Learn how to quickly and easily get started with Artificial Intelligence using IBM Watson.

This course focuses on the core concepts and models of generative AI, including deep learning and large language models. It covers the concept of foundation models and the capabilities of pre-trained models and platforms for AI application development.

Learn how to use Node-RED to augment the capabilities of your Watson Assistant chatbots by integrating services such as Watson Translate and Text to Speech. You'll also practice deployment of chatbots to Facebook Messenger.

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

This course will teach you how to identify, evaluate, and implement generative AI opportunities within your company to achieve strategic business goals.

Implement a neural network model for performing real-time image classification on a secured, internet-connected microcontroller-based device (Azure Sphere). Describe the components and steps for implementing a pre-trained image classification model on Azure Sphere.

Classify images by training a custom model with Azure AI Vision.

Explore image recognition techniques using machine learning, focusing on practical implementations and algorithms.

Use more advanced features of Azure AI Search to improve your existing search solutions. Learn how to change the ranking on documents, boost terms, and allow searching in multiple languages.

Define a solution for smoke testing for virtual Azure IoT Edge devices. Your solution employs a CI/CD (continuous integration/continuous deployment) strategy using Azure DevOps and Azure Pipelines on a Kubernetes cluster.

Do you have information locked up in structured and unstructured data sources? Using Azure AI Search, you can extract key insights from this data, and enable applications to search and analyze them.

This course covers LangChain integration and automation in data pipelines. The topics include automating workflows, data flow management, ETL processes, connecting LangChain with other systems, maintaining and monitoring workflows, and scaling.