Azure AI portfolio and its offerings - cheat sheet

 Courtesy: Almost all the information in this blog has been compiled from these 2 YouTube videos. So thanks to the original creators.

https://www.youtube.com/watch?v=qJGRd34Hnl0

An introduction to Microsoft Azure AI | Azure AI Essentials

https://www.youtube.com/watch?v=8aMzR8iaB9s

AZ-900 Episode 16 | Azure Artificial Intelligence (AI) Services | Machine Learning Studio & Service

Azure AI portfolio has options for every developer may it be in the form of

Pre-built AI models

• Advanced machine learning capability or

• Low code/ no code development experience

Azure cognitive services provide the most comprehensive portfolio of customizable AI models in the market. It includes

• Vision,

• Language,

• Speech &

• Decision.

It just needs an API call to integrate them to our applications.

Users can customize AI models using one’s own data without any machine learning expertise required. These models can also be deployed to containers so it can be run from anywhere.

For Business users Azure provides access to the same AI models through AI Builder which provide a no-code experience to train models and integrate them into apps within Microsoft Power Platform.

For common solution like chatbot and AI powered search, services are provided, which accelerate development for these solutions. These scenario specific services often bring together multiple cognitive services along with business logic and a user interface to solve for a common use case.

If we are looking to develop advanced machine learning models, Azure Machine Learning enables to quickly build, train and deploy machine learning models with experiences for all skill levels ranging from code first to a drag and drop no code experience.

It provides services that empowers all developers. It helps in the entire process by providing us with a set of tools. The processes include –

• · Training the model

• · Packaging and validating the model

• · Deploy the model as web services

• · Monitoring those web services

• · Retraining the model to get even better results.

Set of tools mentioned above include –

• Notebooks written in python/R

• Visual designer which allows us to build machine learning models using a simple drag and drop experience directly in our browsers.

• Machine learning model allows us to manage all the compute resources where train, package, validate and deploy those models so that we don’t have to worry about Azure infrastructure and underlying resources ourselves.

• Additionally, Azure machine learning comes with something called automl. This automated process allows us to perform different algorithms with our data and see which one scores the best and deploy that as our designated web service.

• Features of pipelines which allows us to build the entire process end-to-end.

Complete end to end solution for building machine learning models.