Best learning resources for AI
OpenVINO is an open-source toolkit from Intel that streamlines the optimization and deployment of AI inference models across a wide range of Intel® hardware.
This paper introduces GPipe, a model-parallelism library designed to train large neural networks efficiently using pipeline parallelism. It partitions models across accelerators, processes micro-batches in parallel, and supports synchronous gradient updates. GPipe enables near-linear scaling with the number of devices while maintaining model quality and training stability. It achieves state-of-the-art performance in large-scale image classification (AmoebaNet) and multilingual machine translation (6B parameter Transformer), demonstrating flexibility across tasks. Its impact lies in making massive model training more practical and accessible across diverse architectures without relying on high-speed interconnects or custom model designs.
Microsoft’s high-performance, cross-platform inference engine for ONNX and GenAI models.
An Iguazio-backed open-source framework that orchestrates data/ML/LLM pipelines with serverless execution, tracking and monitoring.
This paper introduces GPT-2, showing that large-scale language models trained on diverse internet text can perform a wide range of natural language tasks in a zero-shot setting — without any task-specific training. By scaling up to 1.5 billion parameters and training on WebText, GPT-2 achieves state-of-the-art or competitive results on benchmarks like language modeling, reading comprehension, and question answering. Its impact has been profound, pioneering the trend toward general-purpose, unsupervised language models and paving the way for today’s foundation models in AI.
Open-source, node-based workflow-automation platform for designing and running complex integrations and AI-powered flows.
NVIDIA’s model-parallel training library for GPT-like transformers at multi-billion-parameter scale.
Open-source framework for building, shipping and running containerized AI services with a single command.
Netflix’s human-centric framework for building and operating real-life data-science and ML workflows with idiomatic Python and production-grade scaling.
A Kubernetes-native workflow engine (originally at Lyft, now LF AI & Data) that provides strongly-typed, versioned data/ML pipelines at scale.
reveals that language model performance improves predictably as you scale up model size, dataset size, and compute, following smooth power-law relationships. It shows that larger models are more sample-efficient, and optimally efficient training uses very large models on moderate data, stopping well before convergence. The work provided foundational insights that influenced the development of massive models like GPT-3 and beyond, shaping how the AI community understands trade-offs between size, data, and compute in building ever-stronger models.
This paper introduces GPT-3, a 175-billion-parameter autoregressive language model that achieves impressive zero-shot, one-shot, and few-shot performance across diverse NLP tasks without task-specific fine-tuning. Its scale allows it to generalize from natural language prompts, rivaling or surpassing prior state-of-the-art models that require fine-tuning. The paper’s impact is profound: it demonstrated the power of scaling laws, reshaped research on few-shot learning, and sparked widespread adoption of large-scale language models, influencing advancements in AI applications, ethical debates, and commercial deployments globally.
