Azure Open AI & Prompt Engineering Zero to Hero with Chatgpt

Learn how to use Azure OpenAI with OpenAI to build powerful applications with minimal code through hands-on labs. Benefit from practical labs over theory, guided by an experienced trainer.

Explore Azure OpenAI fundamentals, cover concepts, models, and features, and examine real-world use cases before diving into labs and application building.

Explore how Azure OpenAI provides access to OpenAI models via APIs, enabling you to deploy and fine-tune chatbots, language translation, sentiment analysis, and computer vision within Azure.

Explore Azure OpenAI features for sentiment analysis, NLP, chat bots, speech recognition, and text summarization. Apply machine learning, deep learning, computer vision to fraud detection, predictive maintenance, and personalized marketing.

Explore real-world use cases of azure openai across industries, from uber's ride-query chatbot and reddit's ai moderation to medical imaging diagnostics and starbucks' ai powered virtual assistant for orders.

Compare OpenAI and Azure OpenAI to see they share APIs for training and fine-tuning models like GPT-4, GPT-3, Codex, and Dalet, while Azure adds networking, regional availability, and content filtering.

Learn how to request access to Azure OpenAI by filling the apply now form with your name, subscription ID, contact details, and region selections.

Explore prompts and completions within Azure OpenAI, labeling the prompt input and model-generated completion while grounding concepts with practical examples and token basics.

Explore how Azure OpenAI processes text by breaking it into tokens, which can be words or character chunks. Learn how token length impacts latency and throughput for prompts and responses.

Explore tokens, prompts, and completions, and see how token counts drive processing and output in prompt interactions.

Explore learnings in prompt engineering: few-shot, one-shot, and zero-shot approaches, showing how multiple or single examples in prompts guide model behavior and training versus direct use.

Explore the Da Vinci model in Azure OpenAI for deep content understanding, summarization, and content generation, and apply it to tasks like email prioritization and large-content summarization.

Compare the Curie model with Da Vinci in speed and depth, showing when to use Query for sentiment, classification, summarization, and question answering.

Explore the Babbage model's simple classification, sentiment analysis, and semantic search for ranking documents, and learn why it's faster for straightforward tasks than DaVinci.

Explore Ada and Eda models, emphasizing fast text parsing, grammar corrections, and lightweight classification, while showing how more context and data boost performance.

Codex enables writing, fixing, and optimizing code, leveraging GPT-3 based models trained on natural language and billions of lines of public GitHub code.

Understand prompt engineering: design prompts, fine-tune GPT models with curated datasets, and use Azure OpenAI APIs to produce accurate, engaging responses for specific tasks.

Explore the Azure OpenAI API's generative capabilities by fine-tuning models to generate new ideas and outputs, improving accuracy, and creating business plans, descriptions, and slogans.

Create an Azure OpenAI resource by selecting subscription, resource group, east US region, and name, set pricing tier and network, add environment demo tag, then validate and deploy.

Explore a hands-on first look at Azure OpenAI Studio, create deployments with DaVinci 003 and Code DaVinci, and fine-tune models using one-shot or zero-shot learning.

Explore how to fine-tune and train models in Azure OpenAI Studio using the playground to generate responses and adjust prompts. Watch token behavior and use case examples.

Explore Azure OpenAI Studio using the DaVinci model to generate emails, subject lines, and bodies with predefined and custom prompts, and learn summary and posting workflows.

Explore how to generate Python scripts and bash scripts using Code DaVinci and Codex models in the playground, compare results, and fine-tune for better code generation.

Understand data sets and how high-quality data enables fine tuning of models, guiding you to upload datasets in Azure OpenAI for tailored, higher quality responses.

Fine-tune your model with a high-quality dataset in Azure OpenAI to tailor results and reduce latency. Avoid incorrect or inappropriate answers by fine-tuning with high-quality data.