9 docs tagged with "cloud"

S3, Glue, Athena, EMR, and the AWS data engineering ecosystem.

Master the complete AWS SageMaker ecosystem for end-to-end ML workflows - training jobs, pipelines, model registry, feature store, and production inference at scale.

What query optimisation, storage tiering, and cloud cost controls do for AI systems, when large-scale model training and feature computation drive unpredictable cloud spend, and how to implement cost reduction strategies in production AI data pipelines.

Databricks Lakehouse, Unity Catalog, MLflow integration, and AutoML.

BigQuery architecture, ML built-in functions, and BigQuery ML.

Master AWS SageMaker, Google Vertex AI, Azure ML, Databricks, and cloud cost optimization strategies for production ML systems.

What multi-cloud data architectures do for AI systems, when vendor lock-in and data gravity risks threaten the portability of ML training and serving infrastructure, and how to design resilient multi-cloud strategies for production AI data pipelines.

Overview of cloud data platforms for AI and ML workloads.

Snowflake architecture, Snowpark, and ML feature serving from Snowflake.