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Many data scientists don't know how to push ML models to production. Here's the recipe ๐Ÿ‘‡

๐—ž๐—ฒ๐˜† ๐—œ๐—ป๐—ด๐—ฟ๐—ฒ๐—ฑ๐—ถ๐—ฒ๐—ป๐˜๐˜€

๐Ÿ”น ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป / ๐—ง๐—ฒ๐˜€๐˜ ๐——๐—ฎ๐˜๐—ฎ๐˜€๐—ฒ๐˜ - Ensure Test is representative of Online data
๐Ÿ”น ๐—™๐—ฒ๐—ฎ๐˜๐˜‚๐—ฟ๐—ฒ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ฃ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ - Generate features in real-time
๐Ÿ”น ๐— ๐—ผ๐—ฑ๐—ฒ๐—น ๐—ข๐—ฏ๐—ท๐—ฒ๐—ฐ๐˜ - Trained SkLearn or Tensorflow Model
๐Ÿ”น ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—–๐—ผ๐—ฑ๐—ฒ ๐—ฅ๐—ฒ๐—ฝ๐—ผ - Save model project code to Github
๐Ÿ”น ๐—”๐—ฃ๐—œ ๐—™๐—ฟ๐—ฎ๐—บ๐—ฒ๐˜„๐—ผ๐—ฟ๐—ธ - Use FastAPI or Flask to build a model API
๐Ÿ”น ๐——๐—ผ๐—ฐ๐—ธ๐—ฒ๐—ฟ - Containerize the ML model API
๐Ÿ”น ๐—ฅ๐—ฒ๐—บ๐—ผ๐˜๐—ฒ ๐—ฆ๐—ฒ๐—ฟ๐˜ƒ๐—ฒ๐—ฟ - Choose a cloud service; e.g. AWS sagemaker
๐Ÿ”น ๐—จ๐—ป๐—ถ๐˜ ๐—ง๐—ฒ๐˜€๐˜๐˜€ - Test inputs & outputs of functions and APIs
๐Ÿ”น ๐— ๐—ผ๐—ฑ๐—ฒ๐—น ๐— ๐—ผ๐—ป๐—ถ๐˜๐—ผ๐—ฟ๐—ถ๐—ป๐—ด - Evidently AI, a simple, open-source for ML monitoring

๐—ฃ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐—ฑ๐˜‚๐—ฟ๐—ฒ

๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ - ๐——๐—ฎ๐˜๐—ฎ ๐—ฃ๐—ฟ๐—ฒ๐—ฝ๐—ฎ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป & ๐—™๐—ฒ๐—ฎ๐˜๐˜‚๐—ฟ๐—ฒ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ - ๐— ๐—ผ๐—ฑ๐—ฒ๐—น ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.

๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ - ๐—”๐—ฃ๐—œ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜ & ๐—–๐—ผ๐—ป๐˜๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฟ๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฐ - ๐—ง๐—ฒ๐˜€๐˜๐—ถ๐—ป๐—ด & ๐——๐—ฒ๐—ฝ๐—น๐—ผ๐˜†๐—บ๐—ฒ๐—ป๐˜

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฑ - ๐— ๐—ผ๐—ป๐—ถ๐˜๐—ผ๐—ฟ๐—ถ๐—ป๐—ด

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.
LLM + LSTM = Large Memory Models (LMMs)
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AutomatedCleaning is a Python library for automated data cleaning. It helps preprocess and analyze datasets by handling missing values, outliers, spelling corrections, and more


Features

Supports both large (100+ GB) and small datasets

Detects and handles missing values and duplicate records

Identifies and corrects spelling errors in categorical values

Detect outliers

Detects and fixes data imbalance

Identifies and corrects skewness in numerical data

Checks for correlation and detects multicollinearity

Analyzes cardinality in categorical columns

Identifies and cleans text columns
Detect JSON-type columns

Performs univariate, bivariate, and multivariate analysis

https://lnkd.in/gmaStAsp
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Let's learn about Model Interpretability

Interpretability is essential for:

Model debugging - Why did my model make this mistake?

Feature Engineering - How can I improve my model

Detecting fairness issues - Does my model discriminate?

Human-AI cooperation - How can I understand and trust the model's decisions?

Regulatory compliance - Does my model satisfy legal requirements?

High-risk applications - Healthcare, finance, judicial,
2025/07/10 15:27:39
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