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 the benefits of Cloud Computing?
Migrating applications to cloud & modernizing applications with cloud involve familiarizing with concepts such as microservices, containers, kubernetes, devops and more!
How do you migrate applications to cloud? What do all microservices need?
Here are benefits and use cases of storage in cloud
Here are the three most common types of storage in cloud - Object store, Block store, File store
Here are the benefits of cloud databases
Here are the two common database types: Relational & Non-relational. There are databases that combine the benefits of both relational and non-relational.
Cloud network is a Software Defined Network (SDN) where the control plane (decision making) is separated from the data plane (packet forwarding) allowing for more flexibility and programmability in network management.
Here are the fields of Data Analytics
Here are the steps involved in a data analytics pipeline
Here are the 6 stages in a data science and a machine learning workflow.
Here are the components of cloud security.
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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
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.
@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
⚖️ 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
Here are 3 techniques for addressing data imbalance. You can use just one of these or all of them together:
⚖️ Downsampling
⚖️ Upsampling
⚖️ Weighted classes
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
@kweinmeister 2️⃣ Jumping into development without a prototype
👉 ML project is an iterative process
👉 Start with simple model & continue to refine it until you've reached your goal
👉 Quick prototype can tell a lot about hidden requirements, implementation challenges, scope, etc