Market Overview:
Machine learning as a service (MLaaS) provides machine learning models and related services through the cloud. It helps various industries such as healthcare, telecom, retail and others to build and deploy machine learning models without internal machine learning expertise and infrastructure. Companies opt for MLaaS as it reduces capital expenditure and speeds up deployment of ML projects. The cloud-based deployment of MLaaS lowers the entry barriers for organizations and provides easy access to machine learning.
Market key trends:
Cloud-based deployment is one of the key trends driving the growth of the MLaaS market. Most MLaaS vendors offer cloud-based services which eliminates the need for on-premise machine learning infrastructure and resources. Companies are preferring cloud-based ML services as it is more scalable, cost-effective and does not require huge upfront investment on hardware. Also, ML models and algorithms deployed on cloud can easily handle huge volumes of data and processing requirements. The transition to cloud architecture has accelerated digital transformation initiatives of organizations and propelled the demand for cloud-based MLaaS services. The increasing adoption of ML by businesses across verticals coupled with benefits of cloud is expected to boost the MLaaS market during the forecast period.
The global Machine Learning as a Service (MLaaS) Market Size is estimated to be valued at US$ 10,072.55 Mn in 2023 and is expected to exhibit a CAGR of 38.% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Porter's Analysis
Threat of new entrants: The threat of new entrants is low in the MLaaS market as it requires high start up capital and expertise to break into this market. Established players have strong brand recognition and resources.
Bargaining power of buyers: The bargaining power of buyers is high in this market. There are many players offering MLaaS solutions which gives buyers options to choose from. Buyers can negotiate on price and demand additional services.
Bargaining power of suppliers: ML engineers and data scientists are the key suppliers. However, their bargaining power is medium as top players offer lucrative compensation packages to retain talents.
Threat of new substitutes: Threat of substitutes is low as ML solutions require specialized technical skills that are not easily replaceable. However, open source tools provide some substitution.
Competitive rivalry: Intense as major tech giants are focusing on ML and competing on pricing, services and reliability.
SWOT Analysis
Strength: MLaaS enables businesses to harness machine learning without investing heavily in infrastructure, skills. It offers scalability, reduces costs.
Weakness: Vendor lock-in risks, data security and privacy concerns, lack of control over algorithms.
Opportunity: Growing adoption across industries like healthcare, marketing, transportation creates scope.
Threats: Regulation around data use and algorithm transparency, economic slowdowns restricting IT budgets.
Key Takeaways
The global Machine Learning as a Service (MLaaS) market is expected to witness high growth, exhibiting CAGR of 38.% over the forecast period, due to increasing demand from industries for ML capabilities without investments. North America dominates currently due to extensive investments by tech companies in ML.
Asia Pacific region is expected to be the fastest growing regional market for MLaaS during the forecast period. This is attributed to growing digitization programs by governments in countries such as China and India.
Key players operating in the Machine Learning as a Service (MLaaS) are H2O.ai, Google Inc., Predictron Labs Ltd, IBM Corporation, Ersatz Labs Inc., Microsoft Corporation, Yottamine Analytics, Amazon Web Services Inc., FICO, and BigML Inc. Major players are focusing on expanding their geographical presence and enhancing their service portfolio to leverage growth opportunities.
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