Microsoft Azure is a great cloud computing option for enterprise workloads. Azure Machine Learning is a good tool for developers building applications on the Azure platform.
Domino runs natively on Azure Kubernetes Service (AKS). It connects seamlessly with Azure data sources such as Azure Data Lake Storage. Domino delivers best-in-class reproducibility, collaboration, and project management tools, for machine learning you can trust
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 auditing, governance, compliance, and regulatory reporting.
Native project management capabilities allow you to easily set goals, track progress, and resolve blockers. Sync code and project status with Git and Jira to integrate data science into broader enterprise project processes. With Domino, your scientists collaborate with one another for improved model quality and productivity.
Choice of Cutting Edge Development Tools
Compute Environments in Domino allow data scientists to customize the packages and tools they use to unlock innovation with sandboxes that can be secured, audited, and shared.
Domino is open and flexible with support for more data science development tools, not just Jupyter. Domino supports Jupyter, JupyterHub, RStudio, SAS, MATLAB, VS Code, and more. Plus, Domino can access data wherever it lives, including Amazon RedShift, Google BigQuery, Azure Data Lake, Databricks, Snowflake, and many others.
On-demand distributed compute clusters in Spark, Ray, Dask, and MPI let data scientists speed up computationally intensive work by a factor of 10 to 100 or more.
Runs on any Platform with Open Access to Data
Domino runs in all major public clouds, on-premises, and in hybrid- and multi-cloud environments so you can choose 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 open-source Rancher. All Domino workloads run in Kubernetes today. And Domino supports 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 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
Forrester TEI Report
Forrester conducted an in-depth study to measure costs against the value Domino’s enterprise customers realize for their business. Here’s a look at what they found.
2022 Gartner Market Guide for DSML Engineering Platforms
Making IT the Hero of Data Science