Releases

MLflow AI Gateway provided a unified interface for routing requests to multiple commercial and open model providers.

Databricks introduced automated and human evaluation tooling for assessing generative-AI quality on enterprise data.

Databricks announced managed vector search integrated with the lakehouse for retrieval-augmented generation and semantic search.

Databricks introduced an AI assistant for generating, explaining and repairing SQL and Python with organizational context.

Databricks launched Lakehouse AI, later branded Mosaic AI, as an integrated platform for building generative-AI applications on enterprise data.

MosaicML released the MPT-30B base, instruct and chat models with commercial-use terms.

MosaicML released MPT-7B, Base, Instruct, Chat and StoryWriter variants under commercially usable licenses.

Databricks agreed to acquire data-governance company Okera to strengthen governance and AI security capabilities.

Databricks released Dolly 2.0 with commercial-use weights and the human-generated databricks-dolly-15k instruction dataset.

Databricks released Dolly, a six-billion-parameter instruction-following model that could be trained inexpensively on one machine.

MosaicML released MosaicBERT, an optimized BERT architecture designed to pretrain high-quality models at much lower cost.

MLflow 2.0 expanded evaluation, pipelines and deployment workflows for production machine learning.

Databricks closed a $1.6 billion Series H with participation from AWS, CapitalG and Microsoft at a $38 billion post-money valuation.

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.

Databricks and Google Cloud launched Databricks on Google Cloud, completing availability across the three major clouds.

Databricks raised $1 billion led by Franklin Templeton at a $28 billion post-money valuation.

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

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

Databricks raised $400 million in Series F financing at a $6.2 billion valuation.

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

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

Databricks raised $250 million led by Andreessen Horowitz, valuing the company at $2.75 billion.

Databricks introduced MLflow as an open-source platform for managing the machine-learning lifecycle.

Microsoft and Databricks announced Azure Databricks, a first-party Apache Spark analytics service on Azure.

Databricks raised $140 million in a Series D led by Andreessen Horowitz.

Databricks raised a $60 million Series C led by New Enterprise Associates.

Co-founder Ali Ghodsi became chief executive officer of Databricks.

Databricks raised $33 million to expand its cloud platform for large-scale data processing.

Andreessen Horowitz led Databricks' first institutional financing to commercialize Apache Spark.

Databricks was founded by seven University of California, Berkeley researchers who helped create Apache Spark.