No-Code Machine Learning Market Report 2024

This No-Code Machine Learning market report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.

The no-code machine learning market size has grown exponentially in recent years. It will grow from $0.85 billion in 2023 to $1.1 billion in 2024 at a compound annual growth rate (CAGR) of 30.3%. The growth during the historic period can be attributed to a rising demand for user-friendly tools, an increasing need for cost-effective machine learning solutions, greater use of cloud-based no-code platforms, heightened awareness of machine learning benefits among non-technical users, and the growing popularity of low-code and no-code platforms.

The no-code machine learning market size is expected to see exponential growth in the next few years. It will grow to $3.22 billion in 2028 at a compound annual growth rate (CAGR) of 30.7%. The growth during the forecast period can be attributed to the increasing demand for accessible AI tools, broader adoption of AI across different sectors, growing use of cloud computing, greater availability of pre-built machine learning templates, and a focus on lowering the barrier to technical skills. Key trends expected in this period include technological advancements, AI-driven personalization, IoT applications, predictive analytics, and self-service analytics.

The increasing adoption of the Internet of Things (IoT) is expected to drive growth in the no-code machine learning market in the future. The Internet of Things (IoT) refers to a network of interconnected devices and systems that communicate and exchange data over the Internet to automate processes and improve operational efficiency. The adoption of IoT is driven by its ability to enhance operational efficiency, provide real-time data insights, enable automation and remote monitoring, reduce costs, improve decision-making, and foster innovation across various industries by connecting and optimizing a broad range of devices and systems. No-code machine learning is increasingly utilized within the IoT ecosystem to simplify the creation, deployment, and management of machine learning models without requiring extensive technical expertise. For example, in November 2022, Ericsson, a Sweden-based network and telecommunications company, projected that the number of global IoT-connected devices would grow from 13.2 billion in 2022 to 34.7 billion by 2028. Consequently, the rise in IoT adoption is fueling the expansion of the no-code machine learning market.

Major companies in the no-code machine learning market are focusing on developing advanced technologies to enhance workflow automation, including no-code machine learning tools. These tools enable users to create and deploy machine learning models without writing any code, making the technology more accessible to those without technical expertise. For example, in December 2023, Amazon, a US-based technology company, introduced SageMaker Canvas, a no-code machine learning tool aimed at users without coding experience. This tool is designed for business analysts and non-technical users, offering a user-friendly interface for easy model creation, data preparation, and training. Key applications of SageMaker Canvas include customer churn prediction, fraud detection, and inventory optimization.

In July 2024, Forwrd.ai, a US-based data science automation platform, acquired LoudnClear.ai for an undisclosed amount. This acquisition will enable LoudnClear.ai to further its mission of helping revenue operations and business teams swiftly analyze unstructured data and gain insights into customer sentiment through NLP, machine learning, and AI. LoudnClear.ai, based in Israel, specializes in providing no-code machine learning solutions.

Major companies operating in the no-code machine learning market are Apple Create ML, Microsoft Azure Machine Learning Studio, Amazon Web Services, SAS Viya, DataRobot Inc, LityxIQ, H2O.ai, Dataiku DSS, C3 AI Suite, RapidMiner Studio, BigML Inc., Google Teachable Machine, Edge Impulse, Microsoft Lobe, KNIME Analytics Platform, MonkeyLearn, Akkio AI, Obviously AI, Runway ML, Fritz AI, Sway AI, PyCaret, Ever AI, Neural Designer.

North America was the largest region in the no-code machine learning market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the no-code machine learning market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

The countries covered in the no-code machine learning market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

No-code machine learning refers to the practice of developing, deploying, and managing machine learning models without writing any code. This approach typically involves using graphical interfaces, drag-and-drop tools, and pre-built templates provided by no-code platforms. These platforms abstract the complexities of programming and data science, enabling users, often non-technical professionals, to build and use machine learning models by following intuitive steps.

The main offering of no-code machine learning offerings include platforms and services. A no-code machine learning platform is a software tool that enables users to create, train, and deploy machine learning models without writing any code, using a visual interface to simplify the process for non-technical users. It can be deployed both on the cloud and on-premise and is used by various industries such as banking, financial services and insurance (BFSI), healthcare, retail, information technology (IT), telecom, manufacturing, and government. It is used for various applications, including predictive analytics, process automation, data visualization, business intelligence, customer relationship management, and supply chain optimization.

The no-code machine learning market research report is one of a series of new reports that provides no-code machine learning market statistics, including no-code machine learning industry global market size, regional shares, competitors with a no-code machine learning market share, detailed no-code machine learning market segments, market trends and opportunities, and any further data you may need to thrive in the no-code machine learning industry. This no-code machine learning market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The no-code machine learning market consists of revenues earned by entities by providing services such as model building, data preparation, data visualization, model training and evaluation. The market value includes the value of related goods sold by the service provider or included within the service offering. The no-code machine learning market also includes sales of data preparation tools, automated machine learning solutions, drag-and-drop workflow builders and predictive analytics tools. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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