My experiences building Data+AI Products & Platforms

What happens when you put your hand in the fire? There are two ways to find out:

  • Option #1: Try it yourself
  • Option #2: Learn from others who have already tried putting their hand in the fire

My motivation with these blogs is to help you with Option #2 in…

Are any of these landmines hiding in your real-world ML initiative?

87% of ML projects fail today!

These numbers should be taken with a grain of salt. Irrespective of the actual number, it does reflect reality — I have seen a significant percentage of ML-based projects never get into production!

The goal of this blog is to share…

A video series on real-world AI tips not…

A video series on real-world AI tips not…

Solving the right problem is as important as solving the problem right

Every problem is not suitable for AI! Problem framing is important to ensure success in designing and deploying an AI product. In developing AI products, the following is my checklist to ensure we are solving the “right” problem.

Verified there is quantifiable business value in solving the problem

To deliver…

Creating an AI Checklist to streamline Idea to Production

85% of AI projects never get to deployment! From the projects that get deployed, only 60% ever deliver on their promised impact! In a typical AI team today, not everyone is at the same level of experience required for building successful AI products. …

Data Labeling for ML Models

Need for Data Labeling Tools

The key to ML is the availability of “right” data. “Right” data is a combination of right features/metrics, right distribution (IID) in the raw data, and the right labeling of the data samples.

The need for labeled data is dependent on the type of ML algorithm i.e., supervised learning requires…

How to fail fast on AI projects

AI teams invest a lot of rigor in defining new project guidelines. But the same is not true for killing existing projects. In the absence of clear guidelines, teams let infeasible projects drag on for months.

They put up a dog and pony show during project review meetings for fear…

Transforming AI Geniuses into Genius Makers

Data+AI is rapidly evolving with several rapid advancements in data, ML, AI technologies. In order to succeed, it is critical for teams to keep up with the new technologies as well as leverage experiences w.r.t. delivering faster business value. The best way for teams to learn-&-grow is Peer-2-Peer mentoring.


The 7 skill personas of a well-performing AI team

Article originally published in VentureBeat

How do you start assembling an AI team? Well, hire unicorns who can understand the business problem, can translate it into the “right” AI building blocks, and can deliver on the implementation and production deployment. Sounds easy! Except that sightings of such…

Sandeep Uttamchandani

Democratize Data+AI — real-world battle scars to help w/ your journey. Product builder(Engg VP) & Data/ML leader (CDO). O’Reilly Author.

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