Releases

Hugging Face launched AutoTrain to automate model training and fine-tuning from user-provided datasets.

Databricks introduced a managed Feature Store integrated with Delta Lake, MLflow and model serving.

Databricks introduced Unity Catalog as a unified governance layer for data and AI assets across clouds.

Databricks introduced AutoML to automatically train models while generating editable notebooks.

Google announces fourth-generation TPU pods for large-scale machine-learning training.

Google Cloud launches Vertex AI as a unified platform for building, deploying and operating ML models.

Hugging Face introduced a paid individual plan with expanded private, storage, and compute capabilities on the Hub.

NVIDIA introduced Morpheus for real-time AI-assisted threat detection using GPUs and BlueField DPUs.

NVIDIA introduced Base Command to provision, schedule and operate shared DGX SuperPOD AI infrastructure.

NVIDIA announced an enterprise software suite for virtualized AI workloads on VMware vSphere.

NVIDIA opened the Omniverse collaboration and simulation platform to beta users.

AWS introduced a HIPAA-eligible health data lake using FHIR and machine learning.

AWS introduced tools for bias detection and model explainability.

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.

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

NVIDIA doubled A100 accelerator memory to 80GB using HBM2e.

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.

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.

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

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.

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

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.

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

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

AWS launched a web-based integrated development environment for machine learning.

AWS introduced managed human-review workflows for machine-learning predictions.

AWS introduced an intelligent enterprise-search service using machine learning and natural-language queries.

AWS introduced ML-powered code review and performance recommendations.

AWS made Inferentia-powered EC2 instances generally available for machine-learning inference.

AWS introduced a generative-AI learning service and musical keyboard.

CoreWeave launched its specialized cloud business around on-demand NVIDIA GPU infrastructure for compute-intensive workloads.

NVIDIA expanded EGX with certified servers, software and ecosystem support for AI, IoT and 5G at the edge.

Databricks introduced the MLflow Model Registry for centralized model versioning and lifecycle management.

NVIDIA introduced EGX to bring real-time AI computing to edge and industrial environments.

MLflow 1.0 marked the first stable major release of the open-source ML lifecycle platform.