- Purpose: Provides an end-to-end machine learning platform for data scientists and developers.
- Key Capabilities:
- Model training, deployment, and management.
- Automated Machine Learning (AutoML) for no-code/low-code solutions.
- Integration with popular frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Typical Use Cases:
- Predictive analytics
- Fraud detection
- Recommendation systems
- Purpose: Collection of AI-based services for vision, speech, language understanding, and more.
- Key Capabilities:
- Cognitive Services for text analytics, speech-to-text, image recognition, etc.
- Prebuilt AI models for language translation, anomaly detection, sentiment analysis.
- Typical Use Cases:
- Chatbots and virtual assistants
- Document processing and OCR
- Audio/video transcription
- Purpose: Helps organizations build, deploy, and operationalize AI solutions rapidly.
- Key Capabilities:
- Collaborative environment for data science teams.
- Accelerators and industry-specific solution templates.
- End-to-end MLOps to streamline AI solution lifecycle.
- Typical Use Cases:
- Data science collaboration
- Rapid AI prototyping
- Automated CI/CD for AI projects
- Purpose: Provides access to advanced language models (e.g., GPT series) for building AI solutions.
- Key Capabilities:
- Natural language processing (NLP) and generation.
- Code generation, text summarization, translation, etc.
- Easily integrates with other Azure services for end-to-end solutions.
- Typical Use Cases:
- Chatbots with contextual awareness
- Intelligent document analysis
- Code completion or generation
- Purpose: Intelligent search and indexing service powered by AI.
- Key Capabilities:
- Natural language processing search queries.
- Cognitive skills for image, OCR, and text analytics.
- Synonym search and ranking capabilities.
- Typical Use Cases:
- Website and application content search
- Enterprise data search with AI-driven insights
- E-commerce product search