Journeys to Democratize Data+AI Sandeep Uttamchandani

Democratize is not one big strategy but 100s of small things that you put in place. Quicktake is a series of short practical tips to get you started towards making data and AI widely accessible and self-service within your organization.

This tip covers an important myth: To improve model accuracy, start by verifying the correctness of labels. Typically, there is only a small percentage of miss predictions that are related to wrong labels. Often times the biggest reason for model inaccuracies is the poor quality of data samples. Train the team just that instead of jumping to fix the incorrect labels, start by analyzing a sample of results that were misclassified and make a judgment call on whether to invest in fixing the labels going back and looking at opus deposit useful.

Democratize is not one big strategy but 100s of small things that you put in place. Quicktake is a series of short practical tips to get you started towards making data and AI widely accessible and self-service within your organization.

This tip covers 3 recipes on handling misclassified ML predictions within your product.

In this episode, my guest is Daniel Mccaffery. Daniel is a technology thought leader driving Data and Analytics at Climate Corporation (a division of Bayer).

Daniel shares his insights on using ML to provide personalized recommendations for helping farmers grow crops with higher yield, profitable and sustainability. This involves deciding the right seed, right crop protection, density levels across different parts of the farm, etc. This is a fascinating example of AI and physical sciences coming together to build an innovative product offering. Daniel and I had a blast covering several topics: the building of models, model deployment and re-training, explainability for farmers to understand the recommendations, managing bias, experimentation A/B testing, monitoring drifts, data labeling, and perspectives on key bottlenecks in going from idea to ROI.

In this episode, Samir Boualla shares the journey to democratize Data Governance across business teams (with Data Literacy & Data Protection). He also discusses how they build internal and external-facing data products to expedite the journey to self-service Data.

At ING Bank France, Samir is the Chief Data Officer responsible for several teams governing, developing, and managing data infrastructure and data assets to deliver value to the business. With over 20+ years of experience on various data topics. Samir shares interesting battle-tested techniques in this podcast: a process catalog, having a "data minimum standard," change management mindset, applying transfer learning.

In this episode (part 2), Kapil Surlaker shares the journey to democratize data quality at LinkedIn scale!

Kapil has 20+ years of experience in data and infrastructure both at large companies such as Oracle as well as multiple startups. At LinkedIn, Kapil has been leading the next generation of Big Data Infrastructure, Platforms, Tools, and Applications to empower Data Scientists, AI engineers, App developers, to extract value from data. Kapil's team has been at the forefront of innovation driving multiple open source initiatives such as Apache Pinot, Gobblin, DataHub.

In this episode, Chu-Cheng covers experiences in recruiting and building a Data Science team from scratch. 

Chu-Cheng is the Chief Data Officer (CDO) at Etsy. Chu-Cheng leads the global data organization responsible for data science strategy, AI innovation, machine learning & data infrastructure. Prior to Etsy, Chu-Cheng led various data roles at Amazon, Intuit, Rakuten, and eBay.  Chu-Cheng is a Ph.D. in computer science, with published papers in key AI/ML conferences.