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)
Building a Dog Breed Identifier App from scratch - DogNet
→ Build a machine learning pipeline→ Deploy a model to an app
Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus
→ Deploy ML models on Kubernetes→ Configure auto-scaling with HPA
MLOps Tutorial: Build a Full ML Pipeline with MLflow, DVC & Deploy on AWS
→ Build a full ML pipeline with MLflow and DVC→ Deploy a production-ready ML pipeline on AWS
Complete Dockers For Data Science Tutorial In One Shot
→ Implement data science projects using Docker→ Deploy machine learning models using containers
Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation
→ Design a multimodal learning pipeline→ Train a Vision Transformer model
[Live Machine Learning Research] Plain Self-Ensembles (I actually DISCOVER SOMETHING) - Part 1
→ Implement ensemble models for improved accuracy→ Apply self-distillation techniques for label-free learning
Real-Time Event Processing for AI/ML with Numaflow // Sri Harsha Yayi // DE4AI
→ Build real-time event processing pipelines with Numaflow→ Integrate with messaging systems like Kafka
LIVE CODING: Stocks & Sentiment Analysis
→ Build a sentiment analysis model with Hugging Face transformers→ Pull stock prices with yfinance
Live- Implementation Of 7 HealthCare End To End Projects With Deployment
→ Implement end-to-end ML projects→ Deploy healthcare AI models
Easily get started with machine learning using Amazon SageMaker JumpStart - AWS Virtual Workshop
→ Deploy machine learning models with SageMaker→ Fine-tune open source models
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