Short Answer: Yes, but only selectively.
As an AI Platform Engineer, the focus is on building, deploying, and optimizing AI/ML models at scale, not on developing new ML algorithms or performing deep data science research. However, to work effectively with Data Scientists and MLOps workflows, an AI Platform Engineer should understand key Data Science essentials related to:
✅ Understanding ML model workflows (How data moves through AI/ML pipelines)
✅ Feature Engineering & Feature Stores (How data is prepped for models)
✅ Fine-tuning & Inference Optimization (How models are trained and served efficiently)
✅ Evaluating Model Performance (Ensuring models meet production-quality standards)
| Category | Why It's Important? | What to Learn? |
|---|---|---|
| ML Fundamentals | Understanding AI/ML workflows | Supervised vs Unsupervised Learning, ML lifecycle |
| Data Processing & Feature Engineering | Optimizing inputs for models | Pandas, NumPy, Feature Stores (Feast, Redis) |
| Model Training & Evaluation | Ensuring models work as expected | Loss functions, bias/variance, hyperparameter tuning |
| Fine-Tuning & Inference | Deploying efficient AI models | Batch Inference, Online Inference, Quantization |
| ML Model Monitoring | Keeping AI models fresh & accurate | Drift Detection, Post-Deployment Monitoring (NannyML, Weights & Biases) |
| Weeks | Topic | Essential Data Science Knowledge Included |
|---|---|---|
| 5-6 | AI/ML Foundations | ML lifecycle, Training vs Inference |
| 7-8 | Feature Engineering & Data Processing | Feature Stores (Feast), Pandas, NumPy |
| 9-10 | Model Deployment | Understanding ML model evaluation |
| 17-18 | Fine-tuning & Optimization | Hyperparameter tuning, Batch vs Online inference |
| 15-16 | ML Model Monitoring | Drift detection, bias tracking |
❌ Advanced ML Math (Linear Algebra, Probability Theory)
❌ Building ML Algorithms from Scratch
❌ Deep Data Science Research (Exploratory Data Analysis, Feature Selection Methods)
🔹 AI Platform Engineers don’t need deep data science skills but should understand ML workflows, inference, tuning, and monitoring.
🔹 Focus on AI/ML infrastructure, optimization, scalability, and deployment best practices rather than model development.
🔹 Integrate knowledge of Feature Stores, Model Serving, Inference Optimization, and Monitoring rather than full-scale data science.
Would you like me to refine the curriculum flow to emphasize Data Science essentials in the right places? 🚀