I’m doing what every ML person is doing right now — trying out Open AI’s chat bot.

It’s not useful for learning about a new topic — too superficial. For example, I couldn’t get Open AI to describe the algorithms that airlines use to set prices. Google search is still better. When anything goes, though, the responses are pretty cool. So, for example, when you ask it to generate poetry, you’d be happy with any reasonable result. In such cases, the open ai chatbot does quite well. Here are three examples:

DeepMind has just published a paper in Nature on doing nowcasting using neural networks. This paper uses generative models.

How does this compare to previous precipitation nowcasting models from Google?

nature.com/articles/s4158… The first attempt was a U-Net implementation consisting of (1) spatial downsampler +CNN (2) spatial upsampler + CNN with skip connections to maintain knowledge at different scales.

This bettered the performance of optical flow methods, but not by much.
arxiv.org/abs/1912.12132

I'm updating the @OReillyMedia "Data Science on GCP" book to the 2nd ed.
amazon.com/Data-Science-G…
It's been 5 years since I wrote the first version. Best practices have changed. @googlecloud has gotten broader, deeper, easier.

As I update each chapter, I will note key changes.🧵 1. Data roles have become specialized.

In Ch 1, I predicted that data roles would converge --data analysts, data scientists, and data engineers would not be 3 separate roles. While that's happened in startups and some tech companies, many enterprises have built specialized teams

The biggest change I observed between my last visit to India (Dec 2019) and now is how much an Aadhar card (national id card) is woven into everything.

🧵 The Aadhar card provides significant benefits in terms of fraud prevention esp.. with government services.

It also helps Indian businesses know their customers, while KYC is a work-in-progress for most American ones.

The Aadhar card is one reason micro payments have taken off.

Let's put this one to rest using just publicly available information.

Many large, technically sophisticated enterprises are intentionally multi-cloud

🧵

https://twitter.com/samson_hu/status/1412566632480657409

Snap uses Google Cloud and AWS for different workloads
interconnected.blog/aws-gcp-real-w…

Many of our customers want to know how to choose a technology stack for solving problems with machine learning.

In this article, I summarize my thought process when suggesting a tech stack for ML. 🧵

https://twitter.com/GoogleCloudTech/status/1411098610230386689

A key decision that you have to make for each ML problem is to decide whether to:
(1) buy a vendor's pre-built solution
(2) build your own

Make this decision based on whether you have access to more data than the vendor.

This is also a handy rule to choose between vendors.

Many data engineers and CIOs tend to underestimate an ironic aspect of a dramatic increase in data volumes.

The larger the data volume gets, it makes more and more sense to process the data *more* frequently!
🧵 To see why, say that a business is creating a daily report based on its website traffic and this report took 2 hours to create.

If the website traffic grows by 4x, the report will take 8 hours to create. So, the tech people 4x the number of machines.

This is wrong-headed!

2/

Curious about Vertex AI?

Customer testimonials:
cloud.google.com/blog/products/… ML on GCP best practices:

cloud.google.com/architecture/m…

Five months later, our ML patterns book is #3 in AI, behind only the top ML intro book and the top research one. Very grateful for the validation ... W/ @SRobTweets
amazon.com/Machine-Learni…
Like most authors, we keep hitting F5 to read the reviews 😁 My favorites 🧵👇 "When I was learning C++, I found the Gang of Four book "Design Patterns" accomplished a similar goal to help bridge the gap between academic knowledge and practical software engineering. Much like with the GoF book I suspect I may be re-reading parts of this book in the future"