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    How Domino compares to Databricks

    Domino

    Databricks

    Use on all three major public clouds
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    Connect to any data source
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    Spin up a Spark cluster on demand
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    Work with MATLAB and SAS
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    Spin up Ray and Dask clusters on-demand
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    Monitor model quality in production
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    Track and version all project assets automatically
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    Publish web apps in Shiny, Dask, or Flask
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    Deploy to the edge with NVIDIA Fleet Command
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    Use on-premises on native Kubernetes
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    "The paid platform we used briefly—a unified data analytics platform—was too reliant on Apache Spark™ and couldn’t provide the support, security, or flexibility our data engineers, data scientists, and ML engineers needed.”

    Biz Phillips
    Senior Health Data Scientist

    Case Study

    Empowering individuals to participate in better health outcomes

    Learn More

    Consider the Key Advantages of Domino

    Governance and Reproducibility

    Integrated Governance & Reproducibility

    Domino automatically tracks and versions all project assets, including data, code, software, compute, experiments, models, batch jobs, and apps. You can instantly roll back to or recreate the exact environment used to create a model to streamline audit, governance, compliance, and regulatory reporting.

    Domino Projects allow you to easily set goals, track progress, and resolve blockers. Git and Jira integration makes it easy to integrate data science into broader enterprise project processes. With Domino, your scientists collaborate with one another for improved model quality and productivity.

    software and tools

    Choice of Cutting Edge Development Tools

    With Domino you can work with any notebook or IDE, including SAS and MATLAB, with no loss of native functionality. Need more power?

    You can spin up ephemeral Spark, Ray, and Dask clusters without an administrator. Working on a complex deep learning job? You can add GPUs to any workspace with a couple of clicks – all thanks to the industry’s deepest support for NVIDIA DGX architecture and AI Enterprise.

    Domino Platform

    Runs on any Platform with Open Access to Data

    Domino runs in all major public clouds OR on-premises so you can use the computing platform that best meets your needs.

    Domino fully supports Kubernetes, including all of the major distributions: EKS, AKS, GKE, VMware, Red Hat, and Rancher. All Domino workloads run in Kubernetes today and support autoscaling for efficient use of computing infrastructure.

    You can work with diverse data from many different platforms, including relational databases, cloud databases, NoSQL databases, cloud storage, and more. Domino is data platform agnostic with connectors to a wide variety of different sources so data can remain where it is.

    Integrated Monitoring-LP

    Integrated Model Deployment and Monitoring

    Domino provides you with many deployment options, including prediction APIs, apps, and batch jobs. You can also export models as Docker images to CI/CD pipelines, AWS, or other infrastructure. Interactive apps created with Shiny, Dash, and Flask allow non-technical users to interact with models.

    Models don’t work well forever – they degrade over time. That’s why it’s critical to monitor your models in production. Domino automatically collects instrumented prediction and ground truth data so you can monitor deployed models for data drift and accuracy. You can set notifications when quality checks exceed thresholds. When a model drifts, you can easily drill down into model features to quickly modify, retrain and redeploy models.

    Make an Informed Decision

    Whitepaper
    The Pros and Cons of Spark in a Modern Enterprise Analytics Stack
    Blog
    Spark, Dask, and Ray: Choosing the Right Framework

    Domino or Databricks?

    Are you still weighing your options between a data engineering platform and a purpose-built Enterprise MLOps platform for data science? Talk to a Sales representative who can explain why Domino has been selected by over 20% of the Fortune 100.