I have been in cloud tech for 10+ years. Here are some basic concepts that everyone in or aspiring to be in cloud should know!

Read on 👇 Cloud can be used in lots of different ways: IaaS, CaaS, PaaS, FaaS, SaaS

Here are a few click-to-deploy architectures available in our Architecture Diagramming Tool!
✅ Simple VM app
✅ 3 tier app
✅ Batch ETL pipeline
✅ Cost Management
✅ Static web hosting with domain
✅ Storage event function app ✅ Simple VM app
googlecloudcheatsheet.withgoogle.com/architecture?d…

Here's a brief introduction to @googlecloud 🧵
☁️ Infrastructure
☁️ Networking
☁️ Storage & Database
☁️ Data Analytics
☁️ ML/AI
☁️ Operations
☁️ Security
☁️ DevOps

🧵🪡 Infrastructure options:
🔹Compute Engine (GCE)
🔹Cloud Run
🔹Kubernetes Engine (GKE)
🔹Cloud Functions

Experimented with Teachable Machine today and created a #nocode classification model in less than 5 mins!

It's a web-based tool making #machinelearning models fast, easy, and accessible to everyone.

See how I did it 🧵👇

teachablemachine.withgoogle.com

#nocode #31DaysofML How do I use it?

📌Gather data (upload it)
📌Train model (in the web interface)
📌Export the model (use it in your app)

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 👉

#31DaysofML 2/10

Day 13 #31DaysofML

⚖️ How to deal with imbalanced datasets?⚖️
Most real-world datasets are not perfectly balanced. If 90% of your dataset belongs to one class, & only 10% to the other, how can you prevent your model from predicting the majority class 90% of the time?

🧵 👇 🐱🐱🐱🐱🐱🐱🐱🐱🐱🐶 (90:10)
💳 💳 💳 💳 💳 💳 💳 💳 💳 ⚠️ (90:10)
There can be many reasons for imbalanced data. First step is to see if it's possible to collect more data. If you're working with all the data that's available, these 👇 techniques can help

#31DaysofML 2/7

Since it is Day 10 of #31DaysofML it's perfect to discuss 1️⃣0️⃣ things that can go wrong with #MachineLearning Projects and what you can do about it!

I watched this amazing presentation by @kweinmeister that sums it all up

A 🧵 @kweinmeister 1️⃣ You aren't solving the right problem
❓What's the goal of your ML model?
❓How do you assess if your model is "good" or "bad"?
❓What's your baseline?
👉 Focus on a long-term mission with maximum impact
👉 Ensure that your problem is a good fit for ML

#31DaysofML

Day 9 of #31DaysofML

💁‍♀️ I thought today I would share a tip that has helped me in my #MachineLearning journey
💡The best way to learn ML is to pick a problem that you feel excited about & let it guide your learning path. Don't worry about the terms or tools, it's all secondary Here's an example. Few weeks ago I wanted to live translate an episode of @GCPPodcast. The first question I asked myself was:
🤔 Does any video/audio translation API already exist?
🔹 If so - I would give that a try
🔹 If not, I would create it from scratch

#31DaysofML (2/5)

Day 7 of #31DaysofML

⬇️ Reducing Loss ⬇️
An iterative process of choosing model parameters that minimize loss
👉 Loss function is how we compute loss
👉 Loss function curve is convex for linear regression

A 🧵 👇 Calculating loss for every value of W isn't efficient: most common way is called gradient descent
👉 Start with any value of w, b (weights & biases)
👉 Keep going until overall loss stops changing or changes slowly
👉 That point is called convergence

#31DaysofML 2/4

Day 6 of #31DaysofML

🕹 Training & Loss 🕹

In supervised learning training a model means learning good values for weights & bias from labeled examples. In doing so it attempts to find a model that minimizes loss. Process is called empirical risk minimization

A 🧵 2/4
📌 Loss indicates how bad model's prediction was on an example
📌 Loss = 0 if the model's prediction is perfect otherwise it's greater.
📌 Goal of training is to find weights & biases that have low loss, on average, across dataset

#31DaysofML

👋 Day 5 of #31DaysofML

⚡️Unsupervised learning ⚡️
Input data is unlabeled & the program learns to recognize the inherent patterns in the input data

Eg: Data across few people's eating habits
🔸Model input = 🍏🥦🧅🍓🥞🥖🍚
🔸Model output = cluster of vegetarian/vegan

A 🧵 2/8
When is unsupervised learning used?
🔸 On large datasets where annotating (labeling) data is costly
🔸 When we don't know how many classes might exist in the data
🔸 Cluster the data to apply classification on the individual clusters

#31DaysofML

👋Day 2 #31DayofML

Let's explore #MachineLearning terms for supervised learning:
🔸Labels - the thing we're predicting
🔸Features - an input variable
🔸Examples - particular instance of data (Labeled/Unlabeled)
🔸Models - defines the relationship between features & label.

A 🧵 🔸Labels - the thing we're predicting

Eg: The y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.

#31DayofML

🏹 Let's go Day 1 of #31DaysofML

💡What is #MachineLearning? 💡

ML = Using data to answer questions!
📌 Using data = Training
📌 Answer questions = Predictions

Let's keep going... 🧵👇 2/4 What are the 7 steps in Machine Learning?

1️⃣ Collect Data
2️⃣ Prepare Data
3️⃣ Choose a Model
4️⃣ Train the Model
5️⃣ Evaluate the Model
6️⃣ Parameter Tuning
7️⃣ Make Predictions

For more @yufengg amazing video 👉bit.ly/3j3j2ne

#31DaysofML