3 AI Moves Smart Companies Get Right
Artificial intelligence leaders get three moves right when it comes to creating outcomes: priorities, people and platforms. That’s according to Nick Elprin, co-founder and CEO of machine learning/AI platform Domino Data Lab at this month’s Rev4 conference.
Priorities may seem like an obvious one, but companies do get it wrong, he said.
“Too many companies make the mistake of starting with some interesting data set they have or some trendy or novel new technique or algorithm, and they ask what can I do with this?” Elprin said. “In contrast, AI leaders working backwards, they start from a strategic objective or a business goal and they ask how can AI help me achieve this.”
Surprisingly, many companies also don’t talk about KPIs or business goals, he added — instead, many seem to view it as a shiny new toy without having clarity around how it will help their businesses, he said.
People and Platforms
Once there’s clarity around priorities, AI leaders build their talent strategy around a core of professional data scientists.
“That doesn’t mean that everyone has to be a Ph.D. in computer science, but what it does mean is that you need people inside your organization who have the expertise and the knowledge and a sound fundamental understanding of the methods and techniques involved in this type of work,” Elprin told audiences.
He shared customer testimonies about Domino’s support for collaboration across people and — perhaps more importantly to programmers and data scientists — different programming languages, including Python and R. He also predicted that a new wave of advanced AI, with its more complex models, is going to be the death knell for “citizen data scientist experiments.”
“They have a wider range of unexpected failure modes and negative consequences for the model from unexpected model behavior,” he said. “So it’s going to be ineffective and risky to have citizens doing the heavy lifting and building operators models.”
The third step is to empower them with technology and platforms for operating AI, he added.
“It [AI] is unlike anything that most businesses have had to build or operate or manage in the past, and it has some important implications for the kinds of technology you need to empower enable this sort of work,” he said.
How Domino Data Lab Differentiates
Domino Lab has built a business model on the premise of a purpose-built system. It handles the infrastructure and integration pieces, allowing a data scientist to start with a smaller footprint and then scale up — whether that means more GPU, CPU or whatever — as needed, without rebuilding. That’s one way it differentiates itself from the big cloud providers, who focus on compute and use proprietary platforms. It primarily competes against these cloud providers, custom solutions and, to some extent, the SAS Institute.
The company announced a number of new capabilities at its Rev4 conference in New York, starting with Code Assist for hyperparameter tuning of foundation models. Ramanan Balakrishnan, vice president of product marketing demoed deploying a new chatbot. He shared how experiment managers can enable automatic login of key metrics and artifacts during normal training to monitor the progress of AI experiments, including model training and fine-tuning. Domino Data Lab has also added enterprise security to ensure only approved personnel can see the metrics, logs and artifacts.
The summer release, which will be available in August, also includes advanced cost management tools. Specifically, Domino introduced detailed controls for actionable cost management. Balakrishna also introduced Model Sentry, a responsible AI solution for in-house generative AI. One aspect of Model Sentry that will be of interest to international companies is that it supports the training of models using on-premise GPUs, so data isn’t moved across borders, he said.
Domino Cloud will now include Nexus support. Users can now use a fully-managed control plane in the cloud with single-pane access to private hybrid data planes, including NVIDIA DGX clusters. Finally, Domino has a new Domino Cloud for Life Sciences, which incorporates an audit-ready specialized AI cloud platform with a Statistical Computing Environment to address the unique needs of the pharmaceutical industry.
“It’s fair to say that now we live in a new era of AI,” Balakrishna said.
Domino Data Lab paid for The New Stack’s travel and accommodations to attend the Rev4 conference.
