ModelOps, ML Validation & ML Assurance: The Next Frontiers of AI-led Digital Assurance - A Cigniti Digital Dialogue

Why should you be attending this Cigniti Digital Dialogue : ModelOps, ML Validation & ML Assurance: The Next Frontiers of AI-led Digital Assurance

Register here :?https://lnkd.in/gBEJ-u-2????????????????????????????????????????

The hypercompetitive age, in which new technologies change in the space of a single blink of an eye, is centered on machine learning (ML) and artificial intelligence (AI). In every area of the economy, new developments like AI, predictive analytics, ML, and other digital disruptors are transforming how businesses run and how consumers connect with brands. Organizations increasingly experience existential transitions. In this age of digital disruption, digital leaders must have the skills and understanding to integrate AI into their whole business plan.

The buzz around AI from a few years ago is undoubtedly a reality today, with every industry looking for ways to capitalize on the potential long-term benefits. Whether you run a business that focuses on retail, banking, construction, or everything in between, the number of companies utilizing the best AI and data scientist teams to support their company's success is growing every day.

However, creating and using AI/ML models is not straightforward, and there is a significant risk of failure. By expanding their investment in AI, several companies have seen an increase in their bottom line. A good method is needed to reduce this risk and promote corporate growth.

“By 2025, 80% of the biggest enterprises on the planet will have taken part in federated ML at least once in order to develop models that are more precise, secure, and environmentally friendly.”

-Gartner

Surprisingly, 87% of ML models are never used in production. In other words, just one out of every ten workdays for a data scientist actually contributes to the success of the company. For those models that do make it to production, it takes at least three months to get ready for deployment. A real operational expenditure and a longer time to value are the results of the increased time and labor.

To solve this problem, we need a framework or method called ModelOps that would reduce the amount of manual effort and hasten the deployment of ML models.

An AI and Advanced Analytics technique called ModelOps aims to move models as quickly as possible from the lab to validation, testing, and production while preserving high-quality outcomes. It permits model management, scaling, and ongoing monitoring in order to spot and handle any early warning signs of decline.

The fact that institutional knowledge about a particular process is rarely fully defined and that many judgments are difficult to reduce to a few basic rules poses a major problem. Additionally, a lot of information sources that are crucial for scaling ML are either too high-level or too technical to be useful. As a result, managers are left without much information about how to guide their teams as they embrace ML algorithms.

Therefore, from the perspective of ML quality assurance, a thorough testing procedure is required to guarantee the stability and effective performance of these models. To guarantee that judgments based on data are reliable entails reducing biases in their creation.

?Against this backdrop, join us for an insightful digital dialogue series where Cigniti’s thought leaders give you an in-depth look at ML validation and ML assurance. AI-led digital assurance is extremely important for any digital transformation program’s success. The bedrock of becoming digital-first in modern-day business is ensuring impeccable digital experiences, and customers today are looking to leverage this expertise further.

This digital dialogue will focus on

·??????How ML Assurance is accelerating the digital transformation of leading organizations

·??????How to assess the maturity of your AI/ML initiatives including their ability to predict models and identify data leakages or biases to enable true digital transformation

·??????How to strengthen your ML initiatives with actionable insights and success stories from thought leaders

Register here : https://lnkd.in/gBEJ-u-2