No-code machine learning market
The no-code machine learning market is witnessing impressive expansion in the coming years, reflecting the growing demand for accessible AI solutions. This market is poised to become a major player in the technology world, as companies across sectors seek faster and easier ways to build and deploy machine learning models. Let us explore the current market size, key drivers, prominent players, emerging trends, and segmentation details shaping this vibrant sector.
Projected growth trajectory of the no-code machine learning market
The no-code machine learning market is expected to grow significantly and reach a valuation of $5.49 billion by 2030. This impressive expansion corresponds to a compound annual growth rate (CAGR) of 30.5%. Factors driving this rapid growth include increased integration with predictive analytics tools, increased adoption of business intelligence platforms, and the growing need for rapid model deployment. Additionally, sectors such as healthcare, banking, financial services and insurance (BFSI) are also adopting these platforms. The emergence of self-service machine learning platforms is further supporting this upward trend. Key market trends expected to impact growth include the proliferation of low-code/no-code solutions, automated model tuning, enablement of citizen data scientists, drag-and-drop AI workflows, and availability of pre-built machine learning templates.
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Key market drivers driving the expansion of no-code machine learning
One of the key factors for the rapid growth of the no-code machine learning market is the growing demand for rapid deployment and easy integration of AI models within existing business intelligence systems. Companies aim to quickly gain actionable insights without relying on specialized data science teams.
Additionally, the adoption of no-code solutions by the healthcare and BFSI sectors is accelerating the market momentum. These industries are benefiting from faster model creation and deployment to support critical tasks such as patient outcome prediction and risk management.
Leading organizations in the no-code machine learning space
Several influential companies dominate the no-code machine learning industry, including big names like Apple Create ML, Microsoft Azure Machine Learning Studio, Amazon Web Services, SAS Viya, and DataRobot Inc. Other important players include 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, and Neural Designer. These companies continue to innovate and expand the capabilities of their no-code products around the world.
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Recent strategic moves to strengthen market capabilities
In July 2024, US-based data science automation company Forward.ai acquired Israeli no-code machine learning provider LoudnClear.AI. The acquisition, for an undisclosed amount, will enable LoudnClear.AI to further its mission of empowering revenue and business teams by using natural language processing (NLP), machine learning, and AI to analyze unstructured data to quickly and effectively gain deeper insights into customer sentiment.
Emerging technology trends shaping no-code machine learning
Market leaders are investing heavily in workflow automation through no-code machine learning tools that democratize AI by eliminating the need for programming skills. These innovations make it easy for non-technical users to independently build and deploy models.
For example, in December 2023, Amazon launched SageMaker Canvas, a no-code tool designed specifically for business analysts and other users without coding expertise. It simplifies the entire machine learning process, from data preparation to model training, for applications in areas such as customer churn prediction, fraud detection, and inventory management.
Comprehensive Segmentation of No-Code Machine Learning Market
This market has been segmented on the basis of various factors to better understand its scope and scope.
1) By providing:
– Platform
– service
2) By deployment mode:
– Cloud-based
– On-premises
3) By industry:
– Banking, Financial Services and Insurance (BFSI)
– health care
– retail
– Information Technology (IT) and Telecommunications
– Manufacturing industry
– Government
4) By application:
– Predictive analytics
– Process automation
– Data visualization
– Business intelligence
– Customer relationship management
– Supply chain optimization
A more detailed breakdown includes platform subcategories such as automated machine learning platforms (AutoML), drag-and-drop machine learning platforms, model deployment platforms, data preparation platforms, visualization and reporting platforms, and API and data source integration platforms. Types of services include consulting, implementation, training and education, support and maintenance, and custom solution development.
A global perspective on market trends
The fragmentation and rapid growth of the no-code machine learning market spans multiple industries and deployment models, reflecting its broad appeal and adaptability. This holistic approach enables businesses around the world to better leverage AI capabilities, regardless of their technical expertise.
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