Course Outline
Introduction to AI in the Software Development Lifecycle
- Distinguishing between Generative AI and Predictive AI.
- Exploring AI applications in coding, data analytics, and automation.
- A comprehensive overview of LLMs, transformer networks, and deep learning architectures.
AI-Assisted Coding and Predictive Development Techniques
- Leveraging AI for code completion and generation using tools like GitHub Copilot and CodeGeeX.
- Proactively identifying potential code bugs and security vulnerabilities prior to deployment.
- Automating code review processes and generating optimization recommendations.
Developing Predictive Models for Software Solutions
- Mastering time-series forecasting and predictive analytics methodologies.
- Building AI models for demand forecasting and anomaly detection.
- Utilizing Python, Scikit-learn, and TensorFlow for robust predictive modeling.
Generative AI for Text, Code, and Visual Media Creation
- Working effectively with GPT, LLaMA, and other prominent LLMs.
- Generating synthetic datasets, automated text summaries, and technical documentation.
- Creating AI-generated images and videos utilizing diffusion models.
Deploying AI Models in Production Environments
- Hosting and managing AI models via Hugging Face, AWS, and Google Cloud.
- Architecting API-based AI services to support business operations.
- Fine-tuning pre-trained models to address domain-specific requirements.
AI for Strategic Business Insights and Data-Driven Decisions
- Enhancing business intelligence and customer analytics through AI.
- Forecasting market trends and analyzing consumer behavior patterns.
- Streamlining and optimizing workflows through AI automation.
Ethical AI Frameworks and Development Best Practices
- Addressing ethical considerations in AI-assisted decision-making processes.
- Detecting bias and ensuring fairness within AI models.
- Adopting best practices for interpretable and responsible AI development.
Practical Workshops and Industry Case Studies
- Applying predictive analytics to a real-world dataset scenario.
- Building an AI-powered chatbot featuring advanced text generation.
- Deploying an LLM-based application designed for automation tasks.
Conclusion and Path Forward
- Consolidating key learning outcomes.
- Curating AI tools and resources for continued professional development.
- Final Q&A session for open discussion.
Requirements
- A solid grasp of fundamental software development principles.
- Proficiency in at least one programming language, with a strong recommendation for Python.
- A basic understanding of machine learning or AI concepts is advantageous, though not strictly required.
Target Audience
- Software developers.
- AI and ML engineers.
- Technical team leads and architects.
- Product managers with an interest in integrating AI capabilities into their products.
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
