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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering.
- Differences between AI, Machine Learning, and Deep Learning.
- The AI engineering lifecycle.
- AI applications across various industries.
- Roles and duties of an AI engineer.
2. Foundations of Artificial Intelligence
- Key AI concepts and terminology.
- Supervised, unsupervised, and reinforcement learning.
- Fundamentals of neural networks and deep learning.
- Overview of generative AI and foundation models.
- AI development ecosystems and frameworks.
3. Python for AI Engineering
- Essential Python libraries for AI.
- NumPy, Pandas, and Matplotlib.
- Data manipulation and visualization.
- Utilizing Jupyter Notebooks.
- Writing reusable code for AI.
4. Data Preparation for AI
- Gathering and comprehending datasets.
- Data cleaning and preprocessing.
- Feature engineering.
- Feature scaling and normalization.
- Dividing datasets into training, validation, and test sets.
- Addressing missing values and outliers.
5. Machine Learning Fundamentals
- Regression algorithms.
- Classification algorithms.
- Clustering techniques.
- Model training workflow.
- Model evaluation metrics.
- Preventing overfitting and underfitting.
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow.
- Introduction to PyTorch.
- Creating neural networks.
- Model training and validation.
- Saving and loading models.
- Comparing both frameworks.
7. Natural Language Processing Fundamentals
- Text preprocessing.
- Word embeddings.
- Text classification.
- Sentiment analysis.
- Introduction to transformer models.
- Practical NLP applications.
8. AI in Software Development
- Integrating AI into existing applications.
- Accessing AI services via APIs.
- Developing AI-powered applications.
- AI-assisted software development tools.
- Testing AI-enabled applications.
9. AI Engineering Best Practices
- Project organization.
- Version control with Git.
- Experiment tracking.
- Model versioning.
- Documentation standards.
- Reproducibility in AI projects.
10. Deploying AI Models
- Model serialization.
- Building inference services.
- REST APIs for AI models.
- Introduction to Docker for AI deployment.
- Monitoring deployed models.
- Model maintenance and updates.
11. AI Data Engineering
- Data pipelines.
- ETL processes.
- Managing structured and unstructured data.
- Data storage options.
- Data quality management.
- Preparing production-ready datasets.
12. Responsible and Ethical AI
- AI bias and fairness.
- Explainable AI (XAI).
- Privacy and data protection.
- AI security considerations.
- Responsible AI development.
- Regulatory and governance considerations.
13. AI Project Management
- AI project lifecycle.
- Agile methodologies for AI projects.
- Collaboration between technical and business teams.
- Estimating AI projects.
- Managing risks.
- Measuring project success.
14. Hands-on AI Engineering Workshop and Future Trends
- Setting up a complete AI development workflow.
- Building an end-to-end machine learning project.
- Training and evaluating a model using TensorFlow or PyTorch.
- Deploying a simple AI application.
- Current trends in AI Engineering.
- Generative AI and Large Language Models (LLMs).
- MLOps and AI automation.
- Career paths and continuous learning.
- Summary, Q&A, and next steps.
Requirements
- Familiarity with fundamental programming concepts.
- Proficiency in Python programming.
- Knowledge of basic statistics and linear algebra.
Audience
- AI engineers.
- Software developers.
- Data analysts.
14 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
