Similarities and Differences between ML and Analytics
About this event
Outline:
- Predictive and prescriptive analytics
- Similarities and differences in skills, tools, and use cases
About the guest:
Rishabh has worked with analytics and ML teams for 7+ years. Most recently, he led Sales Engineering at a data infra company called Datacoral (acquired by Cloudera) helping analytics teams with their data pipelines. Previous to that, he was an early employee at Primer.ai where he built and deployed ML models for multiple natural language applications. He also writes a newsletter that discusses challenges with ML in production: mlopsroundup.substack.com.
Rishabh has a Masters in CS from Stanford and completed his undergraduate studies at the University of Cambridge.
I learned many things in the IT Webinar. For example, analytics looks at the past. Machine learning is looking at data from the past but you make forecasts and try to guess what happens in the future. Analytics and machine learning might have the same people working on both but the outcomes are different. Machine learning spends lots of time trying to figure out how we improve the system so it performs good in a software point of view. A data scientist’s job can be very different depending on what company you work for. Some of the best data scientists closely understand why they are working on something and how it will impact the company.