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!

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Experiences divided into 6 phases of any ML project. Depending on your role, feel free to read the respective sections in this blog (Image by author)

This is a…


Understanding under-the-hood details of modern NoSQL systems

What distinguishes a good data engineer from a great one? Having an understanding of distributed systems concepts can help in making the right Big Data technology choices as well as write better data apps and pipelines.

To illustrate, imagine buying a car. You will typically have a few different car models and then compare the price/performance i.e., engine, transmission, braking, etc. …


Blog series on DataOps for effective AI/ML

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Weekly curated articles on applying AI without the hype

Why read this newsletter? There is real progress being made in AI! But, with all the hype, it is difficult to distinguish reality from fiction. This curated newsletter will help you distill the real AI from the noise — stay informed with how AI is being used today to automate well-defined tasks, advancements in platforms/tools to apply AI, and progress in research in defining the art of possible! A weekly newsletter for your weekend reading!

The Four Waves of AI — Understanding the AI task you are trying to solve as a combination of four capabilities: Perceiving, Learning, Abstracting, Reasoning

AI in Action

This week we are focussing on articles related to AI for Marketing & Sales: Customer service management, Next product to Buy, Churn reduction, Pricing, and…


In addition to skills, IQ, EQ, look for Data IQ (DQ)

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How do you future proof your Data + AI/ML team hires? Will your current team be able to learn the skills for technology that does not exist today?

Consider the role of a data engineer. Two decades ago, the role was focussed around data warehouse schema designs, schema-on-write governance, generating weekly business reports. This is the time when data was considered part of the IT expense.

Over the…


Weekly curated articles on applying AI without the hype

Source

AI in Action

  • Using re-enforcement learning is answer the question: Which of the many ads is more likely to appeal to a certain viewer? Finding the right…


There is real progress being made in AI! But, with all the hype, it is difficult to distinguish reality from fiction. This curated newsletter will help you distill the real AI from the noise — stay informed with how AI is being used today to automate well-defined tasks, advancements in platforms/tools to apply AI, and progress in research in defining the art of possible! A weekly newsletter for your Sunday morning reading!

GIF Source

AI in Action

  • AI robo-advisors in Finance: AI is helping human financial advisors by relieving them from having to perform mundane portfolio monitoring and administrative tasks that currently take…


21 Playbooks to help navigate the common leadership scenarios

Whether you are a software engineer looking to grow into a Tech Lead/ Manager role or a data engineer/analyst/scientist aspiring to move into a data leadership role, there are a common set of behaviors that are required.

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Source: https://rework.withgoogle.com/guides/managers-identify-what-makes-a-great-manager/steps/learn-about-googles-manager-research/

I have gone through the journey from an IC to VP of Engineering and Chief Data Officer. …


The need to think holistically about metadata in your DataOps

There is no dearth of data within the enterprise, but consuming the data to solve business problems is a major challenge today! There is a growing focus in building metadata management frameworks: Netflix’s Metacat, Uber’s Databook, Airbnb’s Dataportal, LinkedIn’s Datahub, Lyft’s Amundsen, WeWork’s Marquez, Spotify’s Lexikon, Apache’s Atlas, and many more.

Why is metadata becoming important?

Prior to the big data era, data was curated before being added to the central warehouse — the metadata details, including schema, lineage, owners, business taxonomy, and so on, were cataloged first. This is known as schema-on-write.

Image by author

Today, the approach with data lakes is…

Sandeep Uttamchandani

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

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