by Machine learning as a service (MLaaS) has gained immense popularity over the recent years. MLaaS helps businesses and organizations integrate machine learning algorithms into their applications without having to build and maintain dedicated infrastructure. MLaaS providers help clients with data collection, algorithm selection, model training, deployment and cloud hosting of machine learning models. Machine learning techniques require huge volumes of data for training models which can be accessed and processed cost effectively using cloud based ML services. The rise of big data analytics and availability of large public datasets has facilitated wider adoption of ML techniques. MLaaS offers cost savings over hiring data scientists and building in-house machine learning capabilities. Users arecharged based on pay-as-you-go pricing models for cloud ML services which lowers the entry barriers.
The global Machine Learning as a Service (MLaaS) Market is estimated to be valued at US$ 10072.55 Mn in 2023 and is expected to exhibit a CAGR of 26.% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Market key trends:
One of the major trends in the MLaaS market involves further commoditization of ML services. Leading cloud providers are offering turnkey ML solutions for applications ranging from computer vision to natural language processing. Customers can configure and deploy ML models with minimal coding. This is driving wider usage of machine learning across industries. Another trend is the evolution of ‘automated machine learning’ techniques which help accelerate the model development cycle using automation. MLaaS providers are leveraging automated techniques to recommend best performing algorithms and hyperparameter optimization to build effective models faster. This is reducing the dependency on data scientists and AI engineers for routine tasks. Integration of ML capabilities into popular DevOps tools and frameworks is also simplifying model building, deployment and maintenance workflows for developers and businesses.
Porter’s Analysis
Threat of new entrants: The threat of new entrants is moderate in the MLaaS market. Establishing ML infrastructure requires significant capital investment and technical expertise which acts as a barrier for new players.
Bargaining power of buyers: The bargaining power of buyers is high. Buyers have a variety of MLaaS providers to choose from and can negotiate on price and quality of service. Switching costs are also low.
Bargaining power of suppliers: ML labor and infrastructure are widely available which reduces the bargaining power of suppliers. However, the specialized skills required place some bargaining power with suppliers.
Threat of new substitutes: Substitutes like in-house ML teams pose a potential threat. However, MLaaS provides cost and time savings which makes it difficult for substitutes to gain traction in the short run.
Competitive rivalry: The MLaaS market sees intense competition among established players. Competition is based on capabilities, pricing, customization and quality of service.
Key Takeaways
The Global Machine Learning As A Service (Mlaas) Market Size is expected to witness high growth.
Regional analysis:
Europe and Asia Pacific are also promising markets for MLaaS. In Europe, Germany, UK and France are early adopters while in APAC, China and Japan are emerging as revenue generators with their focus shifting to digital technologies.
Key players:
Key players operating in the Machine Learning as a Service (MLaaS) market are Fitbit Incorporation, Garmin International, Pebble Incorporation, Xiaomi, Samsung Electronics, NIKE Incorporation, Shanda Group, and Sony Corporation. Fitbit and Garmin dominate the wearables market while Xiaomi has a strong presence in APAC.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
