List of Databricks MLflow Customers
Databricks
160 Spear Street, 15th Floor,
San Francisco, 94105, CA,
United States
San Francisco, 94105, CA,
United States
1 866-330-0121
Professional Services
8000
$4.0B
Since 2010, our global team of researchers has been studying Databricks MLflow customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Databricks MLflow for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Databricks MLflow for ML and Data Science Platforms include: University of Washington, a United States based Education organisation with 35331 employees and revenues of $7.10 billion, Wix.com, a Israel based Professional Services organisation with 5344 employees and revenues of $1.96 billion, Altair Engineering, a United States based Professional Services organisation with 3300 employees and revenues of $666.0 million, Outreach.io, a United States based Professional Services organisation with 680 employees and revenues of $200.0 million, Virgin Hyperloop, a United States based Transportation organisation with 500 employees and revenues of $153.0 million and many others.
Contact us if you need a completed and verified list of companies using Databricks MLflow, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Databricks MLflow customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
Altair Engineering | Professional Services | 3300 | $666M | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2022 | n/a |
In 2022, Altair Engineering deployed Databricks MLflow to support MLOps capabilities within its SmartWorks Analytics product and internal data science workflows. Databricks MLflow is positioned within Altair Engineering ML and Data Science Platforms to provide model lifecycle management, experiment tracking, and model packaging for the company's product management and data science teams.
Implementation emphasized experiment tracking and model registry functions of Databricks MLflow, with concrete packaging of models into MLflow Models artifacts. For serving and endpoint validation Altair integrated MLflow with Seldon Core to containerize and expose models, building Docker images and running containers on Docker Desktop. API request validation was performed using Postman to confirm inference responses from deployed endpoints.
The work was scoped to SmartWorks Analytics and adjacent MLOps workflows, with the product management and data science teams collaborating on preprocessing pipeline requirements and use case documentation. The project included researching open source preprocessing packages and documenting training versus inference implementation patterns, codifying operational steps for packaging and serving models via Databricks MLflow.
Deliverables included a coded deployment pipeline that produced a Docker image of the MLflow-packaged model and a validated model endpoint that returned predictions via API calls. The implementation produced artifacts and documentation to support continued operationalization of machine learning models within Altair Engineering.
|
 |
Brandfolder | Professional Services | 200 | $13M | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2018 | n/a |
In 2018 Brandfolder implemented Databricks MLflow as a core component of an ML platform to enable a data driven creative experience for its customers, integrating the application into a small team, constrained budget delivery model. This implementation situates Databricks MLflow within the ML and Data Science Platforms category, supporting experiment tracking, model versioning and both batch and real time serving workflows for product facing use cases.
The technical implementation used Spark based ETL to prune transactional events and store partitioned parquet files in Google Cloud Storage, forming a centralized data lake. Feature extraction was implemented with custom Spark user defined functions and persisted to a global feature store on GCS, while MLLib libraries were used to prototype and build initial models. Databricks MLflow was configured as the experiment and artifact registry, logging model parameters, metrics and artifacts to a dedicated mlflow server endpoint.
Operational architecture integrated cloud data processing on Dataproc with a Kubernetes hosted mlflow server, enabling model serving and selection of best performing experiments. Scoring paths were implemented in both batch pipelines and low latency real time flows, with each ML use case exposed via a gRPC ML service that invokes the feature UDFs, calls the model served from mlflow and returns scored results to the calling client. The deployment combines data lake storage, Spark based feature engineering, MLLib model development and Databricks MLflow driven model lifecycle management.
Governance centered on centralized experiment tracking and model versioning through Databricks MLflow, which acted as the single source for model artifacts used in production scoring. Rollout focused on embedding ML services into product workflows, with development and serving separated across Kubernetes for model endpoints and Dataproc for data processing, enabling controlled promotion of experiments to production. The narrative reflects an implementation that links the Databricks MLflow application to product engineering and data teams under the ML and Data Science Platforms function.
|
 |
Outreach.io | Professional Services | 680 | $200M | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2020 | n/a |
In 2020, Outreach.io implemented Databricks MLflow as its core ML and Data Science Platforms capability, using MLflow Projects to unify model development and deployment workflows. Databricks MLflow Projects was adopted as a lightweight, self documenting layer that centralizes and standardizes project entry points and environment definitions across the data science lifecycle.
The implementation integrated MLflow Projects alongside existing MLflow Tracking and MLflow Models components to provide end to end experiment, test, and deploy capabilities. MLflow Projects was used to provide a consistent CLI for running .py and .sh files, and to complement first class support for running Databricks notebooks while avoiding the overhead of creating Spark or Databricks jobs for script execution.
Operationally the implementation emphasized provenance and reproducibility, enabling runs from a GitHub commit without pulling code locally and preserving lineage for local uncommitted work. The MLflow Projects API was used to toggle execution from local to remote environments via a backend argument that points to a Databricks or Kubernetes JSON cluster configuration for single use operations, and dependencies were handled in code through Conda rather than manual cluster state.
Governance and process changes focused on centralizing entry points and environment definitions to improve consistency for data science and machine learning engineering teams. The approach reinforced strong provenance tracing from source code to model results and provided flexibility to prototype locally and scale to remote clusters using the Databricks MLflow implementation.
|
 |
TwelveLabs | Professional Services | 73 | $8M | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2021 | n/a |
In 2021, TwelveLabs implemented Databricks MLflow to establish a standardized model lifecycle for its Video Foundation Models within the Engineering ML Platform team based in San Francisco, United States. The deployment targeted scalable model development and operationalization workflows that support multimodal video processing use cases including image and audio extraction and inference orchestration.
Databricks MLflow was configured to provide core capabilities such as experiment tracking, a model registry for versioned artifacts, reproducible run metadata, and automated model packaging for serving. The platform was extended with end to end MLOps constructs, including automated model testing pipelines, promotion gates, and monitoring hooks to instrument model behavior and lineage for internal ML practitioners.
Integrations were implemented with container and GPU runtime ecosystems to support production inference, specifically integrating with Kubernetes for orchestration and NVIDIA Triton, ONNX, and TensorRT for optimized model serving and inference acceleration. The MLflow deployment also aligned with commonly used MLOps tooling such as Weight and Biases for experiment correlation, and it was provisioned to operate on public cloud infrastructure consistent with the teams experience deploying on AWS, Azure, and GCP.
Operational coverage focused on the ML Platform engineering group and adjacent API teams that consume model outputs to power customer facing video search and summarization services. Governance practices emphasized a controlled model promotion workflow, experiment reproducibility, and access controls in the model registry to support cross functional development, testing, and production handoffs.
Databricks MLflow serves as TwelveLabss ML and Data Science Platforms solution, providing experiment management, model registry, and deployment orchestration that support the companys video model development and ML operations functions.
|
 |
University of Washington | Education | 35331 | $7.1B | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2020 | n/a |
In 2020, University of Washington implemented Databricks MLflow within its ML and Data Science Platforms environment. The deployment targeted standardization of experiment tracking and model lifecycle management to support campus research computing and institutional data science teams.
The implementation used Databricks MLflow core capabilities, including experiment tracking, Projects for packaging reproducible runs, the Models component for artifact capture, and a centralized model registry to record metadata and versioning. Configuration integrated MLflow with Databricks notebook and compute workflows so code, metrics, parameters, and artifacts were logged consistently across research experiments.
Databricks MLflow was operated with built-in integrations for Tensorflow, PyTorch, scikit-learn, H2O.ai and Amazon Sagemaker, allowing models built in those frameworks to be tracked, registered, and prepared for downstream serving or export. Operational coverage emphasized research labs and university data science groups, aligning model development and experiment management with academic analytics and cross-disciplinary research projects.
Governance focused on reproducibility, artifact version control and registry-based model promotion workflows to enforce provenance and handoffs between researchers and productionization channels. University of Washington is also named among organizations using and contributing to MLflow, reflecting both internal use of Databricks MLflow and participation in the broader MLflow community and standards.
|
 |
|
Transportation | 500 | $153M | United States | Databricks | Databricks MLflow | ML and Data Science Platforms | 2020 | n/a |
|
|
|
|
Professional Services | 5344 | $2.0B | Israel | Databricks | Databricks MLflow | ML and Data Science Platforms | 2020 | n/a |
|
Showing 1 to 7 of 7 entries
Buyer Intent: Companies Evaluating Databricks MLflow
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Databricks MLflow. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Databricks MLflow for ML and Data Science Platforms include:
- Uber, a United States based Transportation organization with 31100 Employees
- Al Neama Holding, a Qatar based Oil, Gas and Chemicals company with 1600 Employees
- Aspect Capital, a United Kingdom based Banking and Financial Services organization with 136 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated |
|---|---|---|---|---|---|---|
 |
Uber | Transportation | 31100 | $44.0B | United States | 2026-03-24 |
 |
Al Neama Holding | Oil, Gas and Chemicals | 1600 | $200M | Qatar | 2025-11-10 |
 |
Aspect Capital | Banking and Financial Services | 136 | $65M | United Kingdom | 2025-07-10 |
|
|
Professional Services | 150 | $15M | United States | 2024-08-02 |
FAQ - APPS RUN THE WORLD Databricks MLflow Coverage
Databricks MLflow is a ML and Data Science Platforms solution from Databricks.
Companies worldwide use Databricks MLflow, from small firms to large enterprises across 21+ industries.
Organizations such as University of Washington, Wix.com, Altair Engineering, Outreach.io and Virgin Hyperloop are recorded users of
Databricks MLflow for ML and Data Science Platforms.
Companies using Databricks MLflow are most concentrated in Education, Professional Services and Transportation, with adoption spanning over 21 industries.
Companies using Databricks MLflow are most concentrated in United States and Israel, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Databricks MLflow across Americas, EMEA, and APAC.
Companies using Databricks MLflow range from small businesses with 0-100 employees - 14.29%, to mid-sized firms with 101-1,000 employees - 42.86%, large organizations with 1,001-10,000 employees - 28.57%, and global enterprises with 10,000+ employees - 14.29%.
Customers of Databricks MLflow include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Databricks MLflow customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.