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321 real-world gen AI use cases from the world's leading organizations

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Everyone knows about LLM aka Large Language model.

Now we will talk about SLM aka Small Language model

As their name implies, SLMs are smaller in scale and scope than large language models.

Some examples of SLM are
- Phi 3.5
- tiny Llama
- mobile Llama
- Gemma2

SLMs can be trained using two main techniques:

Knowledge distillation: A smaller model learns from a larger, already-trained model

Pruning: Extra bits that aren't needed are removed to make the model faster and leaner

Here are some characteristics of SLMs:

Smaller in size: SLMs have fewer parameters than LLMs, often in the tens to hundreds of millions, compared to billions in LLMs.

More efficient: SLMs are more computationally efficient and can run on less powerful hardware.

Faster training: SLMs can be trained and developed faster than LLMs.

Specialized: SLMs are trained on curated data sources and can be specialized in specific tasks.

Fine-tunable: SLMs can be fine-tuned to do exactly what is needed for a specific task.

Cost-effective: SLMs can be more cost-effective than LLMs, making them a good option for integrating intelligent features when resources are limited.

AI Agents are about to change everything—and it’s happening now.

Here’s the cheat sheet:
1️⃣ Agentic RAG Routers: Think of them as traffic controllers for your workflows.
2️⃣ Query Planning RAG: Perfect for making tasks super efficient.
3️⃣ Adaptive RAG: Always learning, always improving.
4️⃣ Corrective RAG: Spotting and fixing errors before they derail you.
5️⃣ Self-Reflective RAG: Basically, AI journaling to improve itself.
6️⃣ Speculative RAG: Solving problems before you even know they exist.
7️⃣ Self Route RAG: Dynamic workflow magic.

List of 500+AI Agent projects/UseCases

https://github.com/DataSpoof/500-AI-Agents-Projects

How to make real time stock market data processing pipeline using AWS Lambda and kinesis

Complete video is available on YouTube. Like and subscribe to our YouTube channel for such content.

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🔥 BREAKING: OpenAI Launches Operator: The Future of AI Automation

OpenAI has introduced Operator, an AI agent that can complete tasks on its own using a web browser. It’s designed to make work easier by handling tasks for you.

Operator is powered by the new Computer-Using Agent (CUA) model. It combines GPT-4o's vision with advanced reasoning, allowing it to see, click, type, and interact with websites just like a person. No special integrations are needed.

⭐️Want an open source version of OpenAI's Operator?

There's a great open source project called Browser Use that does similar things (and more) while being open source

Allows you to plug in any model you want

Love to see open source leading the way🚀


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Complete Data Preprocessing video is available on our YouTube channel.

It contains two things
1- Checking the quality of data
2- Doing data cleaning

Steps for checking the quality of data

1- Check the data manually
2- Check for the incorrect data types
3- Check for the spelling errror in the column names
4- Check for the spelling error in the categorical column values
5- Chcek for the negative values in the numerical column
6- Check for the missing values
7- Check for the duplicates values
8- Check for the outliers in the numerical column
9- Check for the data imbalance in the target column
10- Checking for the skeweness in the numerical column
11- Checking for multicollinearity
12- Checking for Cardinality in the categorical columns
13- Encoding the categorical column

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Learn how DeepSeekv3 cause stock market to crash

Complete Exploratory data analysis in python.

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How to perform statistical data analysis in Python.

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How to perform Inferential statistics in Python

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