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Headlines · 8
- Databricks details free browser-based platform for AI courses
Databricks says its Free Edition lets students run complete data and AI courses at no cost, with individual browser-based access and no cloud contract, credit card, or local installation. The vendor says the edition is intended for learning and non-commercial use, includes fair-use compute limits, and supports collaboration for up to five people. Databricks also provides course mappings for areas including Spark, data engineering, analytics, machine learning, and AI agents.
Databricks Blog · 🔥 0 - Databricks AI Runtime launches with expanded NVIDIA collaboration
Databricks announced at GTC Berlin that AI Runtime is generally available, expanding its collaboration with NVIDIA for customizing open models with enterprise data. Developers can use NVIDIA NeMo, NGC containers, and Nemotron cookbooks to train and fine-tune models on serverless NVIDIA GPUs, then evaluate them and deploy them through Databricks Model Serving. Databricks also says AI Runtime supports distributed training, governed data access through Unity Catalog, experiment tracking with MLflow, and custom containers.
Databricks Blog · 🔥 0 - Databricks AI Runtime reaches general availability
Databricks announced the general availability of AI Runtime (AIR), a fully managed GPU training layer inside its lakehouse that combines serverless GPU capacity, developer tooling and Unity Catalog governance. Databricks says customers can run multinode training with seconds-level launch latency on RDMA-enabled GPUs, bring their own code, containers and Ray workloads, and debug through a new MLflow observability experience. The company says hundreds of companies, including Robinhood, PicPay, Merck, Flo Health, TinyFish and FactSet, already use AIR in production, though these adoption and performance claims are vendor-supplied and not independently verified.
Databricks Blog · 🔥 0 - Databricks brings NVIDIA's RFD4-Proteina protein-design model into Genesis Workbench
Databricks published guidance for deploying NVIDIA's RFD4-Proteina protein-design model through its Genesis Workbench blueprint on Databricks AI Runtime, which provides serverless NVIDIA GPU compute for supported workloads. The company says the planned integration connects model execution with research data, experiment tracking and governance, and that pipelines can be assembled with Genie Code, NVIDIA BioNeMo Agent Toolkit skills and coding-agent assistance. Databricks describes RFD4-Proteina as an NVIDIA BioNeMo open model co-developed with the University of Washington Institute for Protein Design that generates a protein's sequence and full atomic structure together and can be steered toward properties such as thermostability or binding affinity.
Databricks Blog · 🔥 0 - Databricks explains how its AI research team uses AI Runtime for GPU training
Databricks published a blog post describing how its AI Research team uses Databricks AI Runtime (AIR) to run the training and evaluation work behind models for products such as AI Functions, Genie and Smart Routing. The team says AIR provides resiliency for long-running jobs, fast job launches through its CLI and custom Docker containers, automatic MLflow experiment tracking, and governed data access via Unity Catalog, and that it supports an agent-driven "autoresearch" loop. Databricks says its models range from a few billion to nearly a trillion parameters and are trained on NVIDIA H100 or B300 GPUs, with the largest runs requiring hundreds of GPUs over days or weeks.
Databricks Blog · 🔥 0 - How FactSet cut model training and deployment from days to hours with Databricks AI Runtime
FactSet’s Text-to-Formula (TTF) system has a deceptively difficult job: take a natural-language...
Databricks Blog · 🔥 0 - Flo Health fine-tunes open models with Databricks to boost medical safety and usefulness
A Databricks blog post describes how Flo Health, a women's health app with 81 million monthly active users, built safer and more useful custom open models on Databricks AI Runtime. Flo first created an evaluation system of 26 AI judges (12 medical-safety, 6 micro-behavior, 8 usefulness) to turn medical standards into a training signal, generated more than 15,000 privacy-first synthetic questions, then ran supervised fine-tuning and DPO on 32 NVIDIA H100 GPUs using LLM Foundry and Compose-RL with MLflow tracking. Flo reports medical safety rising from 34% to about 61%, usefulness from 42% to 76%, and the share of responses meeting both from 16% to 47%, and estimates tailored models can cut inference costs by an order of magnitude versus frontier APIs; it plans to apply the pipeline to newer open-weight models such as Gemma, Qwen and Kimi.
Databricks Blog · 🔥 0 - TinyFish runs its web-native agent model on Databricks AI Runtime
TinyFish is developing a specialized web-native model for enterprise agents that work across the live web, and has consolidated its post-training, evaluation and production serving on Databricks: managed GPUs via AI Runtime, evaluation with MLflow, and deployment through Model Serving. The team started with supervised fine-tuning and is moving toward Direct Preference Optimization. On its internal go/no-go benchmark, the model completed 1,691 of 2,178 web-agent tasks for a 77.6% task-success rate, with a separate LLM judge reviewing the request, action history and final result; partial completion or incorrect answers count as failures. Over a recent 15-day period, TinyFish recorded 294,781 agent runs involving the model across 6.4 million tool steps, at an estimated serving cost of $0.088 per run and $0.0040 per tool step — covering only that model, not the full agent stack. TinyFish already serves its search reranker and query rewriter on Databricks and uses Unity Gateway to centralize access to external foundation models.
Databricks Blog · 🔥 0
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