Keeping Up With Data — Week 15 Reading List

5 minutes for 5 hours’ worth of reading

Source: https://medium.com/data-for-ai/building-real-time-ml-pipelines-with-a-feature-store-9f90091eeb4

he image above comes from an article about feature stores. Moving from batch to real-time brings many challenges. One of the most painful ones is the feature engineering. Making sure features used for training a model are the same as the ones used for scoring in the real-time has caused a lot of grey hair. My business is facing this problem in one of our current assignments, I certainly hope we’ll solve it without too much stress.

A bit of a ‘pop science’ reading list with a very high frequency of the word “AI”. But it includes strawberries too.

  • Building a Data Platform to Enable Analytics and AI-Driven Innovation: Digital transformation journeys can be a rough ride, so any advice is always welcome. Simplifying operations, creating data culture, making decisions where it matters and using end-to-end packaged AI solutions are among the advised steps. Coming from a director of data analytics and AI at GCP — we need to take them with a pinch of salt. I’d actually add a thought to the very beginning of the list: be sure you know why you doing all of this! Because, it won’t be free or easy. (Lak Lakshmanan @ The Startup)
  • Here’s what happened when AI and humans met in a strawberry-growing contest: Can technology grow strawberries? Apparently, quite well. Data analysis, intelligent sensors and greenhouse automation helped the teams of scientists win over traditional farming. Whether being able to control temperature and humidity through greenhouse automation is fair — as opposed to controlling everything by hand and experience — is hard to tell. But technology and analytics certainly can increase the labour productivity by a considerable margin. (World Economic Forum)
  • VC Firms Have Long Backed AI. Now, They Are Using It. There are so many early-stage companies — which ones to bet on? Since data can help make better decisions and automate operations, it was just a matter of time before VC firms started using them more for their own operations. I think Gartner is overly optimistic with estimating that AI will be involved in 75% of investments (unless they count Google search and Siri as ‘AI being involved’?!) but who wouldn’t want to use an algorithm to process thousands of pitches of companies aiming to ‘change the world’ and identify those that have the highest chance of actually doing it. (WSJ)

After attending couple of online conferences this year, I had a chance to speak at one last weekend. I’m passionate about data and analytics because I see it as a material opportunity for businesses to increase their profit and value. But it’s heart-breaking to see how many data projects are failing. So, I shared my views on what can we do to beat the odds with the conference attendees. (Stay tuned for the recording of the talk to be released soon.)

Thanks for reading!

Please feel free to share your thoughts or reading tips in the comments.

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Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | CEO & co-founder at DataDiligence.com