AI Infra2015
Builds and deploys machine learning models across research, production, web, mobile, and edge environments. Its ecosystem spans Keras, TFX, LiteRT, TensorFlow.js, datasets, model hubs, and visualization tools.
AI Deploy2016
Converts trained PyTorch, TensorFlow, and ONNX models into GPU-tuned inference engines via layer fusion, kernel auto-tuning, and reduced precision. Cuts latency, raises throughput on NVIDIA GPUs from Turing (INT8), with FP8 on Ada+ and FP4 on Blackwell+.
AI Infra2017
Defines a portable model format and operator set for moving trained machine learning models across frameworks, runtimes, and hardware targets without locking the model to one toolchain.
AI Infra2017
Scales any Python or ML workload across CPUs and GPUs with a few decorators, instead of rewriting code for Spark or MPI. Bundles libraries for distributed training, hyperparameter tuning, RL, batch inference, and online model serving on one cluster.
AI Client2018
Turns a top-to-bottom Python script into an interactive web app: each widget interaction reruns the whole script, with cache decorators skipping redundant work. No callbacks or HTML needed; built for data dashboards, ML demos, and internal tools.
AI Deploy2018
Serves machine learning and deep learning models for cloud, data center, edge and embedded environments. Supports multiple frameworks and backends, dynamic and sequence batching, HTTP/gRPC APIs, Docker deployment and NVIDIA-optimized runtimes.
AI Deploy2018
Converts, quantizes, and runs deep learning models from PyTorch, TensorFlow, ONNX, and PaddlePaddle across Intel CPUs, GPUs, and NPUs without the training framework. Adds a GenAI pipeline for LLMs plus Hugging Face, vLLM, and LangChain integrations.
AI Deploy2019
Turns Python ML code into production inference APIs that scale on Kubernetes or any cloud. Bundles models, dependencies, and serving logic into versioned "Bentos" with autoscaling, scale-to-zero, and multi-GPU serving for LLMs and custom models.
AI Infra2020
An AI-native, weight-centric infrastructure for quantitative trading that produces target portfolio weight vectors to unify data ingestion, strategy composition, backtesting, and live/broker execution. Modular pipeline supports ML/DRL allocators, LLM-ready preprocessing, multi-source data, and Alpaca integration for paper/live trading.
AI Deploy2021
Deploys trained SavedModels behind gRPC and REST endpoints, with hot-swappable versioning so new weights load without downtime. Built around servables, loaders, sources, and a manager, plus request batching to cut accelerator cost.
AI Infra2021
Runs, manages, and scales AI workloads across 20+ clouds, Kubernetes, Slurm, and on-prem from one YAML or Python spec. Auto-provisions GPUs/TPUs, fails over across regions and providers when capacity is short, and routes jobs to the cheapest option.
AI Deploy2021
Serves predictive and generative ML models on Kubernetes via a single InferenceService CRD, with scale-to-zero, canary rollouts, and an OpenAI-compatible LLM path on vLLM. One autoscaling abstraction over PyTorch, XGBoost, ONNX, and HuggingFace.