AWS SageMaker, Google Vertex AI, Azure Machine Learning

Deploying models on cloud servers, Serverless inference options

Using S3 and Block storage, Managing large datasets in cloud

Automating cloud infrastructure, Managing ML environments as code

Infrastructure deployment pipelines, CI/CD for AI infrastructure

Real-time monitoring of AI pipelines, Custom metrics for ML models

Retraining triggers, Statistical drift detection methods

Real-time anomaly monitoring, Alerting systems for AI services

Federated learning architectures, Decentralized model training

Differential privacy, Homomorphic encryption, Secure multi-party computation

Model optimization for edge devices, Hardware considerations

Developing for IoT AI applications, On-device learning techniques