Day 14 #31DaysofML

🤔 How to pick the right #GoogleCloud #MachineLearning tool for your application?

Answer these questions
❓ What's your teams ML expertise?
❓ How much control/abstraction do you need?
❓ Would you like to handle the infrastructure components?

🧵 👇

@SRobTweets created this pyramid to explain the idea.
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
@SRobTweets If you’re using Open source ML frameworks (#TensorFlow) to build the models, you get the flexibility of moving your workloads across different development & deployment environments. But, you need to manage all the infrastructure yourself for training & serving

#31DaysofML 3/10
@SRobTweets Deep Learning VMs provide managed, click-to-deploy VMs for processing data & training the model
🔹 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
@SRobTweets Kubeflow - OS project for deploying ML workloads on #Kubernetes
🔹 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
@SRobTweets AI Platform - managed service for all custom model needs
🔹 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
@SRobTweets BQML: Brings the power of ML closer to where the data is analyzed & make it accessible to data analysts
🔹 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
@SRobTweets AutoML democratizes ML to build custom ML models regardless of ML expertise.
🔹 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
@SRobTweets ML APIs: Easiest and fastest way to get started with AI
🔹 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
@SRobTweets ML APIs → https://t.co/XdR6oS5Xrc​
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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The first ever world map was sketched thousands of years ago by Indian saint
“Ramanujacharya” who simply translated the following verse from Mahabharat and gave the world its real face

In Mahabharat,it is described how 'Maharishi Ved Vyasa' gave away his divine vision to Sanjay


Dhritarashtra's charioteer so that he could describe him the events of the upcoming war.

But, even before questions of war could begin, Dhritarashtra asked him to describe how the world looks like from space.

This is how he described the face of the world:

सुदर्शनं प्रवक्ष्यामि द्वीपं तु कुरुनन्दन। परिमण्डलो महाराज द्वीपोऽसौ चक्रसंस्थितः॥
यथा हि पुरुषः पश्येदादर्शे मुखमात्मनः। एवं सुदर्शनद्वीपो दृश्यते चन्द्रमण्डले॥ द्विरंशे पिप्पलस्तत्र द्विरंशे च शशो महान्।

—वेद व्यास, भीष्म पर्व, महाभारत


Meaning:-

हे कुरुनन्दन ! सुदर्शन नामक यह द्वीप चक्र की भाँति गोलाकार स्थित है, जैसे पुरुष दर्पण में अपना मुख देखता है, उसी प्रकार यह द्वीप चन्द्रमण्डल में दिखायी देता है। इसके दो अंशो मे पीपल और दो अंशो मे विशाल शश (खरगोश) दिखायी देता है।


Meaning: "Just like a man sees his face in the mirror, so does the Earth appears in the Universe. In the first part you see leaves of the Peepal Tree, and in the next part you see a Rabbit."

Based on this shloka, Saint Ramanujacharya sketched out the map, but the world laughed
Ivor Cummins has been wrong (or lying) almost entirely throughout this pandemic and got paid handsomly for it.

He has been wrong (or lying) so often that it will be nearly impossible for me to track every grift, lie, deceit, manipulation he has pulled. I will use...


... other sources who have been trying to shine on light on this grifter (as I have tried to do, time and again:


Example #1: "Still not seeing Sweden signal versus Denmark really"... There it was (Images attached).
19 to 80 is an over 300% difference.

Tweet: https://t.co/36FnYnsRT9


Example #2 - "Yes, I'm comparing the Noridcs / No, you cannot compare the Nordics."

I wonder why...

Tweets: https://t.co/XLfoX4rpck / https://t.co/vjE1ctLU5x


Example #3 - "I'm only looking at what makes the data fit in my favour" a.k.a moving the goalposts.

Tweets: https://t.co/vcDpTu3qyj / https://t.co/CA3N6hC2Lq