AI Costs Keeping You Up at Night? It's time for Domino

Our data-driven world has made artificial intelligence (AI) a critical enterprise function. AI helps businesses make better decisions, improve customer experiences, and optimize operations. Generative AI amplifies those powers with creative capabilities, natural language interactions, and even flashes of reasoning. AI propels industry leaders to advance faster and enables followers to catch up and leapfrog the competition. Yet, as you think about all this promise and opportunity, one thing can bring you back to earth: your budget.

Delivering successful AI models can quickly get expensive. AI typically needs large amounts of data for model training. Companies invest in collecting, securing, and managing data at this scale. They then hire data science teams that command high salaries to analyze the data. The data scientists need robust infrastructure, including GPUs, to be productive. To simplify matters, many turn to cloud services. Since this cloud infrastructure is in high demand, you end up paying high prices. And this is before we even consider the extreme needs of GenAI projects; GPT4 was trained on thousands of GPUs at an estimated cost of $100M. So, how do you balance innovation and budgetary discipline?

Many data and analytics leaders grapple with this issue. As the leading enterprise AI platform, Domino has enabled and supported numerous customer cost-reduction and ROI-focused initiatives. Over the years, we collected these best practices and lessons learned. We liked them because they were actionable and delivered meaningful savings. Domino’s product team even implemented some into our platform. We are now happy to offer these learnings in Domino's new 'Definitive Guide to Cost-Effective AI.'

The guide groups these AI cost-savings and ROI into core areas:

• Cost Controls: Gain insight into concrete actions to help your company save money by reducing waste and other bad habits in your cloud consumption. Paying for multiple clouds and platforms, using GPUs for even mundane tasks, and moving data around - are all things you can and should avoid. The guide will tell you what you need and how.
• Team Productivity Optimization: To help your data scientists make the most impact, you need to keep them working. In reality, they waste too much time waiting. They wait for infrastructure. They wait to get access to the data you want them to process. They even wait for help deploying their valuable models. This guide will show you precisely how to get team output soaring and deliver models to users faster than ever.
• Improved Governance: Having control over data science activities goes beyond spending. Governance allows you to ensure everyone - from business stakeholders to legal and IT - is on the same page. That helps you avoid delivering outdated models and ensures everyone is aligned. Control also means knowing and tracking what AI models you have running or need an update. At the end of the day, though, good governance saves you money. Download our guide to help you understand what you need and how to get started.
• Risk Mitigation: Like any new technology, AI introduces risk. Ignoring it can cost you a lot. Regulatory and privacy violations are emerging as headlines and cautionary tales everywhere, and nobody wants to be the subject of these. Beyond legal headaches, a bad model or hallucination can destroy your reputation. And that's before we consider the financial damage from an outdated model.

If this sounds like a lot, it is. The good thing, however, is that you have many opportunities to save and get more out of your AI and data science budget. Ready to get started? Download the guide now!