Highlights
Advanced AI Development Training:
- Review of AI Development Basics
- Explore Advanced Topics in AI Development
- Learn Advanced AUTO-GPT Applications
- Learn Advanced Prompt Engineering
- Implement Vector Databases in AI Development
- AI Ethics and Fairness
- AI System Design and Architecture
- Deploying and Maintaining AI Applications
Course Details
Unit 1: Review of AI Development Basics
- 1.1 Recap of fundamental AI, Machine Learning and Deep Learning concepts
- 1.2 Review of GPT and AUTO-GPT models
- 1.3 Overview of AI development tools and libraries
Unit 2: Advanced Topics in AI Development
- 2.1 Hyperparameter tuning in Machine Learning and Deep Learning
- 2.2 Advanced techniques in feature engineering and data augmentation
- 2.3 Deep dive into transformer models and advanced architectures
- 2.4 Understanding Capsule Networks and Graph Neural Networks
Unit 3: Advanced AUTO-GPT Applications
- 3.1 Building complex applications with AUTO-GPT
- 3.2 AUTO-GPT in multi-turn conversations and dialog systems
- 3.3 AUTO-GPT in advanced NLP tasks: summarization, translation, sentiment analysis
- 3.4 Troubleshooting and optimizing AUTO-GPT applications
Unit 4: Advanced Prompt Engineering
- 4.1 Advanced techniques for effective prompt design
- 4.2 Prompt engineering for advanced tasks and complex domains
- 4.3 Handling biases and ethical issues in advanced prompt engineering
Unit 5: Vector Databases in AI Development
- 5.1 Deep dive into vector databases
- 5.2 Use-cases and applications of vector databases in AI
- 5.3 Best practices when using vector databases
Unit 6: AI Ethics and Fairness
- 6.1 Advanced topics in AI Ethics: interpretability, explainability, accountability
- 6.2 Understanding and mitigating bias in advanced AI models
- 6.3 The role of regulation in AI
Unit 7: AI System Design and Architecture
- 7.1 Designing robust and scalable AI systems
- 7.2 Advanced topics in AI system architecture
- 7.3 Designing AI systems for security and privacy
Unit 8: Deploying and Maintaining AI Applications
- 8.1 Advanced topics in AI application deployment
- 8.2 Monitoring and maintaining AI systems in production
- 8.3 Ensuring the reliability and robustness of AI applications
Who should attend
This course is ideal for those who already have an established knowledge base in AI/ML and some hands-on experience building, training and deploying models.
Learners should be comfortable with coding in Python and using libraries like TensorFlow, PyTorch, HuggingFace etc.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022
