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Deployment

5 Must-Know Ways to Test ML Models in Production
...with implementation.
 • 
Avi Chawla
Version Controlling and Model Registry in ML Deployments
Take your production environment from good to great.
 • 
Avi Chawla
How You Can Simplify Cloud Development with Winglang?
An ecosystem dedicated to cloud.
 • 
Avi Chawla
7
Activation Pruning — Reduce Neural Network Size Without Significant Performance Drop
Get rid of useless neurons.
 • 
Avi Chawla
6
PyTorch Models Are Not Entirely Deployment-Friendly
Eliminating the dependence of PyTorch models on Python.
 • 
Avi Chawla
1
Boost Sklearn Model Training and Inference by Doing (Almost) Nothing
...along with a lesser-known advice on vectorized operations.
 • 
Avi Chawla
4
Federated Learning: An Overlooked ML Technique That Deserves More Attention
A step towards privacy-preserving ML.
 • 
Avi Chawla
Transfer Learning vs. Fine-tuning vs. Multitask Learning vs. Federated Learning
Four critical model training paradigms that you MUST know for real-world ML modelling.
 • 
Avi Chawla
3
How to Interpret Reconstruction Loss While Detecting Multivariate Covariate Shift?
Solving critical challenges of real-world model deployment.
 • 
Avi Chawla
How to Detect Multivariate Covariate Shift in Machine Learning Models?
Dealing with some real-world problems of ML models.
 • 
Avi Chawla
1
You Cannot Build Reliable Data Projects Until You Learn Data Version Control
The underappreciated, yet critical, skill that most data scientists overlook.
 • 
Avi Chawla
1
Why You Should Avoid Deploying Sklearn Models to Production?
...And here's how to make them deployment friendly.
 • 
Avi Chawla
3
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