Operational AI Company Verta aims to simplify the delivery of AI and machine learning models at scale for a smarter future. Verta recognizes that our daily digital experiences – image recognition software, voice assistants and a whole host of chatbots, powerful computers in our pockets and in our hands via smartphones and all their capabilities, etc. – increasingly rely on both AI and ML models to function.
That said, while the tools for building practical AI and ML models are becoming more mature, scalable, and robust, the processes by which these models become operational are new, relatively speaking. In Verta’s words, many are “fragile” and not yet in “prime condition.” Additionally, between the challenges of managing model and metadata versions (as well as packaging and deploying models into existing systems), maintaining model observability throughout lifecycles and Harnessing nuanced AI and ML in production can be a frustrating struggle for SMBs and global enterprises. look alike.
So, Verta creates software for data science, ML, and AI product teams at high speed. By solving problems related to model management (tracking, versioning and auditing models used across products), Verta provides solutions for the entire AI and ML lifecycles, from experiment tracking and production log to deployment, inference, serving and monitoring.
So that’s Verta in a nutshell.
Now let’s talk about Verta Insights.
As the practical research arm of Verta, Verta Insights conducted a study that lasted between last March and April. This 2023 AI Regulations Study surveyed more than 300 AI and ML practitioners to, in Verta Insights’ own words, “compare awareness of current and upcoming regulations covering AI, as well as as the readiness levels of businesses to comply with AI regulatory requirements.” “Responsible AI” and ML model transparency. » He also touched on the lineage of data and models, and even a host of what he considers “widespread concerns” about generative AI (e.g. ChatGPT) and the increased urgency that individuals and organizations feel about AI regulation.
The full study will be linked at the end of the article, but here are the main takeaways, in terms of recap.
Today, few companies are considered well prepared to meet current or future AI regulatory requirements.
Companies that adopt advanced AI/ML models are considered to have a “more mature advantage” with respect to pending regulatory actions.
“Companies typically respond to regulatory pressures on a predictable curve, where we see leaders and fast followers making substantial early investments in the type of people, processes and technology needed for compliance,” said Rory King, head from Verta Insights Research and Verta Go- to the market. “Laggards and later adopters, on the other hand, tend to wait until a regulation is imminent or takes effect before preparing to comply. This means that leaders are ready to comply from day one when regulations come into effect, while laggards do not proactively take initiatives and often have to scramble to comply, resulting in additional costs, loss of business or even legal risks.
Overall, while there is no single definitive “moral” here (given what has yet to change in the world of AI), it is still wiser to invest in more mature AI readiness as regulatory action looms and new advancements in AI and ML are on the horizon as hundreds of large companies integrate them into competing technologies that will determine the direction of today’s markets.
Edited by Greg Tavarez
