Architecting the ML Research Experiment | Reproducible & AI-Assisted Machine Learning Workflow

In this free masterclass, Architecting the ML Research Experiment, we explore how modern machine learning research goes far beyond simply running code.

True research is about building a reproducible discovery engine — a structured, scientific workflow that ensures clarity, rigor, and integrity.

In this session, you will learn how to architect an end-to-end ML research workflow:
Research Question → Hypothesis → Experiment Design → Tracking → Analysis

🔍 What You’ll Learn:
Clean & Modular Experiment Design using PyTorch Lightning
Rigorous Metric Tracking & Artifact Management with MLflow
AI-Assisted Research Workflow using Gemini AI for:
Ethical code generation
Debugging & logic verification
Hypothesis refinement

🤖 Special Focus: AI-Assisted Research
Writing boilerplate research code faster with LLMs
Ethical prompt engineering for debugging and validation
Maintaining scientific integrity while using AI tools

🧰 Tech Stack Covered:
PyTorch · PyTorch Lightning · MLflow · Gemini AI

🎯 Session Highlights:
Live architectural demo
Building a thesis-ready ML research template from scratch

👨‍🏫 Mentor:
Masum Bhuiyan
Mentor, bongoDev | Lecturer, Jahangirnagar University

📌 This recording is perfect for students, researchers, and ML practitioners who want to design scalable, reproducible, and AI-augmented research workflows.

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