ML Pipelines
Build end-to-end ML pipelines — feature engineering, cross-validation, and deployment.
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After this skill you can…
- Engineer features and handle missing data
- Cross-validate models without leakage
- Export and serve a model as an API
Prerequisites
Watch (10 videos)
Become an AI Engineer in 2026 | Microsoft AI Engineer Program | #Shorts| #Simplilearn
→ Build machine learning pipelines→ Deploy machine learning models→ Work with cloud-based AI tools
Best Machine Learning Courses Online | Top Online Machine Learning Courses | #Shorts | #Simplilearn
→ Build machine learning models→ Deploy machine learning models→ Use cloud-based machine learning platforms
AI Engineer Roadmap 2026 | Become an AI Engineer from Scratch | #Shorts | #Simplilearn
→ Build machine learning models→ Deploy AI applications
Model Size and Hyperparameters Explained
→ Optimize model size→ Understand hyperparameter effects→ Deploy models efficiently
AI/ML System Design Session 2 | Complete Guide to Machine Learning System Design
→ Build data pipelines→ Implement batch and stream processing→ Use feature registries
Better Data Categorization
→ Categorize data using knowledge graphs→ Apply vectorization to input data
Measure Voice Agent Latency in Python with LiveKit: Trace STT, LLM, and TTS Delays
→ Compute p50/p95 latency budgets→ Analyze stage timings→ Optimize system performance
Build and deploy AI at the edge for real-world impact | OD837
→ Design cloud-consistent infrastructure→ Implement edge AI solutions
When to choose CPU vs GPU: Databricks AI Runtime Explained
→ Build AI workflows→ Choose appropriate compute resources→ Train models with Databricks AI Runtime
how to run ablation in pretraining?
→ Design ablation experiments→ Analyze feedback loops from results
DeepCamp AI