Pattern ML

In this episode, we dive into the challenges and solutions in applying AI to a field as meaningful and subtle as your own career. Stellares is an AI Talent Agency that uses machine learning to help top tech talent navigate their careers.

UnifyID authenticates users using implicit signals like typing speed and gait. In this episode, we learn more about this innovative use of machine learning, and explore UnifyID’s unique recruiting strategy.

Pedro compares the state of ML now to the beginning of the aviation industry; early pilots needed to know everything from the physics of flight to the engineering behind the plan and nowadays, a pilot interfaces with the entire plane through a cockpit dashboard. Ople AI is trying to be that dashboard, hiding implementation details of training to allow ML engineers to focus on higher level questions like how can the data available be used as features to solve business problems.

NanoNets is a Machine Learning as a Service platform for developers to rapidly create/deploy models. Learn about how they do it with transfer learning, a technique that uses learnings from coarse models to bolster others. They have a product aimed at developers and give insights into where ML tech is going!

Sales is a game of managing customer leads, pipelines, and followups. People.AI is automating the sales manager to help you close more deals backed by some unexpected yet highly reliable data sources. This interview we focus on the advantages of using high quality data sets and starting off as a consulting shop, to help build out new models that you work alongside your customers to develop!

Learn how Adam and the team at LiftIgniter are bringing innovations from Google to the rest of the business world. Google, whose  main revenue come from ads, has developed arguably the world’s most sophisticated machine learning for ad targeting. Lift Igniter is taking similar concepts and creating a bleeding edge personalization engine that ups user engagement across the board.

Preteckt is an ingenious cross between a IoT and ML company. The company uses its own hardware sensors to determine when a customer's 16 wheeler is going to break down.

In this episode, we talk about how to optimize for data growth and not revenue growth when first starting a ML company. Its the ultimate positive feedback loop of
Customer -> More Data -> Better Product -> More Customers

Kylie.ai is a service that automates customer service tickets. This is a longer than average episode as Sinan’s interview was too good to be cut down any shorter. We dive in on building models per rep, and how Sinan was able to train a model using public Twitter data so that he could have a customer solution before even stepping into a meeting!

A former Data Science lecturer at Johns Hopkins University, Sinan has written a book for beginners who would like to learn Data Science. His book is practical and we highly recommend it. Check it out