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AI with Cloud
Train at scale, serve globally, and manage ML pipelines with cloud-native architecture.
QuantQ Vision// ai-with-cloud
Full course details & curriculum
Train at scale, serve globally, and manage ML pipelines with cloud-native architecture.
Learning outcomes
- Operate MLOps pipelines on AWS/Azure/GCP
- Serve models globally with low latency
- Automate retraining and evaluation
Curriculum — module by module
AWS SageMaker
Train, tune, and deploy on AWS's managed ML platform.
Lessons
- SageMaker Studio & training jobs
- Hyperparameter tuning at scale
- Real-time & batch endpoints
- Model registry and lineage
You will be able to
- Ship a SageMaker endpoint with autoscaling
- Track lineage from data to model version
Azure AI
Build enterprise AI on Azure ML and Cognitive Services.
Lessons
- Azure ML pipelines
- Prompt flow & Azure OpenAI
- Managed online endpoints
- Responsible AI dashboards
You will be able to
- Deploy an Azure ML pipeline end to end
- Wire Azure OpenAI into a governed workflow
Vertex AI
Operate on Google Cloud's unified ML platform.
Lessons
- Vertex Pipelines & Feature Store
- Model Garden & tuned foundation models
- Online prediction & Model Monitoring
- Cost and quota governance
You will be able to
- Run a reproducible Vertex Pipeline
- Monitor drift on a served model
MLOps
Automate the full model lifecycle across clouds.
Lessons
- CI/CD for models & data
- Feature stores and offline/online parity
- Automated retraining triggers
- Evaluation, canary, and rollback
You will be able to
- Automate retraining on data drift
- Ship canary + rollback for a served model