On September 1, 2021 Gartner published their 2021 "Market Guide for AI Trust, Risk and Security Management". Per Gartner, "This Market Guide identifies new capabilities that data and analytics leaders must have to ensure model reliability, trustworthiness and security, and presents representative vendors who implement these functions."1 At HPE, we believe HPE Ezmeral ML Ops was recognized for the advantages our solution provides to our customers. As such, we're proud to announce that Gartner listed HPE Ezmeral ML Ops as a Representative ModelOps Vendor in the 2021 "Market Guide for AI Trust, Risk and Security Management." Gartner defines the AI Trust, Risk and Security Management (TRiSM) market as being made up of multiple software segments.

It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services. The possibilities for AI use grow almost daily, so it's important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management.

It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services. The possibilities for AI use grow almost daily, so it's important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management.

Asset-intensive organizations are pursuing digital transformation to attain operational excellence, improve KPIs, and solve concrete issues in the production and supporting process areas. AI-based prediction models are particularly useful tools that can be deployed in complex production environments. Compared to common analytical tools, prediction models can more easily amplify correlations between different parameters in complicated production environments that generate large volumes of structured or unstructured data. My regular talks with executives of production-intensive organizations indicate that AI use is steadily rising. This is in line with IDC's forecast that 70% of G2000 companies will use AI to develop guidance and insights for risk-based operational decision making by 2026.

Artificial Intelligence is transforming the world in multiple ways. The technology is used not only in the corporates. But it is being used in a wide range of other fields as well, like healthcare and agriculture as well. Artificial Intelligence business solutions could be utilized to enhance the experience, be it the customer experience that the company offers to the customers or the hospitality experience offered by the hotels etc. AI has a pivotal role to play in the enrichment of the experience in the museums. In this article, we will talk about that in detail. When it comes to digitalization, Artificial Intelligence has a major role to play.

AI and machine learning (ML) tools and solutions are fundamentally changing how businesses are run. Soon, organizations that are not using AI will be at a disadvantage from those that do and applications that are not AI-powered may feel broken. This week at Google Cloud Next '20: OnAir we explored how Cloud AI is empowering teams with AI and ML tools and solutions across a range of skills and knowledge. We gave you a sneak peak of a set of MLOps tools including Prediction backend GA, Managed Pipelines, Metadata, Experiments, and Model Evaluation, to operationalize and scale your machine learning workflows. We also shared how we are implementing our AI Principles in Cloud AI in Responsible AI: from theory to practice, and how we're bringing Google's expertise in machine learning into solutions like Contact Center AI (CCAI) to give you a way to better serve your customers.

The future of the healthcare industry offers tons of opportunities. As, the industry is growing continuously. Also, it has been observed that the healthcare industry has wholeheartedly accepted the new technologies like artificial intelligence and machine learning. The industry is always keen to accept and integrate the new tools and techniques that lead to automation. Also, with the help of AI and ML, the healthcare institutes as well as the businesses have experienced better quality of care.

The practice of collecting basic demographic information from customers to create a successful business marketing strategy is one of the past. In recent times, there has been a major shift in the way that businesses interact with their customers. The digital space has spread so far and wide that it has had a lasting influence on virtually everything we do. As a result, the conventional approaches to marketing that were prevalent even as early as a few years ago are considered severely ineffective today. The rapidly growing popularity of Big Data means that marketers need to embrace sophisticated approaches to processes and perform in-depth analysis of customer data, preferably in real-time.