🚀 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

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🗂️ 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

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🔍 Key Projects & Implementations

MLOps Pipeline

# 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

# Advanced RAG implementation
from langchain_core.prompts import PromptTemplate
from langchain_community.vectorstores import FAISS
# ...implementing intelligent document retrieval

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🛠️ Technology Stack

             ┌──────────────┐
             │   AI/ML      │
┌────────┐   │LangChain    │   ┌──────────┐
│Data    │   │MLflow       │   │Tools     │
│Python  │───│HuggingFace  │───│Jupyter   │
│SQL     │   │Transformers │   │Git       │
└────────┘   └──────────────┘   └──────────┘
             │  Frameworks  │
             │ FastAPI     │
             │ Sklearn     │
             │ PyTorch     │
             └──────────────┘

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📈 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

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🎯 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

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🤝 Connect & Collaborate

I'm always interested in discussing:

• ML/AI implementations
• Production system architecture
• Best practices in software engineering

📧 Reach out at: rahulsamantcoc2@gmail.com
🔗 LinkedIn: linkedin.com/in/rahul-samant-kb37

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"Building tomorrow's solutions, one commit at a time."