Domino Data Lab Joins Google Cloud Partner Advantage Program, Giving Customers A New Way To Access Secure, Scalable And Centralized Data Science On Google Cloud

Model-driven enterprises accelerate their data science journey with team collaboration, process efficiency, and reproducibility on powerful data science management solution

Domino Data Lab, provider of the open enterprise data science management platform trusted by over 20% of the Fortune 100, announced it has joined the Google Cloud Partner Advantage Program. The Domino Data Science Platform offers an open, secure way to centralize enterprise data science efforts across silos and departments. It serves as an orchestration layer for data science research, development, and productionization of models on Google Cloud compute and storage foundations, making it easier to accelerate model development with one-click access to cloud compute. With Google Cloud, Domino is helping enterprises scale and drive value from their model-driven investments.

Customers who opt for a multi-cloud strategy benefit from Domino’s collaboration with Google Cloud. As an example, Moneysupermarket Group, a financial services brands portfolio helping consumers and businesses save money on insurance, travel, mobile services, utilities, and other expenses, relies on sophisticated data science. Machine learning models inform many of its core business functions. The Domino platform offers them the flexibility to run on their cloud providers of choice, which includes Google Cloud, while enabling their data scientists to develop models faster and more efficiently, saving each, on average, four full work days per month via fewer manual processes and less dependency on platform admins.

“We chose Domino over Databricks and Dataiku because it is designed specifically for code-first data scientists and massively reduces friction in our workflows,” said Harvinder Atwal, Chief Data Officer at Moneysupermarket. “Domino allows our data scientists to be self-sufficient and far more efficient than they would be otherwise, which results in faster time to value for models, already generating millions in incremental revenue. It has become a major part of our DataOps ecosystem, helping us reduce wasted time and enforce better control.”

“Domino was built to power the most advanced and complex data science efforts at model-driven enterprises like Moneysupermarket,” said Nick Elprin, CEO at Domino Data Lab. “Google Cloud further enhances Domino’s platform and ability to support customers in their cloud journey with greater flexibility and choice, expanding the reach of its collaboration and more efficient model development, allowing enterprises to scale data science operations at a much faster pace.”

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The combination of Google Cloud and Domino Data Lab offers enterprise customers a platform built on shared principles of security by design, openness and flexibility to deliver scalable, centralized data science. Key features and benefits of the Domino Data Science Platform on Google Cloud include:

Enhanced Collaboration & IT Insight – Data scientists readily collaborate in Domino on shared data sets, artifacts and prior work to build on existing research with governed access to the tools and scalable compute they choose. IT gains visibility, control, and oversight of resources in Domino, now with Google Cloud tools for easier management and scalability of networks and operations—enabling fine-grained networking policies, monitoring and analysis of storage and compute.
Utilization & Flexibility – Domino’s Kubernetes-native platform enables IT to dynamically partition workloads and environments, scaling up as needed for compute-intensive workloads and down again when a job is completed. Domino can be deployed onto Google Kubernetes Engine (GKE) for intelligent Kubernetes orchestration on Google Cloud for containerized workload portability.
Proactive Security and Compliance Management – IT can set access permissions across resources in Domino, including data and premium compute, with full tracking and reproducibility of experiments, meeting compliance requirements of highly regulated industries. At the infrastructure level, GKE enforces container validation and standardized release practices for deploy-time control and policy-based settings.
Streamlined DevOps – A scalable deployment platform facilitates model delivery into the hands of business decision makers. Models are developed, deployed, and maintained in one unified data science platform regardless of the tools and technologies used to build them.
Increased Efficiencies & Model Accuracy – Management and teams drive results from the workflow efficiencies and data science best practices that are enforced by the Domino platform–saving time and deploying more accurate models to production. Ultimately, accelerating time to value from model-driven investments in the enterprise.

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