As you move up the pyramid, less ML expertise is required, and you also don’t need to worry as much about the infrastructure behind your model.
To lear more watch this video 👉 https://t.co/EqJNDmTfhV
#31DaysofML 2/10
Day 14 #31DaysofML
🤔 How to pick the right #GoogleCloud #MachineLearning tool for your application?
Answer these questions
âť“ What's your teams ML expertise?
âť“ How much control/abstraction do you need?
âť“ Would you like to handle the infrastructure components?
🧵 👇
As you move up the pyramid, less ML expertise is required, and you also don’t need to worry as much about the infrastructure behind your model.
To lear more watch this video 👉 https://t.co/EqJNDmTfhV
#31DaysofML 2/10
#31DaysofML 3/10
🔹 Popular ML frameworks pre-installed
🔹 Reduces the overhead of managing & allocating compute & storage required
🔹 But you figure out how you’ll serve those models
#31DaysofML 4/10
🔹 Helps configure a multi-step ML pipeline including pre-processing data, training & serving the model
🔹 Run it on-premise or on any cloud
🔹 You’ll still need to configure where it’s managed
#31DaysofML 5/10
🔹 Includes tools for training & serving models, hosted notebooks, a data labeling service & more
🔹 Eg: take notebook code running on-premise with Kubeflow, and run it on GCP with AI Platform Notebooks
#31DaysofML 6/10
🔹 You don’t have to write any of the underlying model code
🔹 Choose model type
🔹 Simple SQL queries to create & train the model & make predictions
#31DaysofML 7/10
🔹 Use the UI to upload the data - images, video, text, or structured
🔹 Press "train" button
🔹 Model is available for prediction via an API
🔹 No need to deploy it yourself
#31DaysofML 8/10
🔹 Don’t need ML engineers or data scientists just some developers
🔹 Simple API request to pre-trained models for images, video, speech, text & translation
🔹 No need to supply any training data yourself
#31DaysofML 9/10
AutoML → https://t.co/vbmIBiciLF​
BQML → https://t.co/Hs8zz57pcn​
AI Platform → https://t.co/zyYRq4HzT5​
Kubeflow → https://t.co/DNX7MftUb3​
Deep Learning VMs → https://t.co/9MG9KntYXb​
Tensorflow → https://t.co/G2xLT68gRX​
10/10
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1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
I’m torn on how to approach the idea of luck. I’m the first to admit that I am one of the luckiest people on the planet. To be born into a prosperous American family in 1960 with smart parents is to start life on third base. The odds against my very existence are astronomical.
I’ve always felt that the luckiest people I know had a talent for recognizing circumstances, not of their own making, that were conducive to a favorable outcome and their ability to quickly take advantage of them.
In other words, dumb luck was just that, it required no awareness on the person’s part, whereas “smart” luck involved awareness followed by action before the circumstances changed.
So, was I “lucky” to be born when I was—nothing I had any control over—and that I came of age just as huge databases and computers were advancing to the point where I could use those tools to write “What Works on Wall Street?” Absolutely.
Was I lucky to start my stock market investments near the peak of interest rates which allowed me to spend the majority of my adult life in a falling rate environment? Yup.
Ironies of Luck https://t.co/5BPWGbAxFi
— Morgan Housel (@morganhousel) March 14, 2018
"Luck is the flip side of risk. They are mirrored cousins, driven by the same thing: You are one person in a 7 billion player game, and the accidental impact of other people\u2019s actions can be more consequential than your own."
I’ve always felt that the luckiest people I know had a talent for recognizing circumstances, not of their own making, that were conducive to a favorable outcome and their ability to quickly take advantage of them.
In other words, dumb luck was just that, it required no awareness on the person’s part, whereas “smart” luck involved awareness followed by action before the circumstances changed.
So, was I “lucky” to be born when I was—nothing I had any control over—and that I came of age just as huge databases and computers were advancing to the point where I could use those tools to write “What Works on Wall Street?” Absolutely.
Was I lucky to start my stock market investments near the peak of interest rates which allowed me to spend the majority of my adult life in a falling rate environment? Yup.
The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the *hundreds of millions*. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/
Flat Earth conference attendees explain how they have been brainwashed by YouTube and Infowarshttps://t.co/gqZwGXPOoc
— Raw Story (@RawStory) November 18, 2018
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/