Generative & Predictive AI for Developers

• An understanding of basic software development concepts
• Experience with any programming language (Python recommended)
• Familiarity with machine learning or AI fundamentals (recommended but not required)

Audience

• Software developers
• AI/ML engineers
• Technical team leads
• Product managers interested in AI-powered applications

Generative and predictive AI are transforming software development, enabling automated content creation, intelligent predictions, and enhanced problem-solving. This course focuses on practical implementation of predictive modeling and generative AI techniques in real-world applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

By the end of this training, participants will be able to:

• Understand the fundamentals of predictive AI and generative models.
• Utilize AI-powered tools for predictive coding, forecasting, and automation.
• Implement LLMs (Large Language Models) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendations.
• Develop and fine-tune AI models for real-world applications.
• Evaluate ethical considerations and best practices in AI deployment.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI for Software Development

• What is Generative AI vs Predictive AI
• Applications of AI in coding, analytics, and automation
• Overview of LLMs, transformers, and deep learning models

AI-Assisted Coding and Predictive Development

• AI-powered code completion and generation (GitHub Copilot, CodeGeeX)
• Predicting code bugs and vulnerabilities before deployment
• Automating code reviews and optimization suggestions

Building Predictive Models for Software Applications

• Understanding time-series forecasting and predictive analytics
• Implementing AI models for demand forecasting and anomaly detection
• Using Python, Scikit-learn, and TensorFlow for predictive modeling

Generative AI for Text, Code, and Image Generation

• Working with GPT, LLaMA, and other LLMs
• Generating synthetic data, text summaries, and documentation
• Creating AI-generated images and videos with diffusion models

Deploying AI Models in Real-World Applications

• Hosting AI models using Hugging Face, AWS, and Google Cloud
• Building API-based AI services for business applications
• Fine-tuning pre-trained AI models for domain-specific tasks

AI for Predictive Business Insights and Decision-Making

• AI-driven business intelligence and customer analytics
• Predicting market trends and consumer behavior
• Automating workflow optimizations with AI

Ethical AI and Best Practices in Development

• Ethical considerations in AI-assisted decision-making
• Bias detection and fairness in AI models
• Best practices for interpretable and responsible AI

Hands-On Workshops and Case Studies

• Implementing predictive analytics for a real-world dataset
• Building an AI-powered chatbot with text generation
• Deploying an LLM-based application for automation

Summary and Next Steps

• Review of key takeaways
• AI tools and resources for further learning
• Final Q&A session