learning (ML) is permeating every sector and aspect of business, from evaluating the success of a massive online marketing campaign, to predicting insurance payouts, to crime scene analysis. This book shows how to interpret patterns and redundancies from massive amounts of existing data to help your business cut costs, operate more efficiently and effectively, and get to the next level.  You will learn how to analyze, communicate, and launch a viable program that, when done correctly, will positively transform your business. The authors engage you to experience the business of machine learning through actual conversations that open with an exchange between a data scientist and and his counterpart in business, the technical decision maker. You will learn where to go when the conversation leads to an impasse and work step-by-step to methodically resolve the challenges. After reading this book, you will come away with the confidence to tackle a machine learning strategy customized for your team or business objectives. Revel in the vast capabilities of machine learning tools at your disposal and reach that "a ha" moment when you discover the profound and enduring impact machine learning can have on your business.  What You'll Learn Understand the vast potential of machine learning, and how and when to apply different ML techniques Devise strategies to improve efficiency and accuracy in your business Get to know your customers and their specific needs through interpreting highly accurate and complex data Communicate more effectively with teams of architects and data scientists as they develop and deploy complex machine learning solutions  Contrast the life cycle of a machine learning project to a software development project  Master terms such as “convolutional neural network,” “nonparametric regression,” and “multi-class decision jungle”Who This Book is For Any technical business decision maker who has to implement a machine learning strategy or converse with data scientists. A basic level of technical understanding is helpful, but does not have to be specific to programming languages or operating systems. This book is open access under a CC BY license.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Holmes, Josh

Lanzetta, Michael

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Microsoft Programmierung
• Mathematik | Informatik EDV | Informatik Informatik

Inhaltsverzeichnis

Chapter 1:             What is ML: Why the hype right now. (30 pages) a)      Conversation with a Machine Learning Expertb)     Where’s ML being used today? i)       Spam checking, spell check and grammarii)     Siri, search engines, music selection (Spotify…)c)      Short history of Machine learning dating back to the 1950sd)     What is AI and what’s its relationship to MLe)      Why Machine Learning is Hot Right nowi)       Storage is more accessible than everii)      Access to compute, especially GPUS, is higher than everiii)    New algorithms are being created every day iv)    New tooling making things more accessible f)       How is Machine Learning done? i)        What’s inside a model? ii)      What’s a feature? g)      How do data scientists think about feature extraction? i)        This requires domain expertise so a data scientist in the financial space, for example, wouldn't necessarily be effective in machine translation tasks or obstacle avoidance. h)      What are some of the new tools that will help folks access machine learningChapter 2:              What is DL, how does it differ, why now? (20 pages)a)      Conversation involving Deep Learningb)      Deep learning is a branch of machine learning. i)        Machines do their own feature extraction ii)      Deep learning has turned intractable problems to tractable c)      What’s made this possible at this point? i)        Compute, especially GPUs, are more accessible than ever ii)      New Math: Back propagation and Gradient Descent d)      Constructing a deep network i)        Training a model ii)      Using activation functions iii)    batch normalization. iv)    What is dropout? v)      Choosing an optimization function: SGD and Beyond vi)    Evaluating a model vii)  What is a SoftMax? Chapter 3:            Things that kind of look like AI and solve amazing problems but really aren't...  (20 pages)a)      Conversation between a programmer and an ML expert on choosing the right tool for the job b)      Why are these things not actually Machine Learning? i)        Who programmed the rules?  ii)      Deterministic verses Probabilistic results c)      Ways to solve problems that look like machine learning i)        Expert systems – rule based systems including state machines (1)   When to use an expert system (2)   Pitfalls and drawbacks of an expert system ii)      Convex optimization – set of techniques for deterministically finding the optimal resourcing (1)   When to use convex optimization (2)   Pitfalls and drawbacks to convex optimization iii)    Time-series Forecasting - using past data to predict the future, taking into account seasonal effects and short/long-term trends (1)   When to use time-series forecasting (2)   Pitfalls and drawbacks to using time-series forecasting iv)    Dynamic programming -  cleverly breaking problems down, solving the easier smaller ones, and storing their solutions (1)   When to use dynamic programming (2)   Pitfalls and drawbacks of dynamic programming Chapter 4:              What sort of problems can you /should you solve with ML? DL? (20 pages) a)      Conversation with data scientist around selecting ML tools b)      What’s ML really good at? i)       Discussion of problems where ML has helpedc)      What’s ML not good at? i)       A walk through a few problems where ML doesn't do very wellii)     Not enough dataiii)  Curse of dimensionalityd)      Recognizing a machine learning problemi)       Filtering out problems that can be solved by methods in Chapter 3 ii)     Phrasing your problem as an ML problem e)      When is an ML problem actually a Deep Learning problem? How to know when this answer has changed (things are moving fast!)Chapter 5:              ML: Dealing with Data (featurization) (20 pages) a)      Conversation with a data scientist about featurization of data. b)      How do determine what data you need to collect c)      How to store this data i)        Data store ii)      Formats d)      How a data scientist works with datai)        Data cleaningii)      Labeling iii)    Featurization e)      Potential pitfalls i)        Feature skew and Heteroskedasticity ii)      Label skew iii)    Interdependence of features iv)    Outlier detection v)      Data sparsity vi)    Missing values Chapter 6:              ML Under Supervision: Regression and Classification (20 pages) a)      The primary two methods of solving problems with classic machine learning i)        Classification turns features into a single decision such as a yes or no or into different buckets. E.G. Will it rain tomorrow? ii)      Regression turns features into numeric values. E.G. What’s the temperature likely to be tomorrow? Chapter 7:              Unsupervised and Semi-supervised ML (20 pages) a)      Conversation with a data scientist about what a computer can do without any direction b)      What unsupervised learning can do i)        Working with unlabeled data ii)      Clustering and finding patterns in data iii)    Anomaly Detection iv)    How to recognize an unsupervised learning problem c)      How semi-supervised learning can help augment supervised learning i)        Combines labeled and unlabeled data d)      Moving from unsupervised to supervised i)        Creating labels from the clusters Chapter 8:              Deep Learning: On Images (CNNs) (20 pages)a)      Conversation with a data scientist about image processingb)      Short history of image recognition i)        How featurization of an image has traditionally been done c)      How the introduction of deep learning has accelerated the field d)      How does Deep Learning work with Imagesi)        Creating image features ii)      What is a convolution iii)    what is a convolutional neural network(CNN)iv)    what do width, stride and padding mean to a CNN?  Chapter 9:              Deep Learning: On Text and Sound (RNNs) (20 pages)a)      Conversation with a data scientist about text and sound processing b)      Processing sequential data is far different than processing an image i)        Sequencing matters ii)      Looking at sequencing(1)   Letters (2)   Words(3)   Phonemec)      Recurrent Neural Networksi)        Semantic mapping through of sequential data ii)      Vanishing and exploding gradient problemsiii)    LSTMs and GRUs iv)    Attention-based methods d)      How can this be used? i)        Learn to caption imagesii)      Speak like a celebrityiii)    Find information from long documents that would otherwise remain hidden with more traditional ML methods. Chapter 10:          Deep Learning: Self-Xing Y's, or Deep Reinforcement Learning (20 pages) a)      Conversation with a data scientist about reinforcement learningb)      Reinforcement Learning i)        Short history of reinforcement learning (1)   old technique being given dramatic new life with Deep Learning. c)      Basics of Q-Learning i)        Defining intelligent Q-functions d)      High profile uses i)        defeat the best Go player in the world, ii)      drive cars iii)    fly planes on its own. Chapter 11:          The ML Process: Data Provenance, Model Versioning, Deployment, etc. (30 pages) a)      Conversation with a data scientist on the life cycle of a machine learning project. b)      Traditional Software Lifecycle verses Machine Learning Software Lifecycle i)        Accounting for the data (1)   Where the data is coming from (2)   How it’s changed over the time ii)      Versioning the model itself c)      Tracking quality of the model over timei)        Accuracy over timed)      Deployment of the modeli)        Where does the model live? (1)   Server (2)   On a device(3)   Hybridii)      Hierarchical models(1)   Stacked degrees of precision iii)    Model compression/quantization (1)   Various techniques for doing this (a)   Normalization of values (b)   Giving up degrees of precision e)      Updating the modeli)        Retrainingii)      RedeploymentChapter 12:          Advanced Topics: (20 pages)ab)      VAEs (Variational Auto Encoders), GANs (Generative Adversarial Networks), Cyclegans?c)      Featurizers, x2vecd)      Ensembling and voting ensembles – multiple models who are ensembled (voting on the regression discussion in chapter 7)  e)      Collaborative Filtering and Recommenders

Über Autor(innen)

Josh Holmes is CTO of the Commercial Software Engineering Americas team at Microsoft. Prior to joining Microsoft, Josh consulted for a variety of clients ranging from large Fortune 500 firms to startups. Josh speaks and presents globally on the topics of IoT and machine learning. A tireless and passionate advocate for the tech community, Josh has founded and/or run numerous organizations, including the Great Lakes Area .NET Users Group and the Ann Arbor Computer Society. He was also on the forming committee for CodeMash. You can contact Josh through his blog. Mike Lanzetta designs and implements machine learning solutions for Fortune 500 companies at Microsoft. He has been doing software development for more than 20 years, working at four-person startups to Amazon. His experience runs the gamut from electronic circuit design, travel optimization, and drug discovery to demand forecasting at Amazon and machine learning at Microsoft. Mike regularly presents and chairs at conferences nationally and internationally. He has an M.Sc. in CSE from UW and a B.Sc. in CE from UCSC. He is often found blogging or tweeting on the topic of machine learning.