Practical Machine Learning in Real-World Applications #204

Title of the talk

Practical Machine Learning in Real-World Applications

Description

Hi,

I would like to propose a talk for upcoming event.

🧠 Topic:

Practical Machine Learning in Real-World Applications

🎯 What I’ll Cover:
How machine learning works beyond theory
Real-world project examples (classification, prediction)
Common challenges in ML projects (data quality, overfitting, deployment issues)
Tools & technologies used (Python, scikit-learn, etc.)
Tips for beginners to get started in ML
💡 Why This Talk:

Many people learn ML theoretically but struggle to apply it practically. This talk will bridge that gap by sharing real-world insights and practical approaches.

⏱️ Duration:

~20–30 minutes

Looking forward to contributing and sharing my experience!

Thanks 🙌

Table of contents

Built an end-to-end real-time fraud detection pipeline for a fintech client processing 2.3 million transactions daily. The existing rule-based system was generating high false
positives and missing sophisticated fraud, costing ₹4.2 crore per quarter.

Replaced it with an XGBoost model trained on 100K transactions, deployed behind a FastAPI endpoint capable of returning a fraud probability score and an approve / review / block
decision — all under 200ms. Transactions stream through Apache Kafka, are processed in micro-batches via PySpark Structured Streaming, and results are visualized on a live
Streamlit dashboard. Experiments tracked end-to-end with MLflow. The entire infrastructure is containerized with Docker Compose.

Targets met: Precision > 90%, Recall > 85%, False Positive Rate < 5%.

This covers the problem → solution → stack → outcome arc — which maps cleanly to a talk outline like:

1. The Problem (rule-based system failures)
2. The Approach (ML model + streaming pipeline)
3. The Architecture (Kafka → Spark → XGBoost → API)
4. Results & Metrics

Duration (including Q&A)

20-30 minutes

Prerequisites

No response

Speaker bio

Rushikesh Panjabrao Chavan is an AI/ML Engineer at NexGenesis with a strong foundation in Machine Learning, Deep Learning, and Generative AI. He holds a B.Sc. in Physics from
Bharati Vidyapeeth University, Pune, and has self-driven his way into building production-grade AI systems.

Core expertise:

• GenAI & LLMs — RAG systems, LangChain, Hugging Face, Prompt Engineering, Gemini
• ML/DL — Scikit-learn, TensorFlow, Keras, PyTorch
• MLOps — MLflow, Prefect, Flask, Streamlit, GCP, ChromaDB, PySpark

Hands-on projects include:

• A RAG system for research document Q&A (FinVeda RAG project visible in the directory)
• Fraud detection, Predictive Maintenance, PharmaCast — real-world ML pipelines
• CNN-based Rice Grain Classification (96% accuracy), Telecom Churn Prediction, Flipkart Sentiment Analysis

Beyond code, he's an open-source contributor to scikit-learn and Hugging Face, and has published technical articles on Medium covering language modeling and transformers.

The talk/workshop speaker agrees to

• Share the slides, code snippets and other material used during the talk

• If the talk is recorded, you grant the permission to release
  the video on PythonPune's YouTube
  channel
  under CC-BY-4.0
  license

• Not do any hiring pitches during the talk and follow the Code
  of
  Conduct