Data Scientist - ML Platform Engineering & Automation
ML Platform Engineering & Automation for Data Scientist: A comprehensive guide to mastering ML Platform Engineering & Automation as a Data Scientist. Learn recommended tools, practical applications, and resources to develop this critical AI skill.
Modern AI/ML foundations including LLM applications, advanced frameworks, and model optimization techniques.
Cutting-edge AI techniques including multimodal AI, reinforcement learning, and federated learning systems.
Building scalable AI infrastructure, MLOps pipelines, and production-ready AI systems with monitoring.
AI-enhanced data engineering including vector databases, real-time pipelines, and intelligent data quality systems.
Research implementation, AI safety, and contributing to the advancement of AI technology and ethics.
ML Platform Engineering & Automation
Building end-to-end ML platforms with automated pipelines, feature stores, model monitoring, and automated retraining workflows.
- Build end-to-end ML platforms with automated pipelines
- Implement feature stores for consistent data serving
- Design model monitoring and drift detection systems
- Create automated retraining and deployment workflows
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