Heather Dawe – författare

Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfallsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning modelsBook DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learnUnderstand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platformsWho this book is forThis book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.

Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfallsPurchase of the print or Kindle book includes a free PDF eBook

Key Features

Learn ethical AI principles, frameworks, and governanceUnderstand the concepts of fairness assessment and bias mitigationIntroduce explainable AI and transparency in your machine learning models

Book Description

Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.

What you will learn

Understand explainable AI fundamentals, underlying methods, and techniquesExplore model governance, including building explainable, auditable, and interpretable machine learning modelsUse partial dependence plot, global feature summary, individual condition expectation, and feature interactionBuild explainable models with global and local feature summary, and influence functions in practiceDesign and build explainable machine learning pipelines with transparencyDiscover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms

Who this book is for

This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.

'The last descent and I can't let myself think it's in the bag. Anything could happen, take it easy, take no risks. Just get to the finish and win.''The challenge and anticipation that pushes me to try harder. The obsessive urge to achieve. It's not all about winning. Why do I do it?'Growing up in Bristol, Heather Dawe was 17 when she started running. Having fallen in to the teenage trap of smoking and drinking she resolved to do something about it, not knowing then where it would take her.A climber since her youth, an obsession with wild places and the mountains was engrained in her DNA. Moving to Leeds to study, she began to compete in fell races and mountain marathons, joking in the pub one night that she could race at the highest level.Being hit by a car doing over 40mph while cycling would have ended many athletes' dreams, but Dawe's drive pushed her even harder. Hard enough to make her pub joke a reality, hard enough to win Elite Mountain Marathons, to win the Three Peaks Cyclo-cross race and to complete the Bob Graham Round. Pushing harder still, she entered the Tour Divide - racing the 2745-mile route of the Continental Divide in North America as she to sought to discover her physical - and emotional - limits.Dawe writes of what it takes to compete in adventure races; the training, the sacrifice, the mistakes that must be made in order to learn and develop. An intensely deep and personal book, Adventures in Mind explores what drives a woman - living with her partner and their child, working 9-5 - to push so hard and so far; into herself, and into the wild.

In July 2014 the greatest cycle race in the world visited God’s Own County of Yorkshire. The 101st edition of the Tour De France started in Leeds and spent two days cycling over 240 miles through the county.Inspired by Yorkshire’s wonderful landscape and history A Bicycle Ride in Yorkshire is an illustrated guide to the route of Le Tour Yorkshire, by cyclist, writer and artist Heather Dawe. It is a guide to riding the route, to the sights and landscape seen along the way and to the rich cycling heritage found on Yorkshire’s roads.Beautifully illustrated with Heather’s paintings, the route is brought to life. On the first day Le Tour departed from Leeds into the rolling scenery of the Yorkshire Dales, past the breweries of Masham to finish in the spa town of Harrogate. Day two began in the historic city of York, from where it explored the industrial valleys of the South Pennines, skirting the Peak District before finishing in the steel city of Sheffield.A Bicycle Ride in Yorkshire is an essential read for any cycling, Tour de France or Yorkshire aficionado.

Winner: Mountain Literature (Non Fiction) The Jon Whyte Award, Banff Mountain Book Competition 2019Waymaking is an anthology of prose, poetry and artwork by women who are inspired by wild places, adventure and landscape.Published in 1961, Gwen Moffat’s Space Below My Feet tells the story of a woman who shirked the conventions of society and chose to live a life in the mountains. Some years later in 1977, Nan Shepherd published The Living Mountain, her prose bringing each contour of the Cairngorm mountains to life. These pioneering women set a precedent for a way of writing about wilderness that isn’t about conquering landscapes, reaching higher, harder or faster, but instead about living and breathing alongside them, becoming part of a larger adventure.The artists in this inspired collection continue Gwen and Nan’s legacies, redressing the balance of gender in outdoor adventure literature. Their creativity urges us to stop and engage our senses: the smell of rain-soaked heather, wind resonating through a col, the touch of cool rock against skin, and most importantly a taste of restoring mind, body and spirit to a former equanimity.With contributions from adventurers including Alpinist magazine editor Katie Ives, multi-award-winning author Bernadette McDonald, adventurers Sarah Outen and Anna McNuff, renowned filmmaker Jen Randall and many more, Waymaking is an inspiring and pivotal work published in an era when wilderness conservation and gender equality are at the fore.

With a back-story of running round the route of the classic Alpine journey the Tour de Mont Blanc, Heather Dawe explores the drive behind mountain running and racing, and how simply being in the mountains has inspired herself and other climbers, writers, artists and innovators through the years.

Mountain Stories is an illustrated memoir of journeys through some of Scotland’s most beautiful landscapes, including Skye's Cuillin, Knoydart, Assynt and the Far North. Writing during lockdown, author and artist Heather Dawe finds telling these stories a powerful means of reconnection with the mountains when they are physically inaccessible.Dawe's journeys are made by walking, running, cycling or sea-kayak. The stories are a reflection of the importance of wild places and the inspiration, art and culture associated with them.