Domino Data Lab Accelerates Model Delivery on AWSSeptember 27, 2017
Domino Data Lab, a data science platform provider, today announced general availability of its Domino Model Delivery product. Built to run natively on Amazon Web Services (AWS), this offering makes the process of deploying highly scalable production models faster and more cost effective.
Building on four years of customer success with companies including Coatue, DBRS, Eventbrite, Mashable, Monsanto, and Moody’s Analytics, Domino on AWS delivers a superior customer-centric experience spanning the full data science lifecycle. Domino Model Delivery, a new module in Domino’s platform, allows data science teams to weave new and improved models into business processes seamlessly, making a bigger impact without burdening IT or Engineering personnel. Moody’s Analytics recently licensed Domino to create a platform that accelerates the development and deployment of tailored models for its customers.
"Moody’s Analytics selected Domino and AWS to deliver our data and analytics in a way that are tailored to provide our customers with confidence and transparency, " said Jacob Grotta, Managing Director at Moody’s Analytics. "With this new platform, our clients will get access to Moody’s Analytics data, models, and expertise in a collaborative workspace that accelerates model development, validation, delivery, and maintenance. The platform enables clients to combine our modeling processes and data, with their own data and business insight, to create models that fit their unique needs."
Domino Model Delivery solves one of the most vexing problems in today’s data-driven organizations, enabling individuals and teams to rapidly deliver and monitor models as production-grade APIs or microservices without DevOps work or translation. Before, data scientists and IT had to spend months in an arduous process of incorporating models into production applications. Further, the business took on inherent risk during the translation process of re-coding models from their development environments into production, removing lineage and compliance and incurring possible errors and delays.
"Data science teams often struggle with procuring and managing the resources necessary to develop and launch models into production at scale," said Ken Chestnut, Global Segment Lead, Amazon Web Services, Inc. "Domino Model Delivery on AWS automates a lot of this heavy lifting, which improves the customer experience and accelerates time to market."
Data scientists aren’t the only ones who benefit. Stakeholders across the enterprise rally around Domino as their system of record and gain from Domino Model Delivery:
- Data scientists increase their impact by instrumenting more models into business processes, rather than remaining theoretical science projects.
- Data science managers increase the throughput of their teams by alleviating a key bottleneck and risk area in their process.
- IT maintains governance and auditability over key models while minimizing the delays and risk of translating models into "production" languages.
- Business and executive stakeholders minimize the change management burden by integrating results into existing systems and maintaining competitive edge through fast and frequent deployment of business-critical models.
"The unique challenges of building and deploying data science workloads to production make it the perfect business function for the cloud, and the trust and security that comes with AWS made them a natural choice for Domino starting four years ago," said Nick Elprin, CEO, Domino. "Domino Model Delivery validates the advantages of running Domino on AWS, streamlining model delivery for data scientists, ensuring their well-developed models are properly and quickly deployed, and turning the last marathon of model production into a 10-yard dash."
Domino’s full platform provides an elegant orchestration layer running on AWS, with functionality that supports the entire data science process, from initial exploration to maintaining production models. Under the hood, Domino directly manages resources in customers’ virtual private clouds, automating elastic compute resources, container orchestration (using Docker and Kubernetes), content revision, model delivery, monitoring, and more, while providing tools for IT to monitor and manage resource usage.