Many data scientists don't know how to push ML models to production. Here's the recipe ๐
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
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.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
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
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
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.
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
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.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
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
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
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.
This week we are going to upload the video playlist on how to building Agentic Ai Projects on our YouTube channel.
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YouTube
DataSpoof
Hello world, it's Abhishek! I am a Data Scientist | Corporate Trainer on a mission to teach Artificial intelligence to all my students. Topics including AI, Mathematics, Science, Technology, I simplify these topics to help you understand how they work. Usingโฆ
Agentic AI #1
How to Create a flight agents
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https://youtu.be/ivt5q_pIeE4?si=7P1Z-9LHlQKlhttO
How to Create a flight agents
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https://youtu.be/ivt5q_pIeE4?si=7P1Z-9LHlQKlhttO
YouTube
How to create a Flight Agent using Phidata framework
This is the first video of agentic AI video series. In this video you will learn about how to create an AI flight Agent using Phidata Framework.
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Forwarded from Placement preparation material
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Final video reel - Made with Clipchamp.mp4
Agentic AI #2
How to create an AI agent using CrewAI
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https://youtu.be/ikDJ56k0Y9U?si=UyUkuMXOTC8kPC0l
How to create an AI agent using CrewAI
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https://youtu.be/ikDJ56k0Y9U?si=UyUkuMXOTC8kPC0l
YouTube
Learn how to create an AI Agent using CrewAI
In this video you will be learning about how to create an AI agent using CREWAI in a very simple manner.
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Feedback received for the Generative AI training from the students from University of texas
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How to create an End to End LLM application in aws
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https://youtu.be/-3Pk8KFPsB4?si=7l4An6RZGBZDowH5
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YouTube
End to End LLM Application using AWS Services
In this video we will learn about how to create an End to End LLM application using AWS Services like lambda, S3, cloudwatch, IAM and BedRock.
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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
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
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,
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,
Master Python programming in 30 days
https://www.udemy.com/course/master-python-programming-in-30-days-2025/?couponCode=7E26C0F137E5723E1E63
https://www.udemy.com/course/master-python-programming-in-30-days-2025/?couponCode=7E26C0F137E5723E1E63
Udemy
Master Python Programming in 30 days (2025)
Master Python by building real time projects. Learn data science, automation, build API, websites and databases.
Llama 4 is out beating the deepseekv3 model check out the more details
https://www.linkedin.com/posts/abhishek-kumar-singh-8a6326148_datascience-machinelearning-ai-activity-7314452968391561217-gGOa?utm_source=share&utm_medium=member_android&rcm=ACoAACOks34BFSi927IWily_2kgESJV38GQlIWQ
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Llama 4 is out beating the deepseek v3 model | Abhishek Kumar Singh
Llama 4 is out beating the deepseek v3 model
Follow Abhishek Kumar Singh to learn Python programming, data Science and big data.
#datascience #machinelearning #ai #Python #python3 #sql #deeplearning
#computervision #computerscience #programming #bigdataโฆ
Follow Abhishek Kumar Singh to learn Python programming, data Science and big data.
#datascience #machinelearning #ai #Python #python3 #sql #deeplearning
#computervision #computerscience #programming #bigdataโฆ