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Lαк Lαкѕнмαηαη Profile picture
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7 Jul, 7 tweets, 2 min read
Let's put this one to rest using just publicly available information.

Many large, technically sophisticated enterprises are intentionally multi-cloud

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

neowin.net/news/apple-dit…

google.com/amp/s/www.thev…
Twitter uses Google Cloud for its data platform but powers its news feed using AWS

zdnet.com/article/twitte…

google.com/amp/s/techcrun…
HSBC (is it a tech company?) splits workloads between Google Cloud and AWS, and migrates some legacy services to Azure

digfingroup.com/hsbc-cloud/
Netflix is primarily on AWS but uses Google Cloud for disaster recovery and AI.

cloudcomputing-news.net/news/2018/apr/…
Reply with more examples, citing a public source.

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More from @lak_gcp

Lαк Lαкѕнмαηαη Profile picture
3 Jul
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. 🧵
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.
When building your own ML solution, avoid the temptation to build from scratch.

The best return on investment early in your projects is going to come from collecting more data (both more "rows" and more "columns", by breaking down data silos)

Use standard and/or low-code models
Read 9 tweets
Lαк Lαкѕнмαηαη Profile picture
21 Jun
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/
Instead, consider an approach that makes the reports more timely:

* Compute statistics on 6 hours of data 4 times a day
* Aggregate these 6 hourly reports to create daily reports
* You can update your "daily" report four times a day.
* Data in report is only 6 hrs old!

3/
Read 5 tweets
Lαк Lαкѕнмαηαη Profile picture
18 May
Curious about Vertex AI?

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

cloud.google.com/architecture/m…
MLOps whitepaper:

cloud.devsite.corp.google.com/resources/mlop…
Read 4 tweets
Lαк Lαкѕнмαηαη Profile picture
28 Mar
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"
"must-read for scientists and practitioners looking to apply machine learning theory to real life problems. I foresee this book becoming a classical of the discipline’s literature."
Read 9 tweets

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