Build the data pipelines, analytics and AI solutions that modern organisations depend on.
4 courses Amazon Web Services (AWS)Data and AI are among the fastest-moving areas in technology, and both depend on well-built cloud data platforms. This program covers the full journey: ingesting and transforming data, storing and governing it, then using it for analytics, machine learning and generative AI on AWS.
You learn the engineering behind the models as well as the models themselves, which is where many employers struggle to find talent.
Who it's for: Analysts, database and BI professionals, developers and engineers who want to move into data engineering, machine learning or AI roles.
Data lakes and warehouses are converging into unified platforms that serve analytics and AI from one source.
More decisions depend on data that is seconds old rather than days old.
Organisations are moving from pilots to governed, production applications, often connected to their own data.
Repeatable, monitored model pipelines and clear data ownership are now expected.
Trustworthy AI depends on trustworthy data, driving demand for engineers who own quality and lineage.
Data ingestion with Kinesis, DMS and MSK, ETL with Glue and EMR, analytics and visualization.
Data ingestion and streaming, data store management and optimization, data operations and security.
Generative AI concepts, AWS AI services, prompt engineering, model customization and responsible AI.
Data preparation, model training with SageMaker, deployment, MLOps and AWS AI/ML services.
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