AI/ML Engineering Career Program
Build, train, evaluate and deploy machine-learning systems using practical engineering workflows.
What you'll learn
Understand the complete machine-learning lifecycle from problem definition to deployment.
Use Python and core data libraries for machine-learning development.
Prepare datasets and select appropriate features and models.
Train, evaluate and improve supervised and unsupervised learning models.
Understand neural networks and deep-learning fundamentals.
Build reproducible ML pipelines and understand deployment and monitoring.
Complete an end-to-end ML engineering project.
Career opportunities
This course includes
Live instructor-led classes
Python coding labs
Dataset exercises
Model-building assignments
Model evaluation
Deployment exercise
Capstone project
Certificate
Who it's for
- CS/IT/engineering students
- Python developers
- Software engineers moving into AI
- Data professionals
- Career switchers with programming foundations
Curriculum
12 modules · Live classes · Hands-on practice · Practical assignments
- AI vs ML vs deep learning
- ML engineering lifecycle
- Problem framing
- Training vs inference
Practical / Lab: Translate a business problem into an ML problem statement.
- Python fundamentals for ML
- NumPy and Pandas
- Data handling
- Functions and reusable code
Practical / Lab: Prepare and inspect a real-world dataset.
- Missing values
- Outliers
- Encoding
- Scaling
- Feature selection
Practical / Lab: Build a reproducible preprocessing pipeline.
- Regression
- Classification
- Decision trees
- Ensembles
- Model selection
Practical / Lab: Train and compare multiple models.
- Clustering
- Dimensionality reduction
- Anomaly detection
- Use cases and limitations
Practical / Lab: Segment a dataset and interpret clusters.
- Train/validation/test splits
- Cross-validation
- Precision, recall, F1
- ROC-AUC and business metrics
Practical / Lab: Evaluate competing models and justify the selected model.
- Neural-network structure
- Activation functions
- Loss and optimization
- Training concepts
Practical / Lab: Build a small neural-network model.
- Image and text representations
- Embeddings concept
- Classification use cases
- Choosing the right approach
Practical / Lab: Develop a small vision or text classification exercise.
- Project structure
- Experiment tracking
- Versioning
- Configuration and testing
Practical / Lab: Create a reproducible ML project structure.
- Model serving
- APIs
- Batch vs real-time inference
- Monitoring and drift
Practical / Lab: Expose a trained model through a simple inference workflow.
- Bias
- Data leakage
- Privacy
- Explainability
- Human oversight
Practical / Lab: Review an ML solution for technical and ethical risks.
- Problem definition
- Data pipeline
- Model training
- Evaluation
- Deployment
- Documentation
Practical / Lab: Build and present an end-to-end ML solution.
Requirements
- Basic Python is recommended; beginners can learn the required Python within the program.
- Basic mathematics and statistics are helpful.
- Laptop with a modern Python environment and internet connection.
- Access to a suitable notebook/cloud environment is recommended.
Program description
This program treats machine learning as an engineering discipline rather than a collection of algorithms. Learners move from problem definition and data preparation through modeling, evaluation, deployment and monitoring.
Google Cloud's current AI/ML training pathways similarly emphasize designing, building, productionizing, optimizing and maintaining ML systems, including MLOps and deployment. The course therefore keeps practical engineering and lifecycle thinking at its center.
Capstone / final project
Final project: define a practical business problem, prepare data, train and compare models, document evaluation, deploy a model endpoint or batch workflow and present the results.
Recommended tools & platforms
Batches
New batches forming · limited seats
Mode
Live instructor-led training
Duration
12 weeks
24/7 support