Why AI-ML on Cloud
- Access to state-of-the-art technologies and higher-level services to develop new digital products and services
- Affordable test and development of PoCs
- Improvement of customer experience by developing new customer facing solutions to enhance the customer engagement
- Closer integration of the entire process and supply chains within the organization as well as with partners and suppliers
- Acceleration of release cycles (continuous development and deployment)
How Cloud Elasticity helps AI-ML
Cloud Services on AI-ML
- Machine learning platforms: Native cloud services that enable authoring, executing and managing traditional machine learning models (ex: classification, clustering, regression, etc.)
- Natural language processing service: Services that enable the syntactical and sematic analysis of natural language sentences. Some of the common discipline in this category include sentiment analysis, intend analysis, conversation modeling, etc.
- Vision analytic services: Services that provide understanding the content of images. Some of the common techniques in this category include automatic image classification, emotion analysis etc.
- Speech processing services: Services that enable speech analytic capabilities such as translation, conversion to text, etc.
- Knowledge services: Services that complement data with knowledge performing operations such as concept extraction, entity linking etc.
- Data insights services: Services that provide insights about target data sources. Some of the common techniques in this category include tradeoff analysis, news analysis, etc.
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