Machine learning ebook
Data is the raw material for machine learning. Find out how to use machine learning in the cloud with the data you already have.
“Machine learning” is a term that we hear virtually everywhere
today. Everyone, it seems, is getting into it, and for good reason.
Companies are gaining competitive advantage, delivering better cus‐
tomer experiences, increasing revenue, reacting more swiftly to
market shifts—all by applying machine-learning techniques and
technologies to the data they already possess.
But challenges abound as well. Data scientists with the necessary know-how are scarce, and they sometimes work in organizational silos rather than in collaboration with other stakeholders in the technical and business groups. It can be difficult to integrate
machine-learning models with existing or new applications or pro‐
cesses. And a host of security issues in this area have yet to be resolved.
In this report, we go over the types of real-world applications for machine learning that are delivering successes today. We review the benefits and challenges alike of deploying machine learning. We also explain why most organizations are currently implementing their machine-learning projects in the cloud. And we provide you with a list of best practices that early adopters have found useful in their
Think of all the data that is collectively accumulating across different
areas of the enterprise. Terabytes and terabytes of it. Just imagine if
you could really put this data to work for you.
Imagine predicting with an extraordinary degree of accuracy how
much of Product A you’ll sell next quarter. Knowing exactly which
customers will deliver the highest lifetime value. Identifying and fix‐
ing inefficiencies in your back-office operations.
This is all possible if you are taking advantage of your data effec‐
tively, and if you are—to use the latest vocabulary—data-driven. But recent research by NewVantage Partners finds that most large companies are experiencing problems getting there. A full 69% of IT
professionals surveyed said they have failed to create data-driven
organizations. Worse, we’re apparently heading in the wrong direction. The percentage of organizations calling themselves “data-driven” has actually declined annually in recent years—from 37.1%
in 2017 to 32.4% in 2018, and down to 31.0% in the latest survey.
Machine learning promises to reverse this negative trend. An
increasingly popular technology that falls under the broad umbrella
of artificial intelligence (AI), machine learning is predicted by
McKinsey Global Institute to create an additional $2.6 trillion in
value by 2020 in marketing and sales, and up to $2 trillion in value for manufacturing and supply-chain planning activities. If you haven’t started experimenting with machine learning to tame your data, you could quickly fall behind the curve. In the next section, we give some real-world examples of how machine learning is already yielding often hefty returns on investment (ROI) to companies.
Machine learning is paying off for early adopters. A full 82% of
enterprises adopting machine learning and AI have gained signifi‐
cant financial advantage from their investments, with an impressive
median ROI of 17%, according to Deloitte. Here are some examples
of machine learning as applied to challenges businesses face every
Your article helped me a lot, is there any more related content? Thanks! https://www.binance.com/en/register?ref=W0BCQMF1
Your article made me suddenly realize that I am writing a thesis on gate.io. After reading your article, I have a different way of thinking, thank you. However, I still have some doubts, can you help me? Thanks.
Reading your article helped me a lot and I agree with you. But I still have some doubts, can you clarify for me? I’ll keep an eye out for your answers.