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Belonging to the realm of intelligent technologies, it is
increasingly accepted that artificial intelligence (AI) has evolved
from being merely a development standpoint in computer science.
Indeed, recent reports and academic publications show that we are
clearly on the path toward pervasive AI in both business and
society. Organizations must adopt AI to maintain a competitive
advantage and explore opportunities for unprecedented innovation.
This book focuses on understanding the wide range of opportunities
as well as the spectrum of challenges AI brings in different
business contexts and society at large. The book highlights novel
and high-quality research in data science and business analytics
and examines the current and future impact of AI in business and
society. The authors bridge the gap between business and technical
perspectives and demonstrate the potential (and actual) impact on
society. Embracing applied, qualitative, and quantitative research
as well as field experiments and data analysis, the book covers a
broad range of topics including but not limited to human-centered
AI, product and process innovation, corporate governance, AI and
ethics, organizational performance, and entrepreneurship. This
comprehensive book will be a valuable resource for researchers,
academics, and postgraduate students across AI, technology and
innovation management, and a wide range of business disciplines.
Machine Learning Governance for Managers provides readers with the
knowledge to unlock insights from data and leverage AI solutions.
In today's business landscape, most organizations face challenges
in scaling and maintaining a sustainable machine learning model
lifecycle. This book offers a comprehensive framework that covers
business requirements, data generation and acquisition, modeling,
model deployment, performance measurement, and
management, providing a range of methodologies, technologies, and
resources to assist data science managers in adopting data and
AI-driven practices. Particular emphasis is given
to ramping up a solution quickly, detailing skills and
techniques to ensure the right things are measured and acted upon
for reliable results and high performance. Readers will learn
sustainable tools for implementing machine learning with existing
IT and privacy policies, including versioning all models, creating
documentation, monitoring models and their results, and assessing
their causal business impact. By overcoming these challenges,
bottom-line gains from AI investments can be realized.
Organizations that implement all aspects of AI/ML model governance
can achieve a high level of control and visibility over how models
perform in production, leading to improved operational efficiency
and a higher ROI on AI investments. Machine Learning Governance for
Managers helps to effectively control model inputs and understand
all the variables that may impact your results. Don't let
challenges in machine learning hinder your organization's growth -
unlock its potential with this essential guide.
Perform more advanced analysis and manipulation of your data beyond
what Power BI can do to unlock valuable insights using Python and R
Key Features Get the most out of Python and R with Power BI by
implementing non-trivial code Leverage the toolset of Python and R
chunks to inject scripts into your Power BI dashboards Implement
new techniques for ingesting, enriching, and visualizing data with
Python and R in Power BI Book DescriptionPython and R allow you to
extend Power BI capabilities to simplify ingestion and
transformation activities, enhance dashboards, and highlight
insights. With this book, you'll be able to make your artifacts far
more interesting and rich in insights using analytical languages.
You'll start by learning how to configure your Power BI environment
to use your Python and R scripts. The book then explores data
ingestion and data transformation extensions, and advances to focus
on data augmentation and data visualization. You'll understand how
to import data from external sources and transform them using
complex algorithms. The book helps you implement personal data
de-identification methods such as pseudonymization, anonymization,
and masking in Power BI. You'll be able to call external APIs to
enrich your data much more quickly using Python programming and R
programming. Later, you'll learn advanced Python and R techniques
to perform in-depth analysis and extract valuable information using
statistics and machine learning. You'll also understand the main
statistical features of datasets by plotting multiple visual graphs
in the process of creating a machine learning model. By the end of
this book, you'll be able to enrich your Power BI data models and
visualizations using complex algorithms in Python and R. What you
will learn Discover best practices for using Python and R in Power
BI products Use Python and R to perform complex data manipulations
in Power BI Apply data anonymization and data pseudonymization in
Power BI Log data and load large datasets in Power BI using Python
and R Enrich your Power BI dashboards using external APIs and
machine learning models Extract insights from your data using
linear optimization and other algorithms Handle outliers and
missing values for multivariate and time-series data Create any
visualization, as complex as you want, using R scripts Who this
book is forThis book is for business analysts, business
intelligence professionals, and data scientists who already use
Microsoft Power BI and want to add more value to their analysis
using Python and R. Working knowledge of Power BI is required to
make the most of this book. Basic knowledge of Python and R will
also be helpful.
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