Who is this course designed for?

<text variant="body1">OpenAI API practitioners seeking to advance their expertise into enterprise-level multimodal functionalities. <text variant="body1">Machine learning specialists and data scientists developing high-scale AI workflows that necessitate mastery of AWS Generative AI frameworks. <text variant="body1">AWS experts and cloud architects pivoting toward the engineering of sophisticated AI applications. <text variant="body1">Technical product leadership requiring a deep comprehension of computer vision capabilities and the strategic implications of various cloud-based GenAI infrastructures.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Vision AI and Advanced OpenAI

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Vision AI and Advanced OpenAI

Ce cours fait partie de Spécialisation "Generative AI Engineering with OpenAI API and AWS Models"

Instructeur : Mumshad Mannambeth

Inclus avec En savoir plus

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

5 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

5 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Build DALL-E and CLIP applications using text-to-image architecture to generate and evaluate visual AI outputs.
Use OpenAI and advanced tools—function calling, structured outputs, batching, and moderation—to build scalable enterprise AI solutions.
Explain key generative AI architecture concepts like tokens, embeddings, chunking, and context windows to guide application design.
Assess AWS generative AI infrastructure, lifecycle stages, performance trade-offs, and cost optimisation.

Compétences que vous acquerrez

Catégorie : Image Analysis
Catégorie : Large Language Modeling
Catégorie : Scalability
Catégorie : Token Optimization
Catégorie : Content Optimization
Catégorie : AI literacy
Catégorie : Multimodal Prompts
Catégorie : Performance Measurement
Catégorie : Responsible AI
Catégorie : Generative Model Architectures
Catégorie : Embeddings
Catégorie : LLM Application
Catégorie : Critical Thinking and Problem Solving

Outils que vous découvrirez

Catégorie : OpenAI API
Catégorie : Prompt Engineering
Catégorie : AI Workflows
Catégorie : Amazon Web Services
Catégorie : Model Deployment
Catégorie : OpenAI
Catégorie : Generative AI

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

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3 devoirs

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Ce cours fait partie de la Spécialisation "Generative AI Engineering with OpenAI API and AWS Models"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

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Il y a 3 modules dans ce cours

Once you can build with text, the next step is multimodal AI and enterprise-scale capabilities. This course advances your OpenAI skills into image generation, advanced production API features, and the foundational AWS architecture knowledge you need to design serious generative AI systems.

You'll start with OpenAI's vision capabilities: how DALL-E generates images from natural language prompts, the evolution from DALL-E 1 to DALL-E 3, and how CLIP connects visual and language understanding. You'll build a working image generator and an image captioning pipeline, and examine the ethical challenges and future trends shaping AI vision technologies. Next, you'll implement OpenAI's most powerful production features — function calling, structured outputs, batch processing, and content moderation — the capabilities that separate prototype applications from scalable, enterprise-grade systems. The course closes with a critical knowledge bridge into AWS: tokens, embeddings, chunking, context windows, the foundation model lifecycle, AWS GenAI infrastructure design, and cost optimisation principles — preparing you for AWS-native deployment. Designed for learners who have OpenAI API experience. Basic programming knowledge is recommended.

In this module, you'll explore OpenAI's cutting-edge vision capabilities, focusing on DALL-E and CLIP. You’ll learn how DALL-E’s text-to-image generation works, the evolution of DALL-E versions, and its practical applications across various industries. Hands-on projects will guide you through creating your own images and fine-tuning models, while discussions on ethical considerations and future trends will prepare you for challenges in AI-driven vision technologies.

Inclus

12 vidéos1 lecture1 devoir

12 vidéosTotal 62 minutes

Overview of OpenAi Vision8 minutes
DALL-E: What It Is and How It Works4 minutes
The Evolution of DALL-E: From DALL-E 1 to DALL-E 35 minutes
Understanding DALL-E Text-to-Image Generation6 minutes
Practical Applications of DALL-E in Various Industries7 minutes
Introduction to Contrastive Language-Image Pretraining (CLIP)4 minutes
Project 1: Image Generator6 minutes
Project 2: Image Captioning6 minutes
Fine-Tuning3 minutes
Practical Vision Applications5 minutes
Limitations, Challenges, and Ethical Considerations5 minutes
Future Trends and Innovations in OpenAi Vision3 minutes

1 lectureTotal 10 minutes

Hands-On Lab: Creating Your Own Images10 minutes

1 devoirTotal 30 minutes

Vision30 minutes

This module dives into the advanced features of OpenAI, including structured outputs, function calling, and batch processing. You’ll learn how to manage and scale AI solutions, applying advanced usage techniques to create robust applications. Through hands-on labs, you’ll practice batch processing and explore moderation techniques to ensure ethical use of AI.

Inclus

5 vidéos1 lecture1 devoir

This module introduces the core concepts of generative AI, including tokens, chunking, and embeddings. You’ll explore the lifecycle of foundation models, their capabilities, and limitations in real-world applications. Additionally, you’ll gain insights into the AWS infrastructure for building generative AI solutions and the cost considerations for optimizing performance, availability, and redundancy.

Inclus

13 vidéos2 lectures1 devoir

13 vidéosTotal 62 minutes

Basic Concepts of Generative AI - tokens, chunking, embeddings, and more5 minutes
Context Window and Tokens4 minutes
Embeddings, Vectors and Chunking4 minutes
LLMS and Prompt Engineering 5 minutes
Generative AI Use Cases and Applications4 minutes
Foundation Model Lifecycle2 minutes
Foundation Model Lifecycle and Use Case Development6 minutes
Capabilities and Limitations of Generative AI Applications6 minutes
Evaluating LLM Performance 5 minutes
AWS Infrastructure for Building Gen AI Applications4 minutes
Building Enterprise Generative AI on AWS: Tools, Models, and Security 6 minutes
Cost Consideration for AWS Gen AI Services - Redundancy, Availability, Performance, and More Part-15 minutes
Cost Consideration for AWS Gen AI Services - Redundancy, Availability, Performance, and More Part-26 minutes

2 lecturesTotal 20 minutes

Game: Match the lifecycle action to the lifecycle stage10 minutes
Game: Match the advantages and disadvantages of GenAI10 minutes

1 devoirTotal 30 minutes

Fundamentals of Generative AI30 minutes

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Mumshad Mannambeth

KodeKloud

49 Cours46 260 apprenants

Offert par

KodeKloud

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Foire Aux Questions

OpenAI API practitioners seeking to advance their expertise into enterprise-level multimodal functionalities.

Machine learning specialists and data scientists developing high-scale AI workflows that necessitate mastery of AWS Generative AI frameworks.

AWS experts and cloud architects pivoting toward the engineering of sophisticated AI applications.

Technical product leadership requiring a deep comprehension of computer vision capabilities and the strategic implications of various cloud-based GenAI infrastructures.

No prior AWS or cloud experience is required. The AWS content in this course is conceptual and architectural — designed to prepare you for hands-on AWS deployment in AWS GenAI: RAG, AI Agents & Responsible AI of the Generative AI Engineering with OpenAI API and AWS Models Specialization. Familiarity with the OpenAI API (from Generative AI: OpenAI API and Prompt Engineering or equivalent) is recommended.

You'll build two vision AI applications — an image generator and an image captioning pipeline using DALL-E and CLIP. You'll also complete a batch processing lab and implement function calling in a practical application, with skills validated through a graded assignment.

This course builds job-ready skills across three high-demand areas.

Vision AI: text-to-image generation, image captioning, DALL-E (v1–3), CLIP-based language-image understanding, multimodal prompting, and cross-industry applications.

Advanced OpenAI API engineering: function calling, structured outputs, batch processing, content moderation, and enterprise-grade usage patterns.

Generative AI architecture: tokens, embeddings, chunking, context management, foundation model lifecycle, model architectures, AWS GenAI infrastructure, LLM performance evaluation, and AI cost optimization.

Together, these skills prepare you to move from API consumer to AI systems designer.

Rather than treating image generation in isolation, this course integrates vision AI directly into production-grade engineering workflows. Its curriculum is founded on three strategic pillars: merging multimodal AI (DALL-E, CLIP) with sophisticated enterprise features such as function calling and structured outputs; creating a technical bridge from OpenAI development to AWS-native architecture through the expert application of tokens and embeddings; and prioritizing practical proficiency via graded, hands-on projects. This structured educational journey provides a clear transition for learners moving from intricate API patterns to robust AWS systems.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.