Amid soaring demand for tools to enable the data-driven organisation, a partnership between data specialists Knowledge Integration Dynamics (KID) and global AI cloud leader DataRobot is automating and democratising artificial intelligence (AI) and machine learning (ML), putting it into the hands of more South African businesses. Markus Top, who is heading up the partnership at KID, says it is a logical next step for KID, which has supported South African enterprises through their data journey for over 20 years. "Every business today wants to be data driven and embed AI at scale. However, until fairly recently achieving this has been a costly and time-consuming task," Top says. "With DataRobot, the manual, time-consuming processes within AI and ML projects are largely automated, allowing businesses to transform and innovate faster."

The Covid-19 pandemic accelerated digital transformation and the need for IT departments to adapt to the working from home environment. While in recent months there has been a call from large corporates for the return of their staff to the office, many people have continued to work from their home offices. Some organizations have realized the benefits of a remote working culture, such as the ability to scale back on expensive offices, while others are adopting a hybrid approach that allows people to commute occasionally to the office and work from home most of the time. Infor-Tech Research Group says, "If the last two years have taught us anything, it's that organizations need their IT departments to survive and thrive in today's digital economy. This has now paved the way for IT leaders like yourself to expand on your department's maturity level and move out of a supportive role toward a more strategic role, enabling growth within your organization. The challenge then becomes, how do you move up the maturity ladder?"

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.

Artificial intelligence and machine learning bring new vulnerabilities along with their benefits. Here's how several companies have minimised their risk When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks.

Last decade has been witnessing consistent evolution of Artificial Intelligence, Machine Learning and other cognitive technologies. Irrespective of size, industry and target customer, companies are increasingly investing in projects based on these emerging technologies for varied reasons. Some businesses are focusing on building smart devices, which are amalgamation of three parallel development tracks of hardware, software, AI / ML models. Some are internal projects wherein the focus is on enterprise predictive analytics, managing fraud, or other tasks aimed at process improvement that serve to provide an additional layer of understanding or mechanism on top of existing data and applications. Various initiatives are based on interactive user interfaces that are spread across a plethora of systems and devices.

Aleksandar Kovačević, Sales Engineer at InterSystems, shares how companies use MLOps combined with a central multi-model database to get the most out of their machine learning initiatives. Artificial Intelligence (AI) and Machine Learning (ML) are hot topics at the moment. But when it comes to producing quantifiable results, there is still a lot of work to be done. How can MLOps, which merges machine learning with operations (procedures and processes), help to make ML projects more successful? There is no doubt that Machine Learning and Deep Learning offer a lot of potential.

To say that artificial intelligence (AI) is the next step in enterprise would be an understatement. In other words, the so-called "AI revolution" is already here. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia. According to McKinsey and Company, by 2030, 70% of companies will have adopted at least one kind of AI technology. The expansion of AI also stands to have a significant impact on the world's economy and job-force.

WALMART is a giant retail organization that managed to keep up with its customer's changing needs in the digital era, delivering groceries and everyday essentials as efficiently as delivering on expectations. At a recent conference, Business Insider heard Walmart Chief Digital Officer Bill Groves talk about the organization's journey to digital. Groves revealed that Walmart employs roughly 1,500 data scientists and 50,000 software engineers who support the 100,000-odd artificial intelligence (AI) and machine learning (ML) projects that the organization currently runs. "I do more work in the AI and [machine learning] space then I have ever done in my life. We're involved in robotics, we're involved in micro-personalization, we're involved in probably the biggest supply chain in the world." The important thing, however, is that Groves said that Walmart's success rate with AI and ML projects is 75 percent.