AI Infra2018
Rust-native, event-driven trading platform for backtesting and live execution across crypto, forex, equities, and futures on 27+ venues. The same strategy code runs in nanosecond backtests and in production, giving true research-to-live parity.
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 Infra2018
Runs ONNX models faster on CPU, GPU, and NPU by routing graph subgraphs to backend execution providers (CUDA, TensorRT, OpenVINO, DirectML, CoreML). One engine serves the same model across cloud, browser, mobile, and edge, for both inference and training.
MLOps2019
Turns plain Python functions into versioned, serverless ML jobs that run unchanged locally or on Kubernetes, with built-in tracking and deployment. Its feature store derives both offline (batch) and online (real-time) serving from one definition.
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 Train2019
Turns raw PyTorch training loops into structured modules that scale from a laptop to multi-node GPUs without rewriting model logic. It handles precision, checkpointing, logging, and distributed execution while preserving PyTorch control.
AI Train2019
Implements deep RL algorithms (PPO, DQN, SAC, TD3, DDPG, C51, PPG) as standalone single-file scripts — the PPO Atari variant is ~340 readable lines. Built for research debugging and reproducibility, with W&B and TensorBoard tracking.
MLOps2019
Write ML, AI, and data science pipelines as plain Python, debug them locally, then deploy the same code to cloud compute and production orchestration unchanged. Handles dependency pinning, data versioning, and experiment tracking automatically per run.
MLOps2020
Orchestrates ML pipelines and agentic workflows authored in plain Python, no DSL required. Adds durable execution with automatic retries and crash recovery, infra-aware autoscaling, and caching so the same code runs locally and at production scale.
AI Infra2020
Optimizes distributed PyTorch training and inference for very large models with ZeRO memory partitioning, parallelism, MoE, offload, and compression. Best when GPU memory, training cost, or cluster throughput is the bottleneck.
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 Infra2020
Covers the full AI quant pipeline — point-in-time data, model training, backtesting, portfolio optimization, and order execution. Supports supervised learning, market dynamics, and RL on 20+ models, plus an LLM-based RD-Agent for factor mining.