Author | Professor | Founder Pragmatic AI Labs

Selected Publications

A practical guide to Data Science, Machine Learning Engineering and Data Engineering
Pragmatic AI Labs, 2020

Four out of 10 recent college grads in 2019 are in jobs that didn’t require their college degree. Student loan debt is at an all-time high, as it has climbed to more than $1.5 trillion this year. Meanwhile, the unemployment rate is hovering at 3.6%. What is happening? There is a jobs and education mismatch.
reInvent, 2019

Learn Ruthlessly Effective Automation
O’Reilly, 2019

When was the last time the CPU clock speed in your laptop got faster? When was the last time it was cool to doubt the cloud? The answer is: around the time the vertically integrated AI stack started to get some serious traction. You might be asking yourself, “What is a vertically integrated AI stack?” The short answer is that there isn’t a perfect definition, but there are a few good starting points to add some clarity to the discussion.
Forbes, 2018

Data science is no longer a niche topic at companies. Everyone from the CEO to the intern knows about how valuable it is to take a scientific approach to dealing with data. Consequently, many people not directly in software engineering fields are starting to write more code, often in the form of interactive notebooks, such as Jupyter. Software engineers have typically been huge advocates of build systems, static analysis of code, and generating repeatable processes to enforce quality. What about business people who are writing code Jupyter Notebooks? What processes can they use to make their data science, machine learning, and AI code more reliable?
CircleCI, 2018

Noah previously worked for a startup called Score Sports, which used machine learning to uncover athlete influence on social media and internet platforms. We look into some of his findings in that role, including how to predict the impact of athletes’ social media engagement. We also discuss some of his more recent work in using social media to predict which players hold the most on-court value, and how this work could lead to more complete approaches to player valuation. Finally, we spend some time discussing some areas that Noah sees as ripe for new research and experimentation across sports, and we take a look at his upcoming book Pragmatic AI, An Introduction to Cloud-Based Machine Learning. For those interested in pre-ordering the book, be sure to check out the link in the show notes for a nice discount code.
TWiML, 2018

Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. Throughout, readers find simple, clear, and effective working solutions that show them how to apply machine learning, AI and cloud computing together in virtually any organization, creating solutions that deliver results, and offer virtually unlimited scalability.
Pearson, 2018

The cloud has been a disruptive force that has touched every industry in the last decade. Not all cloud technology is the same, though. There are cloud-native technologies like serverless and cloud-legacy technologies like relational databases, which were first proposed in 1970. One easy way to note this distinction is to think about the cloud as the new operating system. Before there was the cloud, you had to write your application for Windows, Mac or some Unix/Linux flavor. After the cloud, you could either port your legacy application and legacy technologies, or you could design your application natively for the cloud.
Forbes, 2018

About five years ago in business school, almost every MBA course used Excel. Now, as someone who teaches at business school, I’ve seen firsthand how almost every class uses some type of notebook technology. Outside of the classroom, businesses are rapidly adopting notebook-based technologies that either replace or augment traditional spreadsheets. There are two main flavors of notebook technology: Jupyter Notebooks and R Markdown.
Forbes, 2018

What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles. The 2016 US presidential election showed how important it is to understand how recommendation engines work and the limitations and strengths they offer.
InfoWorld, 2018

As the first technical employee and CTO of Sqor Sports, Noah Gift discovers how social media influences the NBA and vice versa. As a result, Sqor Sports has been able to grow to millions of monthly active users by leveraging influencers that were found by machine learning algorithms. Noah shares his research and lessons learned on how social media and the NBA intersect and explains how Sqor Sports uses data science and machine learning to determine NBA team valuation and attendance as well as individual player performance. You’ll learn how to recreate this research, working with a set of shared Juypter notebooks.
Strata, 2018

Where cloud data and analytic techniques meet.
IBM, 2011

Recent & Upcoming Talks

Solving The Cloud Skills Crisis
Dec 4, 2019 3:15 AM
Managed Machine Learning Systems and Internet of Things
Jun 11, 2019 8:00 AM
Strata Data Conference 2019: Nutrition Data Science
Mar 29, 2019 8:00 AM

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