Plug AI’s Silent Drain: How to deliver AI cost-effectively and achieve 722%* ROI

As large enterprises begin to adopt and embrace Large Language Models (LLMs) and seek enterprise-wide AI adoption that transforms every aspect of their business, data science executives are realizing that building a large team and hiring top talent simply isn’t enough to create a broad and bottom line impact.

Most of Domino’s Fortune 1000 customers report that their data scientists are wasting up to 40%* of their time waiting for IT support, doing DevOps work, and duplicative work, instead of building ML models that impact their company’s competitive advantage, revenues, customers, and profits. Relying on an MLOps platform can improve how efficiently data science gets done, boost data scientist, IT and DevOps productivity by freeing up resources, and helping companies experiment more with AI while spending less. This ability to efficiently deliver AI at scale, in a governed, secure and repeatable way is what enterprise-grade AI will be all about in the future.

Domino’s developed a Business Value Assessment using conservative, real-world customer savings data following use of Domino, to help other customers calculate their savings and productivity gains. Domino helps customers save in three ways:

First, Domino helps turn data science teams into high-impact, profit centers by delivering double the models they do today, with the same resources.

Second, Domino helps boost data scientist productivity and eliminate the IT lift by giving data scientists their favorite self-serve tools and on-demand, elastic infrastructure, while reducing IT wait times and lift with a fully-managed SaaS.

Finally, Domino pays for itself by shrinking IT infrastructure and cloud costs by up to 40%* on average. Most customers saved $900K over three years, by using a variety of Domino’s cost control and billing features. Click this link to learn more about cost-effective AI.

Turn data science into a profit center by delivering 2x the models you do today.

In this macroeconomic climate, not everyone wants to hire more data scientists. In fact, many customers want to improve productivity instead. Domino helps customers spend less time on model validation and deployment and produce 2x more models with the same resources.

“Domino centralizes assets required to build data models, helping drive 10-100x efficiency gains and reducing the costs of ML and AI projects by $20 million a year.” – CDAO of Enterprise Operations, Lockheed Martin (Source: CIO.com article).

Once models are built, quality is an issue. Executives have to rely on models to make decisions and model drift can result in inaccuracies over time, and hinder trust. Domino customers can easily refine and improve model quality, saving weeks per model, with Domino’s turnkey model monitoring and drift detection. Refining models and reusing existing ones takes less time, while boosting accuracy.

In many large enterprises, it’s not uncommon for two different business units to be working on similar AI projects, simultaneously. Domino offers a single system of record that tracks all models at a granular level, removing duplicative work by helping everyone reuse and reproduce models with centrally-stored data and code. All of this means that data scientists are free to experiment more, build more models, and build faster by collaborating more and reusing existing work.

“By testing 5x as many potential new seeds without additional costs, and iterating on models 4x as often throughout the agricultural process, Bayer has generated more than $100 million in NPV in 3 years [by using Domino].” - Data science Center of Excellence Leader, Bayer

Boost productivity & eliminate the IT lift.

Domino’s real-world customer study demonstrated that the platform saved 16+* weeks of wasted data science work, which amounts to $74,000 per data scientist per year, simply by offering self-serve AI tools and on-demand infrastructure on one platform.

Using a self-serve data science platform also cut IT wait times by 15% , freeing up IT headcount to focus on other tasks, and freeing up data scientists to build more models, more efficiently. IT professionals also saved time by not having to accommodate new AI tools as they emerged, since the platform is future-proof and constantly being enhanced. And IT could stop building disparate stacks for each tool, since one platform served multiple needs (e.g. Python, R, Spark, etc).

“Domino makes data scientists 20% to 25% more productive, which is adding up to thousands of hours in time savings” – Sr Mgr AI & ML Lockheed Martin (Source: Wall Street Journal)

Finally, customers often report that deploying models into production faces many challenges and causes regular delays. There is often a dependency on DevOps work done by a data scientist, or DevOps professional. And infrastructure is built and torn down for each model. Domino helped one customer reallocate 80% of their DevOps IT FTEs to other, higher value-added work.

Shrink infrastructure and cloud costs by up to 40%**

It’s not surprising that most Domino customers (and most large enterprises) are paying for infrastructure assets that are idle, underutilized or over-provisioned. That’s because accurately matching existing cloud, on-premises and hybrid infrastructure resources to each data science and AI workload is a challenge. Domino helps orchestrate this in one platform and user interface that minimizes waste. Domino features built-in infrastructure management tools like intelligent workspace sizing, on-demand workspaces that automatically shutdown, and auto-scaling clusters, so waste is minimized. In addition, administrators can set hardware tier limits and storage quotas, and receive proactive billing alerts and real-time reports that track costs by project, user and workload, so they can find new savings and enforce budgets.

Lastly, many customers have existing on-premise GPUs and expensive, high-performance compute resources they own. However, data scientists don’t always use these available, lower cost resources and default to a public cloud instead. Domino Nexus lets customers and end users simply see pricing and choose the lowest cost compute clusters available in one interface - whether it’s on-premise (e.g. owned GPU hardware) or instances in their hybrid cloud, or in multiple clouds. By choosing the lowest cost instance, customers have also been able to avoid cloud computing markups (sometimes up to 20%) in some cases, and vendor lock-in.

Domino delivers real ROI

Real-world customer data provided by customers, proves that Domino’s Enterprise AI Platform unifies AI work and makes it more cost-effective by increasing the impact of data science teams and turning them into a profit center, boosting productivity so more models are produced with the same resources, and lowering infrastructure costs so more transformative AI can be delivered, into more parts of a company. Click here to learn how you can save and unleash the true potential of AI everywhere, with Domino.