Explain the key differences between Azure Cognitive Services and Azure Machine Learning, and provide examples of scenarios where each would be most suitable.

Azure Cognitive Services and Azure Machine Learning are both powerful services offered by Microsoft Azure, but they serve different purposes in the field of artificial intelligence. Understanding their key differences and use cases is essential.

Azure Cognitive Services is a collection of pre-built AI models and APIs that enable developers to integrate various cognitive capabilities into their applications without requiring extensive knowledge in machine learning or data science. These services offer ready-to-use functionality for vision, speech, language, search, and decision-making tasks. Examples of Azure Cognitive Services include Computer Vision, Text Analytics, Speech to Text, and Translator.

Azure Cognitive Services is most suitable for scenarios where you need to add intelligent capabilities to your applications quickly and easily. For instance:

1. In a retail setting, you can use Computer Vision to analyze product images and automatically extract product information, such as SKU numbers or descriptions.
2. In a customer support system, you can utilize the Language Understanding service to extract intent and entities from customer queries, enabling automated routing or response generation.
3. In a media streaming platform, you can leverage the Speech to Text service to transcribe audio content and make it searchable.

On the other hand, Azure Machine Learning (AML) provides a comprehensive platform for building, training, and deploying custom machine learning models. It empowers data scientists and ML engineers with tools and frameworks to develop sophisticated models tailored to their specific business needs. AML supports a range of tasks such as data preprocessing, feature engineering, model training, and model deployment.

Azure Machine Learning is most suitable for scenarios where you require a higher degree of customization and control over the machine learning process. Some examples include:

1. Building a recommendation system for an e-commerce platform, where you need to develop a custom recommendation algorithm based on user behavior data.
2. Developing a predictive maintenance solution for manufacturing equipment, where you build a model to detect potential failures based on sensor data.
3. Creating a natural language processing model specific to your industry or domain, such as sentiment analysis for customer feedback in the hospitality sector.

In summary, Azure Cognitive Services is ideal for quickly adding AI capabilities to applications without extensive ML knowledge, while Azure Machine Learning is designed for developing custom ML models and workflows tailored to specific business requirements. Choosing between them depends on the level of customization and control needed, as well as the complexity of the AI task at hand.