Machine learning is a very powerful tool for businesses and researchers to create predictions for data problems. However, there are many steps to creating models and not every model is suited for each problem. We have to use a combination of human judgment and computational techniques to create the right model.
The most valuable lesson from our recent work was the time invested in identifying opportunities on how best to leverage AI to enhance our customer’s solutions before delving into architectural decisions and proceeding with a proof of concept.
Machine learning is the scientific study of algorithms and statistical models that result in devices automatically learning and improving from experiences without being explicitly programmed. With so much success integrating machine learning into our everyday lives, the obvious next step is to integrate machine learning into even more systems.
In this blog, I will demonstrate how Text Analytics API Version 3 Preview of the Microsoft Azure Cognitive Services can be used to analyze large unstructured data. This analysis aims to understand the sentiments expressed in a solicited public comment process and determines the degree of the positivity or negativity of the comments.
Whether you’re just starting off in tech, building, managing, or deploying apps, gathering and analyzing data, or solving global issues —anyone can benefit from using cloud technology. In this post, we will explore some practical examples where Azure AI is driving innovation.
Where would you find all three (AI, ML and DS) at work? The most common place today is in autonomous driving vehicles. All three disciplines work together to help train an algorithm to recognize obstacles (MS), then to provide real-time actions (AI) to the vehicle, all based on large amounts of information that data science (DS) can analyze.
Translating text can be tricky business. Sure, by using online services and tools, you can quickly find out how to say “Hello”, "that dog is green", and “where’s the closest taco stand?” But what about when the conversation needs to be more contextual and specific to your company/business/audience?
Application Development Manager Rich Maines explores the art of the possible with AI in the context of Microsoft services and the ethical principles that we believe should drive the development of AI.
From the ready-to-consume set of Azure Cognitive Services to the comprehensive set of tools for data scientists available in Azure Machine Learning Service, there are many ways to apply AI into your products and services.
Microsoft’s vision is to democratize AI and make it accessible and valuable to everyone. Join us to learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning.
