Power point

1. Introduction to Machine
Learning Engineering
Principles, Applications & Career
Pathways

2. What is Machine Learning
Engineering?
• A discipline that combines software
engineering with machine learning.
• Focuses on deploying ML models into
production systems.
• Bridges the gap between data science and
software development.

3. Core Concepts
• Supervised, Unsupervised, and Reinforcement
Learning.
• Model training, evaluation, and deployment.
• Data preprocessing and feature engineering.

4. Tools & Technologies
• Languages: Python, R, Java.
• Frameworks: TensorFlow, PyTorch, Scikit-learn.
• Tools: Jupyter, MLflow, Docker, Kubernetes.

5. ML Pipeline
• Data Collection & Cleaning.
• Model Selection and Training.
• Validation and Testing.
• Deployment and Monitoring.

6. Model Deployment
• APIs using Flask or FastAPI.
• Cloud deployment: AWS, GCP, Azure.
• Model versioning and reproducibility.

7. Real-World Applications
• Recommendation Systems (e.g., Netflix,
Amazon).
• Fraud Detection (e.g., Banking).
• Autonomous Vehicles.
• Healthcare Diagnostics.

8. Challenges in ML Engineering
• Data Quality and Bias.
• Model Drift and Maintenance.
• Scalability and Latency.
• Security and Privacy.

9. ML Engineer vs Data Scientist
• ML Engineers: Focus on scalability,
deployment, and systems.
• Data Scientists: Focus on data analysis and
insights.
• Both roles are complementary.

10. Careers in ML Engineering
• ML Engineer.
• MLOps Engineer.
• AI Research Engineer.
• Product ML Engineer.

11. Conclusion
• ML Engineering integrates ML with robust
engineering practices.
• Vital for delivering AI-powered products at
scale.
• Opportunities across industries are growing
rapidly.