AI/ML Developer Stack
A practical open-source stack for ML engineers focused on efficient model training, experimentation, lightweight serving, and performance analysis. This stack prioritizes proven Python-first tools commonly used in real-world AI workflows.
Tools in this Stack
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Web-based environment for interactive and reproducible computing. ([Demo](https://mybinder.org/v2/gh/jupyterlab/jupyterlab-demo/try.jupyter.org?urlpath=lab), [Source Code](https://github.com/jupyterlab/jupyterlab/)) `BSD-3-Clause` `Python/Docker`
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
GUI and Commandline tool from Intel for finding and fixing performance bottlenecks in software written in C/C++, C#, Java, and more.
Why This Stack Works
This stack is centered around PyTorch as the core deep learning framework, with PyTorch Lightning adding structure, reproducibility, and scalable training patterns that are widely adopted in production ML teams. Transformers complements this by providing battle-tested implementations for NLP and multimodal models, dramatically reducing time-to-train for modern AI workloads. JupyterLab serves as the experimentation and research hub, enabling rapid iteration, visualization, and debugging during model development. For serving and demoing models, Gradio offers a simple yet powerful way to expose trained models as web apps or internal tools without heavy infrastructure overhead. While the stack lacks a native vector database or full experiment-tracking platform due to tool availability constraints, Intel VTune Profiler fills a critical gap by enabling low-level GPU and performance monitoring. In environments where more advanced MLOps is required, this stack pairs well with external tools like MLflow, Weights & Biases, or dedicated vector databases when available.