Most MLOps teams have people with extensive software development skills who love to build things. But the continuous build of new AI/ML tools must also be balanced with risk efficiency, governance, and risk mitigation. Many engineers today are experimenting with new generative AI capabilities. It's exciting to think about the possibilities that something like code generation can unlock for efficiency and standardization, but auto-generated code also requires sophisticated risk management and governance processes before it can be accepted into any production environment. Furthermore, a one-size-fits-all approach to things like generating code won't work for most companies, which have industry, business, and customer-specific circumstances to account for.

AI-generated output can't be explained. This is all true, and is happening today, and there's a risk of these issues accelerating as AI adoption grows. Before the lawsuits start flowing and government regulators start cracking down, organizations using AI need to become more proactive and formulate actionable AI ethics policies. But an effective AI ethics policy requires more than some feel-good statements. It requires actions, built into an AI ethics-aware culture.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. There's no doubt that, when applied effectively, machine learning (ML) and artificial intelligence (AI) have proven potential to deliver significant value and cutting-edge technological innovation. But many organizations are struggling with the "effectively" part, according to a new survey. Despite the fact that businesses are increasingly undertaking initiatives to leverage ML and AI, many tools and projects lack appropriate resources, are far less productive than they should be, lag in deployment, and more often than not, fail or are abandoned. In short, business value is rarely captured – and very often falls short of expectations – because significant time, resources and budgets are being wasted, according to a 2021 survey of ML practitioners, "Too Much Friction, Too Little ML." "Building AI is hard," said Gideon Mendels, CEO and cofounder of Comet, the enterprise ML development platform company that commissioned the survey. "ML is often a slow, iterative process with many potential pitfalls and moving parts.

Find out how to implement AI responsibly-- Watch the recorded webinar video Responsible AI in Practice: learn about fairness, AI in the law, and AI security from experts. The use of machine learning (ML) applications has moved beyond the domains of academia and research into mainstream product development across industries looking to add artificial intelligence (AI) capabilities. Along with the increase in AI and ML applications is a growing interest in principles, tools, and best practices for deploying AI ethically and responsibly. In efforts to organize ethical, responsible tools and processes around a common collective, a number of names have been bandied about, including Ethical AI, Human Centered AI, and Responsible AI. Based on what we've seen in industry, several companies, including some major cloud providers, have focused on the term Responsible AI, and we'll do the same in this post.

Bottom Line: Verta helps enterprises track the thousands of machine learning models they're creating using an integrated platform that also accelerates deploying models into production, ensuring that models' results are based on the most current data available. The same is true for all data-intensive businesses today. Despite ramping up their data science teams and investing in the latest machine learning tools, many struggle to keep models organized and move them out of development and into production. Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform where they can be launched into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work to define the first open-source system for managing machine learning models.

More than thirty years ago, Fred Davis developed the Technology Acceptance Model (TAM) as part of his dissertation at MIT. It's one of the most widely cited papers in the field of technology acceptance (a.k.a. Since 1989, it's spawned an entire field of research that extends and adds to it. What does TAM convey and how might today's AI benefit from it? TAM is an intuitive framework.

Find out how to implement AI responsibly--join our free webinar Responsible AI in Practice on December 15 to learn about fairness, AI in the law, and AI security from experts. The use of machine learning (ML) applications has moved beyond the domains of academia and research into mainstream product development across industries looking to add artificial intelligence (AI) capabilities. Along with the increase in AI and ML applications is a growing interest in principles, tools, and best practices for deploying AI ethically and responsibly. In efforts to organize ethical, responsible tools and processes around a common collective, a number of names have been bandied about, including Ethical AI, Human Centered AI, and Responsible AI. Based on what we've seen in industry, several companies, including some major cloud providers, have focused on the term Responsible AI, and we'll do the same in this post.

The emergence of new technologies is usually accompanied with subsequent phases of expansion and contraction in the number of possible solution designs. It is no longer contentious that AI will transform many industries, often becoming a strategic advantage and even creating new "AI first" business models and companies. As a result, all major Cloud vendors (and countless startups) are piling on resources to bring AI developer tools to a broader audience, most importantly big enterprises. All of these vendors broadly attempt to solve the same user needs, but with distinctly different approaches and outcomes, leading to a proliferation in different designs. This phenomenon exists at every level of the stack and usually progresses from the bottom up.

"AI is the new electricity." At least, that's what Andrew Ng suggested at this year's Amazon re:MARS conference. In his keynote address, Ng discussed the rapid growth of artificial intelligence (AI) -- its steady march into industry after industry; the unrelenting presence of AI breakthroughs, technologies, or fears in the headlines each day; the tremendous amount of investment, both from established enterprises seeking to modernize (see: Sony, a couple of weeks ago) as well as from venture investors parachuting into the market riding a wave of AI-focused founders. "AI is the next big transformation," Ng insists, and we're watching the transformation unfold. While AI may be the new electricity (and as a Data Scientist at Comet, I don't need much convincing), significant challenges remain for the field to realize this potential.

There's no doubt that the digital transformation of manufacturing is changing the face of the industry, and many companies are feeling pressure to evolve into a "factory of the future." It's an exciting time that promises significant efficiency and productivity gains, yet the prospect of transitioning to digital records, tools and processes is daunting for many manufacturing businesses. That's why the best place to start might be surprising: paper. To improve your records management process, begin with your paper records. It might seem counterintuitive to start a digital transformation by getting a better handle on paper records, but the approach makes good business sense.