Take this free 10-minute assessment and see where your data science process and lifecycle stacks up towards achieving model velocity, with suggested areas of improvement.
Learn the keys to becoming a model-driven business.
Introducing the world’s first data science book for kids!
See why Forrester Consulting projects that organizations that use Domino will realize nearly $30 million in value over a three-year period, with an ROI of 542% and a payback in less than six months.
On your journey to become a model-driven business, understand how models can turn bad and get recommendations to prevent negative brand reputation, financial loss, and an impact to your customers.
By answering a few short questions, you will receive free access to a high-level estimate of the Total Economic Impact of the Domino Enterprise MLOps Platform.
One of our most popular resources, this guide shares lessons from the field on managing data science projects and portfolios.
Progress through the data science lifecycle faster with the enterprise capabilities that are required to do this safely and universally.
Survey results from 250 data science leaders and practitioners across organizations of all sizes and a variety of industries.
Domino has partnered with DataIQ to survey data and analytics professionals to understand their approaches to data science.
See Why Forrester Named Domino a Leader in the Wave for the second time in a row.
Get recommendations to maximize the ROI of machine learning, optimize data science infrastructure, reduce risk, and increase model velocity.
Learn more about the segments and core products that comprise the vibrant, but sometimes confusing, MLOps product sector.
See why security features headline a growing list of crucial components when selecting an enterprise data science platform.
This document provides IT leaders with the top 10 questions to ask of data science platforms.
Your guide to understanding modern data science and why IT is critical to its success.
Learn how organizations unleash innovation without sacrificing transparency or governance.
According to a new survey, flawed investments in people, processes, and tools are causing failure to scale data science.
See while productivity can suffer when workbenches aren’t built to support the work of large data science teams.
Many leaders wrestle with how to organize data science teams for the greatest impact. We break down best practices for enterprise data ...
Understanding the capabilities needed for enterprise data science and the level of effort needed to build your own data science platform.
Learn about the importance of being vigilant for monitoring and mitigating data drift.
Learn about Spark’s strengths and weaknesses in the context of data science and machine learning workflows.
See why Kubernetes is a great fit for data science and quickly becoming the platform infrastructure layer for the cloud.
Learn about the milestones and best practices for digital transformation at health and life sciences organizations.
See how the Domino platform addresses governance, security, infrastructure monitoring, and other important criteria.
Discover a framework that combines sound governance with end-to-end documentation in the lifecycle of models.
Identify capability gaps and direct future investment with this model.
Learn how to wrangle data science tools and technologies, support your data scientists' compute needs in the cloud, and prevent shadow IT.
Learn how to leverage models for a competitive advantage and avoid the Model Myth.
See how to get the most out of moving data science to AWS.
On your journey to become a model-driven business, understand how models can turn bad and get recommendations to prevent negative brand ...
See why the Domino platform is used by over 20% of the Fortune 100 to centralize their data science and become a model-driven business.
Learn how Domino provides self-serve access to the most popular distributed compute frameworks – Spark, Ray, and Dask.
Create a seamless experience for model development to deployment to monitoring to ensure peak performance of production models.
IT and Analytics Leaders: Thinking too narrowly about your data science platform can prove costly.
Learn how leading organizations use Domino to manage their teams.
Domino and NVIDIA together make NVIDIA GPU resources available on the NVIDIA certified Domino enterprise data science platform.
This document provides IT leaders with answers to how Domino addresses the top 10 questions to ask of data science platforms.
This paper illustrates in three simple steps how to use SAS for Containers on Domino for SAS research and model deployment.
Accelerate SAS and save on CapEx — take it to the cloud.
Develop and deliver models faster, scale rapidly, and reduce regulatory and operational risk in the cloud.
Domino on AWS provides an efficient, scalable, and secure data science platform.
Learn how Domino helps financial institutions become model driven
Learn how Domino helps insurers become model driven
Resource bundle for current and aspiring data science leaders.
This hiring and onboarding plan template covers the key questions to help find and train new data scientists for your team.
This pre-flight project checklist walks data scientists and data science leaders through an ideal set of upfront actions.
Our most popular articles for data scientists. Learn techniques from insights, tips, and tutorials.
Hear how one of Denmark’s largest insurers is infusing models throughout its operations, and discuss what it takes to become model-driven.
Learn best practices from Tata Consultancy Services (TCS) for building a heterogeneous platform to unite analytics and AI workloads.
Apache Spark has been the incumbent distributed compute framework, but has Spark been eclipsed by new frameworks?
Learn from an expert panel about why data science initiatives fail and the importance of Enterprise MLOps.
Join this session with Forrester Consulting to see how the Domino Enterprise MLOps platform is enabling model-driven business.
See how Domino enables data scientists to easily provision a Spark/RAPIDS cluster and access it through an integrated environment.
Johnson & Johnson’s CIO Jim Swanson and Domino CEO Nick Elprin discuss building an MLOps strategy to achieve model velocity.
In <2 years, AES built out a global team, its underlying infrastructure, and they’ve built and deployed 50+ models to date.
Join data science experts from AWS and Domino Data Lab as they discuss the importance of a “single pane of glass” for model monitoring.
Watch this session to hear industry-leading CDAOs discuss all about digital transformation.
Insights from Dr. Kahneman on how his research applies to today’s analytical professionals.
Executives share first-hand experiences about the effects on people, process, and technology when centralizing data science.
See why Model Velocity is a new way to frame and benchmark the ROI of your data science team’s work.
CIOs have a unique opportunity to lead the transformation in data science. Here’s what you need to know to get started today.
How their risk modeling platform on Domino blends SAS & open source analytics software for a fast path to the cloud.
Join this exclusive session for partners to see how Domino helps enterprises deliver data science at scale.
Learn how world-leading reinsurer SCOR is delivering data science at scale with the Domino Enterprise MLOps platform.
Model ethics, interpretability, and trust are seminal issues. Understand the modern approaches for interpreting black box models.
Join Forrester analyst Dr. Kjell Carlsson and IT and data science leaders to uncover best practices that unlock data science innovation.
In this technical webinar, we address the key challenges every data science team faces when training and operationalizing complex AI models.
Today, many organizations are looking to determine the optimal way for organizing their data science talent.
What’s the most effective organizational structure for modern data science teams? Learn best practices from two long-time team leaders.
Learn how Janssen has accelerated model training on multi-GPU machines to diagnose and characterize cancer cells through image analysis.
See how Domino and NVIDIA help them bring development, security, and operations best practices to machine learning.
Understand why data science leaders struggle to organize, implement and communicate effective data science initiatives.
Learn specific techniques for breaking down silos and building a strong data science community across disparate organizations.
Experience new capabilities in Domino 4.2 via live demos and discussion.
Discover the missing piece of MRM solutions: a multi-language reproducibility engine for model development efficiency.
Topdanmark’s Head of Machine Learning discusses his team’s journey into machine learning, and how they build and deploy models.
Learn how Dell and Domino created a refreshingly simple, building-block-based approach to get data science teams up and running faster.
Learn about the GAN framework, how to implement a basic GAN model, how to generate training samples, and more.
Join this webinar to learn how Domino can help you monitor your models at scale.
See how to build a pipeline for automating ML workflows, including how to select and tune hyperparameters.
Donald Miner details the tracking of ML models in production to ensure model reliability, consistency, and performance into the future.
Dive into popular open-source and proprietary AutoML tools such as auto-sklearn, TPOT, MLBox, AutoKeras, and H2O Driverless AI.
In this webinar we’ll review the pros and cons of the top data science web app frameworks (Shiny, Dash, Flask, etc.)
This technical talk discusses traditional and modern approaches for interpreting black box models.
Learn how Domino allows data science managers to define their own data science project life cycle, and easily track and manage projects.
In this webinar, Forrester Research and Domino will demystify the model-driven business.
Relive two days of practical sessions, thought-provoking discussions, and forward-looking solutions from today’s data science leaders.
Learn how Domino 3.4 accelerates the impact of data science teams with Datasets, Experiment Manager, and Activity Feed.
The impact of data science on business and society has never been greater. But why are few organizations getting the value they expect?
Watch how SAS, Domino Data Lab, and AWS have teamed up to deliver SAS for Containers on Domino.
In this webinar, we'll share learnings and how we see the market approach challenges in data science.
Ways to incorporate GPU computing for computationally intensive tasks.
Survey the landscape of interactive visualization (Shiny, Dash, etc) tools and learn best practices for publishing apps within Domino.
Lessons on driving continuous improvement when it matters most.