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Generative AI for Business with Microsoft Azure OpenAI

Generative AI for Business with Microsoft Azure OpenAI

Learn generative AI with code & no-code on Azure & OpenAI

Application closes 12th Dec 2024

  • Program Overview
  • Curriculum
  • Certificate
  • Tools
  • Projects
  • Faculty
  • Fees

Key Highlights of the Generative AI Program

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    AI-900 Training by Microsoft Certified
    Trainers (Optional)

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    Prompt Engineering without and with code

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    Azure Lab access with OpenAI Studio

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    Learn from experienced industry mentors

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    8+ hands-on case studies, 2 hands-on projects + 2 additional projects

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    Get personalised assistance with dedicated Program Manager and Academic Support

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    Get a Microsoft Applied Skill Badge

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    Get a Free AI-900 Exam Voucher

Skills you will learn

  • Prompt Engineering
  • Using OpenAI API
  • Using Python SDK for Prompt Engineering
  • Microsoft Azure Cloud Services for AI

Globally recognized education platform

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Great Learning alumni work at top companies

Curriculum

This program, structured into four distinct modules, offers an in-depth understanding of Azure OpenAI and Generative AI. It begins with Module 1, which introduces the fundamentals of AI, Machine Learning (ML), Large Language Models (LLMs), and Prompt Engineering, along with an overview of Azure's OpenAI services. Module 2 focuses on the Python skills needed to work effectively with generative AI on a large scale. In Module 3, learners gain hands-on experience with the Azure OpenAI API key and Python SDK, exploring practical applications of Generative AI in tasks such as text classification and summarization. The final module, Module 4, prepares participants for the AI-900 Certification Exam. By the program's conclusion, participants will be equipped with the knowledge and skills to leverage Generative AI in various applications, ranging from generating content to crafting effective prompts.

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Module-1: Leveraging Generative AI for Business Applications

The module revolves around three core pillars - understanding Generative AI, exploring Azure OpenAI services, and mastering Prompt Engineering. In this enriching journey, you will delve into foundational concepts of AI, Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and their applications across various industries. You will gain hands-on experience with cutting-edge generative tools and explore the vast capabilities of Azure OpenAI services. Lastly, you will learn the intricate art of Prompt Engineering, mastering the design and implementation of effective prompts without coding.

Week-1: ML Foundations for Generative AI

The outcome of this week is to understand foundational Machine Learning principles that enable Generative AI to perform tasks like creating new content, such as text and images, by learning from extensive datasets.

  • Mathematical Foundations of Generative AI
  • Understanding Machine Learning for Generative AI
  • Connect NLP fundamentals with advanced Generative AI applications

Week-2: Generative AI: Business Landscape & Overview

The outcome of this week is to understand the Generative AI Landscape, fundamentals, and possibilities for businesses to solve problems and create products.

  • Understanding Generative and Discriminative AI
  • A brief timeline of Generative AI
  • A peek into generative models
  • Deconstructing the behavior of a large language models
  • ML, DL, and GenAI applications in business
  • Hands-on Demonstration of popular tools (ChatGPT & DALL-E)

Week-3: Prompt Engineering without Code

The outcome of this week is to gain practical knowledge of Prompt Engineering and the ability to do it without code for various business use cases.

  • LLMs and the genesis of Prompting
  • How does the Attention Mechanism work? 
  • A brief history of the GPT model series
  • Accessing GPT through Azure
  • Designing prompts for business use cases using playground templates
  • Prompting techniques (Prompt templates, precise instructions, chain of thought prompting)
  • Ideating for prompts (prompt generation by induction, prompt paraphrasing)
  • Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
  • Learn the capabilities of DALL-E in the Azure openAI service and Use the DALL-E playground in Azure OpenAI Studio

Week-4: Project: Product Feedback Review & Sentiment Analysis

Problem Statement: Amazon needs an automated system that can efficiently analyze product reviews, extract critical information, and determine the sentiments expressed by customers. The solution should help the company gain insights into product performance and customer satisfaction.

Module-2: Python for Generative AI

This module prepares participants with vital Python skills for large-scale generative AI tasks, focusing on coding techniques, libraries, and frameworks essential for development, deployment, and scaling. Whether you’re a seasoned programmer looking to expand your AI knowledge or a complete beginner interested in the field, this module will set you up with the programming skills you need.
 

 

Week-5: Python for Prompt Engineering : Part-1

This week's goal is to swiftly deepen grasp and expertise in the basics of Python. Concentrating on these fundamental elements, we strive to establish a robust foundation for tasks related to Python.

  • Variables
  • Data types
  • Data Structures 
  • Conditions and Loops
  • Functions
  • Strings
  • Use natural language prompts to write code

Week-6: Python for Prompt Engineering: Part-2

The outcome from this week is to get up to speed on the Python concepts that are needed to automate prompt engineering at scale and understand the cost implications of using APIs.

  • Store text in Python
  • Edit, add, and delete text in Python
  • How to read files in Python
  • How to work with a database
  • Manipulate string columns

Week-7: Learning Break

Module-3: Designing Generative AI Solutions with Azure Open AI

This advanced module plunges deeper into the workings of LLMs, teaching you how to automate prompt engineering and other Generative AI applications at scale using Python. Learn to set up your Azure Open AI API key and import the Python library/SDK to work with various Generative AI models. Master the Completions API, ChatCompletions API, and Embeddings API, understanding their rates, limits, and pricing. The course then moves to practical applications of Generative AI in text classification and summarization, with hands-on exercises such as classifying medical records and assigning themes to finance news articles. Additionally, get a Microsoft Applied Skill Badge.

 

Week-8: Prompt Engineering at Scale

The outcome of this week is to learn how to use the Azure Open AI API key and the Python SDK to leverage Generative AI at scale for solving business problems

  • Getting set with your Azure Open AI key and Python SDK
  • Completions and Chat API
  • Kinds of APIs, Models, Token, Rate Limits and Pricing
  • Evaluating Generative AI Outputs
  • Generate completions to prompts and begin to manage model parameters
  • Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses

Week-9: Classification Tasks with Generative AI

The outcome of this week is to learn how to use Prompt Engineering to solve classification-type problems

  • Framing text classification tasks as Generative AI problem
  • Sentiment classification
  • Assigning themes to a body of text 
  • Aspect-based sentiment analysis

Week-10: Content Generation and Summarization with Generative AI

The outcome of this week is to learn how to use Generative AI for content generation tasks across various business problem spaces

  • Content generation using Generative AI 
  • Abstractive summarization
  • Text generation 

Week-11: Information Retrieval and Synthesis workflow with Gen AI

The outcome of this week is to learn how to setup an information retrieval and synthesis workflow on Azure or a local environment for a business use-case

  • Overview of advanced application of Generative AI 
  • Understand information retrieval and synthesis workflow using Azure Open AI
  • Effectively communicate the core concepts of Retrieval-Augmented Generation (RAG) with the help of the LangChain package
  • Use Azure OpenAI API to generate responses based on your own data

Week-12: Final Project: Aspect-based Classification for Sentiment Analysis

Problem Statement: The objective of this problem statement is to use aspect-based classification for sentiment analysis to identify the aspects of a product or service that customers are most satisfied with and those that need improvement. This will help businesses understand their customers better and make data-driven decisions to improve their products or services. By improving customer satisfaction and loyalty, businesses can increase customer retention rates, reduce churn rates, and ultimately increase revenue.

Module-4: AI-900: Azure AI Fundamentals (Optional 4-week elective)

This module is designed to provide a foundational understanding of machine learning, AI concepts, and associated Microsoft Azure services. While Azure AI Fundamentals can be beneficial in preparing for Azure role-based certifications such as Azure Data Scientist Associate or Azure AI Engineer Associate, it's important to note that it is not a mandatory prerequisite for any of these certifications.

Week-13: Machine Learning workloads on Azure

Identify characteristics of standard machine learning workloads, comprehend foundational principles of ML, and become acquainted with prevalent machine learning methodologies

  • Identify regression, classification, and clustering machine learning scenarios
  • Identify features and labels in a dataset for machine learning
  • Describe the capabilities of Automated machine learning
  • Describe data and compute services for data science and machine learning
  • Describe model management and deployment capabilities in Azure Machine Learning

Week-14: Computer Vision workloads on Azure

Recognize various computer vision solution types and discover Azure tools for handling computer vision tasks.

  • Identify common types of computer vision solution
  • Identify features of optical character recognition solutions
  • Capabilities of the Azure AI Vision service
  • Capabilities of the Azure AI Face detection service

Week-15: Natural Language workloads on Azure

Identify features of typical NLP workload scenarios and explore Azure tools and services applicable to NLP workloads.

  • Identify features and uses for key phrase extraction
  • Identify features and uses for entity recognition
  • Identify features and uses for language modeling
  • Identify features of common NLP Workload Scenarios
  • Identify Azure tools and services for NLP workloads

Week-16: Generative AI workloads on Azure

Focus on recognizing features of generative AI solutions and understanding the capabilities offered by the Azure OpenAI Service.

  • Identify features of generative AI solutions
  • Identify capabilities of Azure OpenAI Service

Earn a Certificate from Microsoft Azure

Enhance your resume with a certificate in Generative AI for Business with Microsoft Azure OpenAI from Great Learning and Microsoft Azure and share it with your professional network

Microsoft Azure certificate

* Image for illustration only. Certificate subject to change.

Industry-relevant syllabus

Learn Top In-Demand Tools

Gain hands-on experience with cutting-edge tools and explore the vast capabilities of Generative AI

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    Azure AI Services

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    Python

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    Azure OpenAI Service

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    Azure OpenAI Studio

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    Azure OpenAI Chat API

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    Azure OpenAI Playground

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    Azure OpenAI Completion API

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    GPT-3.5-Turbo

Data sets from the industry

Work on Industry-Relevant Projects

Find below an indicative list of hands-on projects during the course of the program

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Product Feedback Review & Sentiment Analysis

The objective of this project is to create a prompt template that performs sentiment analysis on product reviews. The model should extract relevant information, such as product names, reviewer names, review ratings, review descriptions, and sentiment (positive or negative), to assist the company in understanding customer feedback better.
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Aspect-based Classification for Sentiment Analysis

The objective of this project is to use aspect-based classification for sentiment analysis to identify the aspects of a product or service that customers are most satisfied with and those that need improvement. This will help businesses understand their customers better and make data-driven decisions to improve their products or services. By improving customer satisfaction and loyalty, businesses can increase customer retention rates, reduce churn rates, and ultimately increase revenue.
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Improving operational efficiency and customer satisfaction

ExpressWay Logistics grapples with challenges in delivery efficiency and customer satisfaction, struggling with parcel management and maintaining a skilled workforce. To address these issues, the company is focusing on a comprehensive strategy that leverages advanced technology and strategic planning, with a particular emphasis on analyzing customer sentiment across digital platforms. This approach is aimed at enhancing operational efficiency and elevating the quality of service.
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Extract insights from customer feedback

Within the highly competitive realm of online retail, the significance of customer feedback cannot be overstated in its influence on user experience optimization and business expansion. As a product analyst at a prominent e-commerce company specializing in items such as footwear, electronic gadgets, and groceries, your task is to harness the power of Generative AI to convert unstructured customer feedback into valuable insights. This initiative will aid in informing strategic planning, refining the platform, and securing a satisfying purchase experience for consumers.

Meet Your Faculty and Mentors

Learn from highly skilled professionals in the ML field who have engineered Generative AI solutions across industry verticals & have real-world, hands-on work experience

  • Dr. Abhinanda  Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Academic Director - Data Science & Machine Learning,
    Ph.D. from Stanford University, Ex-Faculty - MIT

  • Connor Hagen - Faculty Director

    Connor Hagen

    Lead Architect, Microsoft Azure OpenAI & AI Co-Innovation Labs

  • Dr. Pavankumar Gurazada - Faculty Director

    Dr. Pavankumar Gurazada

    Senior Faculty & Director Academics, Great Learning

    Dr. Pavankumar Gurazada, holding a Ph.D. in Applied Machine Learning, specializes in deep learning and AI. He has published extensively in top journals and focuses on building AI-driven systems for Industry 4.0. Dr. Gurazada also serves as a data science advisor and board member for AI-focused startups.

    Read more

  • Vinicio DeSola - Faculty Director

    Vinicio DeSola

    Senior Data Scientist, Aspen Capital

  • Anuj  Saini - Faculty Director

    Anuj Saini

    AI Research Scholar,Université de Montréal

  • Davood  Wadi - Faculty Director

    Davood Wadi

    AI Research Scientist, intelChain

  • Fred  Premji - Faculty Director

    Fred Premji

    Principal AI/ML Engineer, OPTMAL

  • Hassan  Saidinejad - Faculty Director

    Hassan Saidinejad

    Data Scientist, Intact

Get industry ready with dedicated career support

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    CAREER PREP SESSION

    Apply the program skills for professional advancement

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    E-PORTFOLIO

    Create a Professional Portfolio Demonstrating Skills and Expertise

Program Fee

Program Fees: 1,700 USD

Apply Now

Payment Partners

affirm - Payment Partner Climb Credit - Payment Partner

Benefits of learning with us

  • 16-week online learning
  • Microsoft Azure Lab access with OpenAI Studio
  • Prompt Engineering without and with code
  • 8+ Hands-on Case Studies, 2 Hands-on Projects + 2 Additional Projects
  • Certificate of Completion from Microsoft and Great Learning
  • Get a Microsoft Applied Skill Badge

Batch Start Date

Frequently asked questions

Frequently Asked Questions

What is this Generative AI course?

The Generative AI for Business is a comprehensive 16-week online learning program offered by Microsoft Azure OpenAI. This program is designed to equip you with the knowledge and skills to leverage the power of Generative AI, Prompt Engineering and Large Language Models to solve real-world business problems.

 

Key Program Elements:
 

  • Foundational Learning: Gain a solid understanding of Generative AI concepts and their applications across diverse business scenarios.

  • LLM Fundamentals: Explore the core functionalities of LLMs and how to utilize them effectively.

  • Prompt Engineering: Craft effective prompts to guide LLMs and generate desired outputs, both with and without coding.

  • Hands-on Learning: Deepen your knowledge through practical exercises, 8+ case studies, and 4 project development activities using the Azure cloud platform, and get the “Microsoft Applied Skill Badge.”

  • Azure OpenAI Integration: Learn to leverage Azure OpenAI Studio, APIs, and Python SDKs to build data-driven services within the Azure environment.

  • Career Advancement: Pursue an optional 4-week elective focused on core Azure AI functionalities, preparing you for the Azure AI Fundamentals offering AI 900 certification – a valuable asset for career growth in AI.

 

Learning Methodology:

The program emphasizes a "learning by doing" approach, fostering practical skills development through real-world case studies and project building. This hands-on experience equips you with a portfolio that demonstrates your capabilities and aids your transition into high-demand fields like data science and artificial intelligence.

What is unique about this Azure OpenAI course?

This Azure OpenAI course offers a unique blend of comprehensive training, practical application, and career-oriented benefits:
 

  • Extensive Hands-on Learning: Go beyond theory with industry-oriented 8+ hands-on case studies and 4 dedicated projects. This immersive experience allows you to solidify your understanding and build a portfolio showcasing your real-world Generative AI skills.

  • Industry-recognized Certification Preparation: Gain valuable preparation for the sought-after AI-900: Azure AI Fundamentals certification delivered by Microsoft Certified Trainers. This Microsoft Generative AI certificate validates your knowledge and strengthens your resume for AI-focused careers.

  • Practical Skill Development: Thoroughly understand prompt engineering, a crucial skill for working with LLMs. This course empowers you to craft effective prompts, both with or without coding, unlocking the full potential of Generative AI tools.

  • Diverse Generative AI Applications: Explore practical applications of Generative AI through modules on Text Classification, Summarization, and Generation. This equips you with a versatile skillset applicable to various business scenarios.

  • Real-world Development Environment: Gain practical experience working within the Azure cloud platform. You will have access to Microsoft Azure Labs with OpenAI Studio, allowing you to experiment and build Generative AI solutions in a simulated environment.

  • Career-Boosting Credentials: Upon completion, you will receive a Certificate of Completion jointly issued by Great Learning and Microsoft. Additionally, you will earn a valuable Microsoft Applied Skills badge in "Develop GenAl Solutions with Azure OpenAI Service," further enhancing your professional profile.

  • Comprehensive Support: Throughout the program, you will benefit from a dedicated program manager and academic support from Great Learning to ensure your learning experience is smooth and successful.

 

This combination of in-depth learning, practical exercises, industry-recognized credentials, and career-oriented resources makes this Azure OpenAI course an exceptional opportunity to propel your skillset and advance your career in the exciting field of Generative AI.

What do I learn from this Microsoft AI course?

This Microsoft AI course equips you with a comprehensive understanding of Generative AI and its practical applications in business. 

 

Here's a breakdown of the key learning outcomes:
 

  • Foundational Generative AI Knowledge: Gain a solid grasp of GAI's history, current landscape, and future potential. Learn how to practically apply this technology to solve real-world problems and build impactful services.

  • Mastering the Microsoft Azure OpenAI Platform: Leverage the complete potential of the Microsoft Azure OpenAI platform to utilize Generative AI capabilities effectively.

  • Scaling Prompt Engineering: Explore leveraging APIs and Python SDKs to scale your prompt engineering efforts, ensuring efficiency and effectiveness.

  • Business-Oriented Prompt Engineering: Develop expertise in crafting prompts specifically designed for various business use cases. This allows you to extract maximum value from Generative AI solutions.

  • Practical Generative AI Applications: Gain a working knowledge of how to apply Generative AI for core business tasks like natural language classification, summarization, and generation.

  • Large Language Model Optimization: Understand how to fine-tune LLMs to achieve desired outputs, ensuring your GAI solutions deliver accurate and relevant results.

  • Enterprise-Level GAI Thinking: Develop a strategic perspective on implementing Generative AI solutions within an enterprise environment.

  • Hands-on Coding Skills: Learn to write basic code snippets that interact with LLM APIs and enable large-scale prompt engineering. This equips you with the practical skills to build and deploy GenAI solutions.

 

By completing this course, you will develop a well-rounded understanding of Generative AI, its business applications, and the technical skills to implement it effectively within your organization.

How is learning structured for this program?

This program takes a structured, modular approach to learning, ensuring a progressive development of your Generative AI expertise. 

 

Here's a breakdown of the four distinct modules:
 

  • Module 1: Foundational Knowledge

This module establishes a strong base by introducing core concepts of Artificial Intelligence, Machine Learning, Large Language Models, and Prompt Engineering.

Additionally, you will gain a comprehensive overview of Microsoft Azure's OpenAI services, familiarizing you with the available tools and functionalities.
 

  • Module 2: Python Programming for GAI

This module focuses on developing essential Python programming skills. Python is a widely used language for working with Generative AI, and this module equips you to handle large-scale GenAI applications effectively.
 

  • Module 3: Hands-on Generative AI Applications

This hands-on module provides practical experience with the Azure OpenAI API key and Python SDK. You will explore real-world applications of Generative AI, exploring tasks like text classification and summarization.
 

  • Module 4: Preparing for AI-900 Certification (Optional)

The final module focuses on preparing you for the sought-after AI-900 certification exam. This optional module validates your understanding of core Azure AI functionalities and strengthens your AI career prospects.

 

This structured learning journey ensures a strong foundation in core concepts. It is followed by practical application through hands-on exercises, culminating in the opportunity to earn a valuable industry credential.

What projects are included in this Microsoft Azure OpenAI course?

This Microsoft OpenAI certificate course incorporates engaging projects that enable you to apply your knowledge to practical business scenarios. 

 

Here are a few examples:
 

  • Product Review Analysis: Develop a system for analyzing product reviews using sentiment analysis. This project will involve creating prompts to extract key information like product names, ratings, and customer sentiment, helping companies gain valuable insights from customer feedback.

  • Aspect-Based Sentiment Analysis: Take sentiment analysis a step further by identifying specific aspects of a product or service that customers are happy or dissatisfied with. This project equips you with the skills to help businesses understand customer needs and make data-driven decisions for improvement.

  • Optimizing Logistics with Generative AI: Explore how Generative AI can address the challenges faced by logistics companies, potentially improving delivery efficiency and customer satisfaction.
     

Extracting Insights from E-commerce Feedback: Learn to harness Generative AI to analyze unstructured customer feedback in the e-commerce industry. This project equips you with skills to gain valuable insights for optimizing user experience and driving business growth.

How much does this Microsoft Generative AI course cost?


The Microsoft AI certificate course costs INR 1,20,000 + GST. For more details on flexible fee payments, please contact your Program Advisor.

Where can I apply the skills gained from this course?

The skills you gain from this Generative AI for Business with Microsoft Azure OpenAI course can be applied across various industries and job functions. 

 

Here are some potential areas where your expertise can be valuable:
 

  • Data Science and Machine Learning: This course strengthens your foundation in core AI concepts like Machine Learning and Large Language Models, complementing your existing data science skillset.

  • Business Intelligence and Analytics: Generative AI offers powerful tools for analyzing vast amounts of data. You can leverage your skills to extract valuable insights for businesses, informing strategic decision-making.

  • Content Creation and Marketing: Generative AI has the potential to revolutionize content creation. You can apply your skills to develop creative content strategies, automate tasks, and personalize marketing campaigns.

  • Customer Service and Experience: Generative AI can be used to build chatbots and virtual assistants, enhancing customer service interactions. Your skills can be instrumental in developing these solutions to improve customer experience.

  • Product Development and Innovation: Generative AI allows innovative product design and development. You can utilize your knowledge to explore new product ideas and functionalities.

How to build a Generative AI model?

Building a Generative AI model involves several key steps:
 

  • Define the Goal: Clearly define the problem you want your GAI model to solve. What kind of outputs do you want it to generate (text, code, images, etc.)? What data will it be based on?

  • Data Collection & Preprocessing: Gather a high-quality dataset relevant to your chosen task. This data will train the model and shape its ability to generate new outputs. Preprocessing often involves cleaning, organizing, and formatting the data to ensure the model can understand and learn from it effectively.

  • Choose the Right Model Architecture: Different GAI model architectures are suited for various tasks.  Some popular options include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. Researching available architectures and their strengths for your specific goal is crucial.

  • Model Training: Train your chosen model on the prepared dataset. This can be a computationally intensive process, requiring powerful hardware and potentially taking significant time, depending on the model complexity and dataset size.

  • Validation & Refinement: Evaluate your trained model's performance. How well does it generate the desired outputs? Does it meet your quality standards? This iterative process often involves adjusting hyperparameters, the settings that control the model's training process, and potentially refining the model architecture for better results.

  • Deployment (Optional): You can deploy your model when its performance is iterated and optimized for real-world use. This might involve integrating it into an application or service that generates outputs based on user input or specific tasks.

 

Here are some additional points to consider:
 

  • Prompt Engineering: Crafting effective prompts is essential for guiding GAI models to generate the desired outputs. Writing clear and concise prompts is a valuable skill for working with generative models.

  • Computational Resources: Training GAI models often requires significant computing power. Cloud platforms like Azure OpenAI offer resources and tools to facilitate this process.

  • Ethical Considerations: Be mindful of potential biases in your training data and how they might influence the model's outputs. Additionally, the ethical implications of using GAI models, such as potential misuse to generate fake content, should be considered.

 

Building a GAI model can be a complex process, but with careful planning, the right tools, and a solid knowledge of the core concepts, you can create powerful tools for various applications.

Still have queries? Let’s Connect

Get in touch with our Program Advisors & get your queries clarified.

Speak with our expert +1 425 357 7290 or email to microsoft-gen-ai@mygreatlearning.com

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