The evolution of artificial intelligence enabled researchers to adopt new iterative models for machine learning and when these models are exposed to new data they are able to learn independently.
Valley Cottage, NY -- (SBWIRE) -- 09/06/2018 -- With recent advances in science and technology, particularly in machine learning, organisations need to adopt more comprehensive analytics strategies rather than basic analytics as large volumes of data is needed to be analyzed. Machine learning draws from numerous fields of study—artificial intelligence, data mining, statistics, and optimization. With the emergence of need for new computing technologies, the machine learning as service market has emerged drastically from the past where an organisation need critical, reliable and faster ways to learn from the data already available and provide insights. The evolution of artificial intelligence enabled researchers to adopt new iterative models for machine learning and when these models are exposed to new data they are able to learn independently.
Many organisation are adopting machines learning as a service (MLaaS) rather than in-house development due to various Organizational Challenges like talent scarcity etc., and Data Challenges like need of reliable and quality data. The organisations also face Infrastructure Challenges like lack of proper storage facilities and hardware for computation.
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Market drivers & challenges:
Major markets drivers for Machine learning as Service (MLaaS) market increased volume of data and diversity in the data available which need to be studied qualitatively. The other driver for Machine learning as service market was data storage became more affordable and cheaper.
The other major market driver for the Machine learning as Service market (MLaaS) is Recommendation systems which are usedacross a wide range of industries, most notably online shopping sites which help organisation to get deeper insights about the customer behavior by helping them discover new and relevant offers thereby leading to stronger customer relationships and higher sales for the business.
The other major factor driving Machine learning as Service market (MLaaS) is some organizations need to make important decision during real time where machine learning models provide best predictions.
The biggest challenge in adopting Machines Learning as Service (MLaaS) by many organisations was the "cold start" problem wherein they lack the historical data needed to provide recommendations where in organisation first create a feedback lack that test user responses in order to make further recommendations which can improve over time.
Global Machine Learning as a Service Market: Segmentation
Global Machine Learning as a Service Market is segmented based on the, type, vertical and region.
On basis of Type global Machine Learning as a Service Market can be segmented to Supervised learning, Unsupervised learning, Semi supervised learning and Reinforcement learning web and mobile.
On the basis of vertical global Machine Learning as a Service Market is segmented into BFSI, Government, healthcare, retail, energy, transportation and others.
On basis of region, global Machine Learning as a Service Market is segmented into North America, Latin America, Eastern Europe, Western Europe, Asia Pacific Excluding Japan (APEJ), Japan and the Middle East and Africa (MEA).
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North America region is dominant in Machine Learning as a Service Market due to growing demand from large enterprises and startup companies in North America and followed by Western Europe during the period. Asia-Pacific region is expected to grow at a faster rate during the forecast period.
Key Market Players:
Some of the Key players in Machine Learning as a Service Market include Google, Amazon Web Services, IBM Corporation, Microsoft Corporation, SAS Institute Inc., BigML, Inc., DataRobot, Inc., FICO (Fair Isaac Corporation), Yottamine Analytics, LLC, and Algolytics.
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