Machine learning is no longer about experiments. Most industry-leading enterprises have already seen dramatic successes from their investments in machine learning (ML), and there is near-universal agreement among business executives that building data science capabilities is vital to maintaining and extending their competitive advantage. The bullish outlook is evident in the U.S. Bureau of Labor Statistics' predictions regarding growth of the data science career field: Employment of data scientists is projected to grow 36% from 2021 to 2031, much faster than the average for all occupations. The aim now is to grow these initial successes beyond the specific parts of the business where they had initially emerged. Companies are looking to scale their data science capabilities to support their entire suite of business goals and embed ML-based processes and solutions everywhere the company does business.

Organisations across the board are looking to artificial intelligence (AI) to find ways to more accurately manage risk, enhance efficiencies to reduce operating costs, and improve experiences for clients and customers. Nvidia has conducted a survey with some of the world's leading financial institutions to find out what's on the top of their minds. Below are the top four findings gleaned from the "State of AI in Financial Services: 2023 Trends" survey taken by nearly 500 global financial services professionals. Financial services firms, like other enterprises, are looking to optimise spending for AI training and inference -- with the knowledge that sensitive data can't be migrated to the cloud. To do so cost-effectively, they're moving many of their compute-intensive workloads to the hybrid cloud.

The Vertica SQL database and in-database machine learning solutions support the entire predictive analytics process with massively parallel processing and a familiar SQL interface. After a brief dip due to the impact of the pandemic, business analytics services resumed double-digit growth in 2021 and 2022, according to IDC. There is a great need to improve business outcomes using insights from analytics and other techniques, including machine learning. What most businesses find when undertaking new analytics and machine learning (ML) projects is that their current infrastructure is not up to the task. The projects typically need compute and storage capabilities that are not typically available in most businesses.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! There is no shortage of options for organizations seeking places in the cloud, or on-premises to deploy and run machine learning and artificial intelligence (AI) workloads. A key challenge for many though is figuring out how to orchestrate those workloads across multi-cloud and hybrid-cloud environments. Today, AI compute orchestration vendor Run AI is announcing an update to its Atlas Platform that is designed to make it easier for data scientists to deploy, run and manage machine learning workloads across different deployment targets including cloud providers and on-premises environments.

Today's announcement of IBM's new z16 mainframes promises a system that caters to enterprise needs that include support for AI, security, hybrid cloud, and open source efforts well into the future. The new, more powerful and feature-rich Big Iron boasts an AI accelerator built onto its core Telum processor that can do 300 billion deep-learning inferences per day with one millisecond latency and includes what IBM calls a quantum-safe system to protect organizations from anticipated quantum-based security threats. The system's IBM Telum dual-processor chip has 16 cores and runs at 5.2 GHz. IBM says that the z16 comes with up to 200 configurable cores in a single model--the Model A01--and includes 40TB of redundant array of independent memory (RAIM) per system. But while z16 family, available May 31, is more powerful, the system also promises to accelerate other core IBM strategies of growing hybrid computing and open-source based enterprise systems.

From the adversity of almost two years of pedal-to-the-metal crisis-response, rose immense investment in business transformation and technologies across all industries. After such rapid transformation, enterprise leaders might be forgiven for wanting to take a pit stop. But this is no time to slow down. With soaring employee expectations, and competition to digitally innovate, transformation in 2022 will continue apace. Trend 1 – Enterprise technology will be forced to'level-up' and meet consumer tech standards Increasingly, workplace technology seems to be playing a losing game of catchup with the consumer devices and apps we use in our everyday lives.

Artificial Intelligence has been one of the interesting concepts for a long time. Yes, the idea of forming machines that think like humans has been doing the rounds since the 1950s. But now we are hearing more about AI. Artificial Intelligence research on these topics is hot at moment. How can Natural Language Processing be useful?

Capturing big data is easy. What's difficult is to corral, tag, govern, and utilize it. NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud. Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.

IBM Wednesday announced CodeFlare, an open-source, serverless framework designed to simplify the integration and efficient scaling of big data and AI workflows onto the hybrid cloud. CodeFlare is built on top of an emerging open-source distributed computing framework for machine learning applications known as Ray. IBM said CodeFlare extends the capabilities of Ray by adding specific elements to make scaling workflows easier. With data and machine learning analytics are proliferating into just about every industry, tasks are becoming far more complex, IBM noted. While it is important to have larger datasets and more systems designed for AI research, as these workflows become more involved, researchers are spending more and more time configuring their setups than getting data science done.

As demand for intelligent, online digital services continues to surge, governments and businesses are integrating AI throughout transformation. Their applications are migrating to the cloud faster and evolving based on massive amounts of data, apps, AI, and industry know-how. These applications will create more value if they combine new technologies with business dynamics and customer needs. The shift to digital is opening up a plethora of business opportunities for application developers. But many challenges lie ahead, stopping governments and businesses from moving key applications to cloud.