Azure Machine Learning, Azure Synapse Analytics and Spark using Azure Databricks are cloud services that you can use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. Azure Cognitive Services are APIs/SDKs/services available to help developers build intelligent applications without the need for AI or data science skills/knowledge. Azure Cognitive Services enable developers to easily add cognitive features such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The event will be held in English.
Christina Lee joins Scott Hanselman to show what's new in Azure Cognitive Services. Cognitive Services bring AI within reach of every developer--without requiring machine-learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate decision-making into your apps. Create a free account (Azure) https://aka.ms/azfr/592/free
To build an effective and scalable solution, developers need technology that can be deployed around the world and still provide results with high confidence. To that end, we've spent the last year investing in making our Cognitive Services enterprise-ready and bringing them to general availability, ready for production use. Cognitive Services are a set of intelligent APIs and services that are used by more than 1.2 million developers and thousands of businesses throughout 150 countries across every industry from retail to healthcare to public sector to manufacturing and non-profit organizations. We've deployed more services into the Azure data centers around the world, written more documentation in multiple developer languages, re-architected products to change the way we store and retain data in order to give controls to users over their data, adhering to the highest standards available. All while meeting strict SLA standards that we require for every Azure service.
When we refer to AI solutions, we are doing it to the ability to reproduce some human behavior, and that is where these services take their name, hence the Cognitive. Basically, these are pre-trained models, but customizable, which allows us to implement some very clear use cases. Let's see the specific characteristics of each of these groups to see what are the cases that can help us deploy The best thing about all these models is that all of them have the possibility of having a free tier, quantified in monthly transactions, which allows not only to test them, but they can even be valid for scenarios in which the number of transactions is not very high, and we want to start making our first "first steps" with Artificial Intelligence. I believe that they are the perfect companion, in the early stages of maturity in the adoption of AI within an organization and allow, in a simple and economical way, to evaluate the real impact that the integration of this type of solution can have on our business processes. Follow us on Twitter and LinkedIn.
We are really excited to introduce the preview of new machine learning experiences in Azure Synapse Analytics, to make it easier for data professionals to enrich data and build predictive analytics solutions. AI and machine learning is an important aspect of any analytics solution. By integrating Azure Synapse Analytics with Azure Machine Learning and Azure Cognitive Services, we are bringing together the best of two worlds, to empower data professionals with the power of predictive analytics and AI. Data engineers working in Azure Synapse can access models in Azure Machine Learning's central model registry, created by data scientists. Data engineers can also build models with ease in Azure Synapse, using the code-free automated ML powered by Azure Machine Learning and use these models to enrich data.