Machine Learning - Worldwide

Current trends in machine learning include the rise of deep learning and the development of new algorithms that enable more accurate predictions and decision-making. Other uses of machine learning have been increasingly observed in the healthcare, finance, and retail industries, among others, as well as the growth of cloud-based machine learning services. The adoption of machine learning by small and medium-sized enterprises (SMEs) is also a notable trend.

Growth factors in this market include the increasing availability of data, advancements in computing power, and the growing need for automation and optimization across industries. The expansion of the Internet of Things (IoT) and the rise of edge computing are also expected to drive growth in the market, as they generate vast amounts of data that can be analyzed with machine learning algorithms. In addition, the integration of machine learning with other technologies such as natural language processing and computer vision is expected to create new growth opportunities in the coming years.

The Machine Learning market is expected to see significant growth in the coming years, with a forecasted compound annual growth rate (CAGR) of over 18.7% from 2023 to 2030. Potential challenges for this market are the lack of skilled talent and concerns around data privacy.

Data coverage: The data encompasses B2B, B2G, and B2C enterprises. Figures are based on the funding values from different industries for the market.

Modeling approach / Market size:Market sizes are determined through a top-down approach with a bottom-up validation, building on a specific rationale for each market. As a basis for evaluating markets, we use annual financial reports, funding data, and third-party data. In addition, we use relevant key market indicators and data from country-specific associations such as GDP, number of internet users, number of secure internet servers, and internet penetration. This data helps us estimate the market size for each country individually.

Forecasts:In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited to forecast digital products and services due to the non-linear growth of technology adoption. The main drivers are the level of digitalization, the number of secure internet servers, and the revenue of the Public Cloud market.

Additional Notes: The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russian-Ukraine war are considered at a country-specific level. The market is updated twice a year. In some cases, the market is updated on an ad-hoc basis (e.g., when new, relevant data has been released or significant changes within the market have an impact on the projected development). Data from the Statista Consumer Insights Global survey is weighted for representativeness.