Releases

Scale acquired real-time video-understanding startup Helia AI and brought its team and infrastructure expertise into the Nucleus platform.

Scale raised $155 million in a Series D led by Tiger Global at a post-money valuation above $3.5 billion.

AWS introduced machine-learning-based anomaly detection for business and operational metrics.

AWS introduced an end-to-end condition-monitoring system using sensors and machine learning.

AWS introduced predictive maintenance from industrial sensor data.

AWS introduced a computer-vision service for visual defect and anomaly detection.

AWS introduced an ML-powered service for detecting abnormal operational behavior and recommending remediation.

AWS introduced pretrained models and solution templates to accelerate machine-learning deployment.

AWS announced its first custom chip purpose-built for high-performance machine-learning training.

AlphaFold 2 demonstrates accuracy competitive with experimental structures at CASP14, marking a major protein-folding breakthrough.

Transformers v4 began its release cycle with broad framework and architecture support and a standardized model API.

NVIDIA introduced a deskside AI system with four A100 GPUs for workgroups.

NVIDIA doubled A100 accelerator memory to 80GB using HBM2e.

Facebook AI released M2M-100, a multilingual machine-translation model translating directly between 100 languages.

Baidu opened Apollo Go autonomous ride-hailing to public users in Beijing.

NVIDIA introduced Maxine, a cloud-native suite of GPU-accelerated AI video-conferencing and streaming software.

Palantir began trading on the New York Stock Exchange through a direct listing, marking its transition to a public company.

Scale released the core Nucleus workspace for dataset visualization, search, curation, collaboration, and model-error analysis.

Scale launched Nucleus as a dataset-management and model-debugging workspace for visualizing, curating, searching, and collaborating on machine-learning data.

Scale introduced a self-serve, pay-as-you-go product for receiving production-quality labels and instruction feedback in as little as one hour.

Microsoft spins Xiaoice into an independent company while retaining an investment interest.

Scale launched a mapping product for computer-vision and autonomous path-planning workflows.

Hugging Face began its hosted inference platform with a serverless API for models on the Hub, later expanding into dedicated endpoints and provider routing.

Hugging Face introduced a hosted API for running models from the Hub without managing serving infrastructure.

Databricks acquired open-source visualization company Redash to expand SQL analytics and collaboration.

Facebook AI introduced wav2vec 2.0 for self-supervised speech learning with limited labeled data.

OpenAI launched a general-purpose API for accessing its language models.

Microsoft Research introduces DeBERTa, improving Transformer language models with disentangled attention and enhanced mask decoding.

OpenAI published GPT-3, demonstrating few-shot learning at 175 billion parameters.

Hugging Face launched hosted dataset repositories alongside its Datasets library, creating a shared catalog for reusable machine-learning data.

Hugging Face released a unified open-source library and catalog for loading, processing, streaming, and sharing datasets.

NVIDIA began shipping DGX A100, an integrated 5-petaflop AI system with eight A100 GPUs.

NVIDIA launched the A100 Tensor Core GPU, its first Ampere data-center accelerator for training and inference.

Hugging Face made model repositories a central Hub product for versioning, documenting, discovering, and reusing pretrained models.

Hugging Face expanded from an NLP library into a Git-based collaboration platform for sharing models and machine-learning artifacts.

OpenAI released Jukebox, a neural network that generated music including rudimentary singing as raw audio.

AWS and Facebook jointly released TorchServe, an open-source model-serving library for PyTorch.

Hugging Face introduced its high-performance Rust-backed Tokenizers library for production-scale text preprocessing.

Microsoft open-sources DeepSpeed with the ZeRO optimizer to reduce the compute and memory cost of large-model training.

Oracle launched a managed collaborative environment for building, training and deploying machine-learning models.

Microsoft introduces Turing-NLG, a 17-billion-parameter generative language model and, at the time, the largest published language model.

Databricks articulated the lakehouse architecture, combining data-lake openness with data-warehouse management and performance.

Foundry ML capabilities became available for operational machine learning.

Model Studio became available for developing and operationalizing machine-learning models.

Foundry added managed coding environments for data science and software work.

Baidu released its open NLP development library for PaddlePaddle.

CoreWeave established a managed, multi-tenant Kubernetes cloud running directly on bare metal for GPU-accelerated machine-learning and inference workloads.

Scale began supporting U.S. defense and national-security AI programs, extending its data infrastructure into government missions.

Hugging Face raised $15 million in a Series A led by Lux Capital to expand its open-source natural-language-processing platform.

AWS launched automated model building with full visibility into generated models and notebooks.