Choosing the Right Machine Learning Service in Azure (Part 2 of 4)
Azure Cognitive Services
- Vision: Computer Vision and Face API
- Language: Text analytics and translator service
- Speech: Text analytics, speech-to-text, text-to-speech, and speech translation
- Decision: Anomaly detection and content moderation
Most of the Cognitive Services APIs work similarly- Firstly, you have to deploy a specific cognitive service or a Cognitive Services multi-service account in Azure, and after that, you can easily retrieve the API endpoint and access key from the service as well as call the Cognitive Service API with your data and API key. This process will enrich an existing application with API capabilities.
Custom Cognitive Services
- Vision: Azure Custom Vision
- Language: Language Understanding and QnA Maker
- Speech: Custom speech-to-text
- Speech: Custom text-to-speech
- Speech: Speaker recognition
- Decision: Azure Personalizer
Each of the above services offers an interface to train or customize a built-in ML model with your own domain-specific training data. For example- Azure Personalizer allows you to optimize an online recommendation engine through reinforcement learning, whereas, the Azure Custom Vision service allows you to fine-tune a pre-trained ML model on your own training data which is also called transfer learning.
Azure Applied AI Services
- Conversations: Azure Bot Service
- Documents: Azure Form Recognizer
- Search: Azure Cognitive Search
- Monitoring: Azure Metrics Advisor
- Videos: Azure Video Analyzer
- Accessibility: Azure Immersive Reader
However, if you want to fully customize the algorithms, error metrics, and models, then, you have to implement the model and ML pipeline on your own.
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