Decoding ML

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DML: 4 ways to monitor the output of any LLM to increase the accuracy of your system
3 techniques to secure any LLM's input for unwanted behavior and prompt injection. 4 ways to monitor the output of any LLM to increase the accuracy of…
 • 
Paul Iusztin
2
DML: Build & Serve a Production-Ready Classifier in 1 Hour Using LLMs
Stop Manually Creating Your ML AWS Infrastructure - use Terraform! Build & Serve a Production-Ready Classifier in 1 Hour Using LLMs.
 • 
Paul Iusztin
DML: 4 key ideas you must know to train an LLM successfully
My time series forecasting Python code was a disaster until I started using this package. 4 key ideas you must know to train an LLM successfully.
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Paul Iusztin
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DML: Build & Serve a Production-Ready Classifier in 1 Hour Using LLMs
 • Paul Iusztin
DML: How to add real-time monitoring & metrics to your ML System
 • Paul Iusztin
DML: Top 6 ML Platform Features You Must Know to Build an ML System
 • Paul Iusztin
2
DML: 4 key ideas you must know to train an LLM successfully
 • Paul Iusztin
2
PI #007: Airflow - One Piece of Infra All ML Pipelines Must Have
 • Paul Iusztin
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DML: How to add real-time monitoring & metrics to your ML System
How to easily add retry policies to your Python code. How to add real-time monitoring & metrics to your ML System.
 • 
Paul Iusztin
DML: Top 6 ML Platform Features You Must Know to Build an ML System
Why serving an ML model using a batch architecture is so powerful? Top 6 ML platform features you must know.
 • 
Paul Iusztin
2
Decoding ML #013: Build a CI/CD Pipeline Using Github Actions & Docker in Just a Few Lines of Code.
Build a CI/CD pipeline using GitHub Actions and Docker. 3 Simple Tricks to Evaluate Your Models with Ease.
 • 
Paul Iusztin
Decoding ML #012: This Is My Favorite Software Design Pattern You Must Know
My Favorite Software Design Pattern That You Must Know as an MLE. Unify Batch and Streaming ML Pipelines.
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Paul Iusztin
Decoding ML #011: My Ideal ML Engineering Tech Stack
Supercharge Your ML System: Use a Model Registry. My Ideal ML Engineering Tech Stack.
 • 
Paul Iusztin
Decoding ML #010: Should You Even Know How Diffusion Models Work?
Part 2: Learn How to Train and Control a Diffusion Model With Your Favorite Prompt
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Paul Iusztin
PI #009: Let's Open the Stable Diffusion Black Box
Part 1: How Does a Stable Diffusion Model Learn to Generate Images?
 • 
Paul Iusztin
2
PI #008: Your Data Validation Guide in Under 3 Minutes
A wise man said: data validation shouldn't be hard & you should validate everything!
 • 
Paul Iusztin
PI #007: Airflow - One Piece of Infra All ML Pipelines Must Have
The Benefits of Using Airflow. How to Integrate Airflow in Your Python Code.
 • 
Paul Iusztin
Decoding ML
Decoding ML
🤖 Level up in designing and productionizing ML systems using MLOps good practices.
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