Model drift refers to the phenomenon that occurs when the performance of a machine learning model degrades with time. This happens for various reasons, including data distribution changes, changes in the goals or objectives of the model, or changes to the environment in which the model is operating. There are two main types of model drift that can occur: data drift and concept drift. Data drift refers to the changing distribution of the data to which the model is applied. Concept drift refers to a changing underlying goal or objective for the model.

There are several data-driven technologies that are primed to take off this year. Based on what we are seeing with our customers, we can expect a surge in the adoption of emerging technologies like generative artificial Intelligence as well as new software architectures that will transform markets, empower consumers, and deliver new personalized customer experiences. Some of these developments will change the way products are built. Others will improve how consumers interact with organizations while fortifying data privacy and regulatory compliance. All of them, however, will make data more readily available, accessible, and useful to those who need data most – businesses and the customers that patronize them.

The increasing complexity of a digital-first world shows no sign of abating in 2023. And with looming economic uncertainty, organizations will turn to technology as they navigate concerns. We believe that data and AI will ultimately determine business success. Those organizations able to harness the vast volume of data and turn it into actionable insights will be more agile and prepared to prosper throughout 2023 and beyond. Solutions will become increasingly intelligent as AI becomes more prolific across systems and devices.

Post-pandemic, the demand for AI is surging, as many organizations ascertain the need for AI to keep pace with the current business landscape in the face of a looming recession. AI can help enterprises improve business processes, increase speed and accuracy, and help make predictions to optimize their performance. In 2023, there will be many ways that enterprises can implement AI but for more traditional organizations, we suggest the following trends will play an important role. This includes the need for companies to get their data fabric in place before implementing AI, new and interesting ways to "white-label" AI, and the need to develop a Center of Excellence to ensure the entire company is aligned with an AI strategy. As more enterprises look to implement AI projects in 2023 to increase productivity, gain better insights, and have the ability to make more accurate predictions regarding strategic business decisions, the challenge will be for traditional enterprises to establish a robust data framework that will allow their organizations to leverage data effectively for AI purposes.

Data-centric AI is gaining momentum among engineers. While traditionally, a model-centric approach has been used to improve accuracy for a variety of applications, the increase of data available today and the benefits of using reliable data are leading engineers to reevaluate their priorities and workflows. With a model's performance so dependent on the quality of the data it is being trained with, this data focus has empowered engineers to improve model accuracy without the circular process of constantly tweaking parameters. By improving data quality and model accuracy, data-centric AI allows for new areas of application and opens new opportunities in the field of engineering – from 5G communications to LiDAR, medical device imaging, state of charge estimations, and many more. Get our weekly newsletter in your inbox with the latest Data Management articles, webinars, events, online courses, and more.

Today, most businesses are embracing digital transformation to meet ever-increasing customer expectations and to remain competitive and relevant in the world economy. While many organizations still struggle to adopt new technologies – often due to the lack of a tailored digital strategy and the reluctance of many to change the way of business operations that have been continuing for decades – digital transformation examples abound across all industries. According to Statista, worldwide digital transformation spending is projected to reach $2.8 trillion in 2025. Although digital transformation undeniably makes businesses, regardless of their size and kind, more viable and easily accessible to customers, becoming a digital-first organization is not as simple as it seems. Digital transformation involves simple processes like the integration of communication tools as well as complex ones such as the migration of entire business operations to a cloud-based platform.

AI and machine learning models are being used to help companies stay competitive by discovering new revenue opportunities, improving risk management, detecting fraud, and streamlining business processes. But years ago, data science wasn't even on the curriculum at universities, so many software engineers are acquiring the necessary skills on their own. From my experience, anyone who has a strong STEM background can have a smooth transition to becoming a data scientist. Personally, I studied biology at university, but I was comfortable learning how to build machine learning models on my own. So, even if data science wasn't what you studied in undergraduate or graduate studies, it's possible to make the transition to AI and bring the power of machine learning to your teams.

Integrating edge artificial intelligence (AI) is not a simple process. Early forms of artificial intelligence relied on the computer power of data centers to perform their processor-demanding tasks. After some time, AI shifted into software, using predictive algorithms that changed how these systems support businesses. AI has now moved to the outer edges of networks. Artificial intelligence at the edge exists when local "edge" devices process AI algorithms instead of being processed in the cloud.

Machine learning (ML) has enabled a whole host of innovations and new business models in fintech, driving breakthroughs in areas such as personalized wealth management, automated fraud detection, and real-time small business accounting tools. For a long time, one of the most significant challenges of machine learning has been the amount and quality of data that is required to train machine learning models. Recent developments of Transformer architectures, however, have started to change this equation. Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers, developed at Google) and GPT (Generative Pre-Training, developed at OpenAI) have brought about the biggest changes in machine learning in recent years. These technologies were initially developed to process natural language data but are now creating exciting new opportunities across many applications, including fintech. Want to learn the fundamental building blocks of Data Modeling?

AI adoption continues to expand across the globe, with Gartner predicting that organizations over the next five years will "adopt cutting-edge techniques for smarter, reliable, responsible and environmentally sustainable artificial intelligence applications." And as the industry matures and machine learning (ML) models become cheaper, faster, and more accessible, every enterprise will be looking at how and where the technology may benefit their organization. Expectations are high, from driving productivity and efficiency gains to delivering new products and services. AI platforms are being enhanced by developments in related fields, including ML, computer vision, language, speech, recommendation engines, reinforcement learning, edge IT hardware, and robotics. However, with so much noise and hype around AI, it's tough for many businesses to figure out how to harness the technology effectively.