In part one and two, we introduced Iguazio's feature store and discussed the benefits of using one in the ML workflow. Additionally, we ingested and transformed the data that we will be using to train our model. In this blog, we will do the following.

The start of a new year is a perfect time to reflect on what was accomplished and look forward, re-evaluate what we can do better. Change, although difficult at first, can also be very rewarding. That’s why I was excited to see similar sentiments shared at Thoughtspot beyond.2021 to move beyond the traditional dashboards of the past.

For Snowflake CIO and CDO, Sunny Bedi, “life after dashboards” isn’t just a catchy tagline – it’s how he runs his business. At the helm of Snowflake’s 250-person IT and security organization, Bedi oversees a range of strategic initiatives including security and access control, data quality, and general system availability and performance.

Dozens of new connectors, partner integrations, security and compliance enhancements — and the Fivetran data models you love.

Organizations increasingly turn to AI to transform work processes, but this rapid adoption of models has amplified the need for explainable AI. Explaining AI helps us understand how and why models make predictions. For example, a financial institution might wish to use an AI model to automatically flag credit card transactions for fraudulent activity. While an accurate fraud model would be a first step, accuracy alone isn’t sufficient.