The presentation discusses the importance of data preparation and framework in building a successful data-driven company and machine learning models.
- Data preparation is crucial in building a data-driven company and machine learning models.
- The data framework consists of six layers: data sources, data storage, data processing, data module reusability, matrix, and machine learning life cycles.
- The last layer of the data framework is insights, which aim to educate leaders to form opinions and influence business strategies.
- Observability, governance, and automation are the future opportunities in the data-driven industry.
- Proper preparation prevents poor performance in building machine learning models.
- All machine learning models are hypotheses and should be verified with a B test.
- It is important to ensure that the solution can be understood by humans and not just a black box.
The speaker shared an example of how machine learning was used to optimize search results for an e-commerce website. Initially, the most important KPI was to optimize for click-through rate, which led to showing all cheap items on the first page. However, this resulted in showing low-quality items and was not a good idea for a marketplace. This highlights the need for insights from the data to educate leaders and influence business strategies.