Speaker
Description
Managing computational experiments during exploratory R&D is one of the trickiest parts of data science and machine learning. Notebooks get cluttered, parameter variations are hard to track, and reproducing past runs often requires digging through old files.
In this 1-hour hands-on workshop, participants will build real machine learning and deep learning experiment pipelines from scratch using PyLabFlow—an open-source, local-first Python framework for experiment management and reproducibility.
Through guided Jupyter Notebook exercises, attendees will:
1. Set up a structured research workspace using PyLabFlow.
2. Build modular Python components for data processing, scikit-learn ML models, and PyTorch deep learning architectures.
3. Configure and run experiment pipelines (PipeLine) with automatic tracking and duplicate-run protection.
4. Save and organize output artifacts, model checkpoints, and logs cleanly under experiment namespaces.
5. Query and compare past experiment runs into Pandas DataFrames to analyze model performance across sessions.
By the end of the workshop, every attendee will have built two functional hands-on projects (a scikit-learn regression pipeline and a PyTorch neural network workflow) and will leave with a reusable blueprint for organizing their own projects 100% offline.
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Audience Takeaways:
- Practical experience building modular, component-driven ML & PyTorch pipelines.
- Hands-on mastery of local experiment tracking, SHA-256 config deduplication, and artifact management.
- Ability to query past experiment history directly into Pandas DataFrames.
Laptop & System Requirements for Attendees:
- Pre-workshop Setup: Attendees should have a Conda environment (or Python 3.10+ virtualenv) with Jupyter (JupyterLab or Notebook), PyTorch, scikit-learn, pandas, and matplotlib pre-installed.
- Session Setup: During the workshop, attendees will install PyLabFlow (pip install PyLabFlow) and load the project datasets.
- Hardware: Standard laptop (CPU is sufficient; no dedicated GPU required; Linux, macOS, or Windows/WSL2).
Interactivity: 100% hands-on coding workshop with step-by-step notebook exercises and live facilitator support.
Session author's bio
I am a Research Systems Engineer and the Founder of ExperQuick Research Infra, where I build domain-independent infrastructure for computational research. My work focuses on standardizing experiment management and reproducible workflows across AI, mathematics, computational biology, simulation, and other research domains.
I conceptualized and developed PyLabFlow (formerly PyTorchLabFlow), an open-source framework for structured experiment management that has surpassed 25,000 downloads and supported published research in the Journal of Applied Bioanalysis. My academic background includes a Master's in Data Science from the Defence Institute of Advanced Technology (DIAT–DRDO). As an advocate for reproducible science and local-first open-source research infrastructure, I regularly speak at industry events, including UbuCon India, Open Source Summit India, and AI in The New Era.
| In Person Attendance | In-person |
|---|---|
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| Level of Difficulty | Intermediate |
| Social Media | X (Twitter): https://x.com/BBEK_Anand GitHub: https://github.com/BBEK-Anand LinkedIn: https://linkedin.com/in/bbek-anand |