Cheat Sheet forAzure Machine Learning and AI services

Cheat Sheet forAzure Machine Learning and AI services

This is a cheat sheet/summary of the Microsoft Azure ML services on cloud.

Azure Bot Service

The term ‘bot’ is used here to describe an experience more human-like than most computer interactions (e.g. Alexa or Cortana). These sit in Microsoft online services and can access files, APIs, and Microsoft databases to complete the task allocated by the user.

Azure bot service specifically offers the SDK framework to develop bots, with frameworks to help set-up and deploy the bots. Note that the SDK for Python is still under development, so would require the use of other languages (C#, JavaScript, or Typescript).

Azure Cognitive Search

Search through your database with Natural Language Processing. This is “search-as-a-service”, with the sort of capabilities you’re used to with Google (auto-complete, filtering, etc.). The vision sold here is that if you want to just dump your scanned customer contracts into a database and then search by location, or date, or customer, the search will return such data without you working through each individual contract.

Microsoft Genomics

Process genomes using scalable Azure resources (genome sequencing is very resource-intensive, so this is a way to do it without owning suitable architecture).

Azure Machine Learning

Environment to create machine learning models. For some data types simply drop data in and it’ll do lots of computation to find the best model. Uses a flowchart of modules rather than code, theoretically making it harder to make mistakes. Has a button to deploy models.

Machine Learning Studio (classic)

Similar to Azure Machine Learning. Less integrated to Azure, meaning no need to access an Azure Blob storage or create Azure compute. Easier to jump in and “have a go” than AzureML, but with less scalability.

Azure Cognitive Services

A collection of APIs and SDKs for specific machine learning tasks in the categories of Vision, Speech, Language, Web Search, and Decisions. The data is brought to prebuilt models, thus these services can be used without having training data available. These are meant to be accessible to developers who haven’t got experience in machine learning (Microsoft states AzureML is better tailored for data scientists due to the extra flexibility).

Azure Open Datasets

Curated datasets can be used to enhance models. For example, curated weather data and records of public holidays may be used to help explain variations in retail footfall. These are free to use within Azure services, though any compute cost associated will still be charged. Unfortunately, these datasets seem to be very US-centric.

azure azure-services

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