https://github.com/rahulsamant37/daily-task
DAILY-TASK: My Learning & Development Journey repository documenting my growth in MLOps and AI Engineering. It serves as both a portfolio and knowledge base, showcasing production-ready ML pipelines and core Python development practices. With projects on MLOps, AgenticAI, each commit reflects my continuous learning and practical implementations.
https://github.com/rahulsamant37/daily-task
aws-apigateway aws-ec2 aws-lambda aws-s3 chromadb faiss-vector-database fastapi huggingface langchain-python langgraph-python mlflow uvicorn-nginx
Last synced: about 2 months ago
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DAILY-TASK: My Learning & Development Journey repository documenting my growth in MLOps and AI Engineering. It serves as both a portfolio and knowledge base, showcasing production-ready ML pipelines and core Python development practices. With projects on MLOps, AgenticAI, each commit reflects my continuous learning and practical implementations.
- Host: GitHub
- URL: https://github.com/rahulsamant37/daily-task
- Owner: rahulsamant37
- Created: 2024-11-14T15:39:06.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-27T17:36:21.000Z (about 2 months ago)
- Last Synced: 2025-03-27T18:33:20.261Z (about 2 months ago)
- Topics: aws-apigateway, aws-ec2, aws-lambda, aws-s3, chromadb, faiss-vector-database, fastapi, huggingface, langchain-python, langgraph-python, mlflow, uvicorn-nginx
- Language: Jupyter Notebook
- Homepage:
- Size: 11.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 DAILY-TASK: My Learning & Development Journey
> *"A repository that captures my growth across MLOps, AI Engineering, and Full Stack Development - where every commit is a step forward."*
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---
## 💡 About This Repository
This repository documents my learning journey and practical implementations across various domains. It serves as both a portfolio and a knowledge base, containing:
- **MLOps Projects**: Production-ready machine learning implementations
- **AI Engineering**: Cutting-edge work with Agentic AI and RAG systems
- **Core Development**: DSA practice and Python fundamentals
---
## 🗂️ Repository Structure
### 📊 MLOps
- **MLflow Projects**: Production ML pipeline implementations
- **DLMLFLOW**: Deep learning with MLflow integration
- **Model Tracking**: Experiment monitoring and version control
### 🤖 Agentic-AI
- **RAG Systems**: Implementation of Retrieval Augmented Generation
- **LangChain Integration**: Advanced language model applications
- **Tools & Utilities**: Custom AI tool development
### 💻 Python Development
- **DSA Practice**: Data structures and algorithms implementation
- **Problem Solving**: Coding challenges and solutions
- **Best Practices**: Clean code and optimization techniques
---
## 🔍 Key Projects & Implementations
### MLOps Pipeline
```python
# MLflow experiment tracking and model deployment
mlflow.set_tracking_uri("sqlite:///mlflow.db")
mlflow.set_experiment("ml-production")
with mlflow.start_run():
mlflow.sklearn.log_model(model, "model")
```
### Agentic AI Systems
```python
# Advanced RAG implementation
from langchain_core.prompts import PromptTemplate
from langchain_community.vectorstores import FAISS
# ...implementing intelligent document retrieval
```
---
## 🛠️ Technology Stack
```
┌──────────────┐
│ AI/ML │
┌────────┐ │LangChain │ ┌──────────┐
│Data │ │MLflow │ │Tools │
│Python │───│HuggingFace │───│Jupyter │
│SQL │ │Transformers │ │Git │
└────────┘ └──────────────┘ └──────────┘
│ Frameworks │
│ FastAPI │
│ Sklearn │
│ PyTorch │
└──────────────┘
```
---
## 📈 Learning Progress
- **MLOps**: Implementing production-grade ML pipelines
- **AI Engineering**: Building advanced RAG systems and agentic AI
- **Python**: Mastering DSA and backend development
- **Best Practices**: CI/CD, testing, and documentation
---
## 🎯 Current Focus Areas
1. **MLOps Excellence**
- Model versioning and deployment
- Experiment tracking
- Pipeline automation
2. **AI Engineering**
- Advanced RAG architectures
- LLM integration
- Custom tool development
3. **Core Development**
- System design
- Clean code practices
---
## 🤝 Connect & Collaborate
I'm always interested in discussing:
- ML/AI implementations
- Production system architecture
- Best practices in software engineering
📧 Reach out at: [[email protected]](mailto:[email protected])
🔗 LinkedIn: [linkedin.com/in/rahul-samant-kb37](https://www.linkedin.com/in/rahul-samant-kb37/)
---
*"Building tomorrow's solutions, one commit at a time."*