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Course Outline
1. Introduction to AI Engineering
- Defining AI Engineering.
- Distinguishing between AI, Machine Learning, and Deep Learning.
- The AI engineering lifecycle.
- AI applications across various industries.
- Roles and responsibilities of an AI engineer.
2. Foundations of Artificial Intelligence
- Core AI concepts and terminology.
- Supervised, unsupervised, and reinforcement learning approaches.
- 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.
- Utilizing NumPy, Pandas, and Matplotlib.
- Data manipulation and visualization techniques.
- Working effectively with Jupyter Notebooks.
- Writing reusable code for AI applications.
4. Data Preparation for AI
- Collecting and understanding datasets.
- Data cleaning and preprocessing strategies.
- Feature engineering techniques.
- Feature scaling and normalization.
- Splitting datasets into training, validation, and test sets.
- Handling missing values and outliers.
5. Machine Learning Fundamentals
- Regression algorithms.
- Classification algorithms.
- Clustering techniques.
- The model training workflow.
- Model evaluation metrics.
- Strategies to prevent overfitting and underfitting.
6. Building AI Models with TensorFlow and PyTorch
- Introduction to TensorFlow.
- Introduction to PyTorch.
- Creating neural networks.
- Model training and validation processes.
- Saving and loading models.
- Comparing both frameworks.
7. Natural Language Processing Fundamentals
- Text preprocessing methods.
- Word embeddings.
- Text classification techniques.
- Sentiment analysis.
- Introduction to transformer models.
- Practical NLP applications.
8. AI in Software Development
- Integrating AI into existing applications.
- Calling AI services via APIs.
- Developing AI-powered applications.
- AI-assisted software development tools.
- Testing AI-enabled applications.
9. AI Engineering Best Practices
- Project organization strategies.
- Version control with Git.
- Experiment tracking methods.
- Model versioning practices.
- Documentation standards.
- Ensuring reproducibility in AI projects.
10. Deploying AI Models
- Model serialization techniques.
- Building inference services.
- Implementing REST APIs for AI models.
- Introduction to Docker for AI deployment.
- Monitoring deployed models.
- Model maintenance and updates.
11. AI Data Engineering
- Data pipelines architecture.
- ETL processes.
- Managing structured and unstructured data.
- Data storage options.
- Data quality management.
- Preparing production-ready datasets.
12. Responsible and Ethical AI
- Addressing AI bias and fairness.
- Explainable AI (XAI).
- Privacy and data protection.
- AI security considerations.
- Responsible AI development principles.
- Regulatory and governance considerations.
13. AI Project Management
- The AI project lifecycle.
- Agile methodologies for AI projects.
- Fostering 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 opportunities.
- Summary, Q&A, and next steps.
Requirements
- A grasp of basic programming concepts.
- Experience with Python programming.
- Familiarity with fundamental statistics and linear algebra.
Target Audience
- AI engineers.
- Software developers.
- Data analysts.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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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.
