Turbocharge hyper-personalization and omnichannel experiences

With Domino, you and your team can easily work with and share data while complying with IT protocols, data security, and governance policies.

Benefits:

• Centrally establish connections to data wherever it lives.
• Share data across teams without making copies.
• Provide data context and metadata in a unified manner within and across departments or LOBs.
• Maintain strict governance and control over sensitive data.
• Effectively manage data with integrated version control.
• Integrate with established company SSO and LDAP solutions.

Domino helps you increase the volume and effectiveness of the models and analysis supporting every line of business, creating hyper-personalization and better omnichannel experiences.

Benefit

• Data scientists and analysts easily collaborate to develop a high volume of high-quality models.
• Innovate with the latest open-source algorithms for deep learning and other advanced AI and machine learning techniques.
• Attract and retain talent, and upskill teams more effectively with an environment as familiar as their local laptop.
• One-click access to powerful machines with the most popular distributed frameworks, including Spark, Ray, and Dask, and GPUs, accelerates training workloads.

Domino lets data scientists and model validators instantly recreate the environment used to build a model or analysis.

Benefits:

• Easily validate model results prior to deployment.
• Reproduce any model with just a few clicks, regardless of the language used to create it.
• Improve data scientist and analyst productivity by automating the capture of all model assets.

With Domino, data scientists can develop, deploy, monitor, and manage models – all from a single platform. Models get into production faster with consistent deployment practices to rapidly respond to business needs, increase impact, and mitigate risks.

Benefits:

• Get models into production faster with consistent model deployment best practices and processes.
• Monitor models for data drift, quality, and other critical health statistics
• Iterate quickly (and often) to maintain high-performing production models to mitigate risks and losses.
• Integrate with Git and Jira to include data science into broader enterprise project processes.

Streamlined workflows that cut model development times from days to hours and improved collaboration among its data scientists help this Fortune 500 financial services leader enhance call center training to improve customer communications.

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