Domino Data Science Blog

Meta's release of Llama 2 is a pivotal moment for businesses seeking to harness generative AI.

I am fortunate to work with some of the most sophisticated global companies on their AI/ML initiatives. These companies include many household names on the Fortune 500 and come from industries as diverse as insurance, pharmaceuticals, and manufacturing. Each has dozens to literally thousands of data scientists on its payroll. While they have significant investments in AI and ML, they exhibit a surprisingly wide array of maturity when it comes to MLOps.

Today’s businesses are investing heavily in data science – spending on software, hardware and services is projected to break the $500 billion mark by 2024, according to IDC. Data science models with machine learning (ML) and artificial intelligence (AI) techniques have proven their worth at forging new revenue streams and upending entire industries. For a model-driven business, new revenue from such use cases can range from hundreds of millions to billions of dollars. At successful companies, leaders have built a well-oiled analytical flywheel to create a steady flow of models that can tap this new gold rush.

Algorithms may be the toast of today’s high-performance technology races, but sometimes proponents forget that, like cars, models also need a regular tune-up. A highly visible and catastrophic AI model failure recently shamed Zillow, the online real estate company that was forced to shutter its home-buying business.

R Markdown is one of those indispensable tools in a data scientist’s toolbox that provides speed and flexibility with the last-mile problem of getting your work into production. Speed comes from how easy it is to host and schedule R Markdown reports. Flexibility comes from the wide array of options for production output. Whether it is prototyping an intermediate result or producing quality output that will put your work in its best light, R Markdown has a lot to offer.

There’s a lot of momentum right now with machine learning (ML) and artificial intelligence (AI), and we have an opportunity to do something exceptional: build products and solutions that make a real difference in our industries and our world.