Building a board: https://ryancaldbeck.co/2021/12/17/building-a-board-what-i-wish-i-had-known/.
Finally got to watch my agenda from AWS reinvent:
MLOps at Amazon: How to productionize ML workloads at scale.
Integrate ML into DevOps and DataEng pipelines. Training, CI/CD, performance, analytics, everything. On the final deliverables, on algorithms, and meta on the pipelines themselves.
Used for suggestions (of course), search matching, extended “customers also bought”, ETA prediction, optimal packaging/box size/shape, drone delivery pathing.
Sagemaker.
Implementing MLOps practices with Amazon SageMaker, featuring Vanguard.
Remember athena is the query service to run sql against s3.
How Amazon.com transforms customer experiences through AI/ML.
Again, lots of forecasting to map supply and demand.
A bit of computer vision for placing/picking in warehouses.
Data lakes: Easily build, secure, and share data with AWS Lake Formation.
Governed tables support: ACID transactions, storage optimization, and version history.
Supports col, row, and cell based permissions. Also custom tags (TBAC).
Data mesh between producers and consumers, internals and customers.
How the NFL Raiders became analytics champions in Las Vegas (sponsored by Matillion).
NOT for sports stats unfortunately…was for ticket sales and business analytics. Bit of clickbait.
Built on snowflake+tableau.
Building next-gen applications with event-driven architectures.
Direct synchronous, REST with sender/receiver. Better: stick a queue in between. A full bus can sit on top of the queue for routing and more. This is Amazon EventBridge.
Each event is just a json. You can write rules against anything. Events represent a system change. They’re immutable and have a timestamp.
Taco Bell’s example with AWS Step Function. Event-driven workflows.
Drawing the New York City skyline with Amazon Aurora Serverless v2.
Aurora is compatible with both postgres and mysql, but faster and cloud.
Remember (serverless != functions). Aurora is a serverless DB. Helpful for dev/test envs. Very useful for systems with intermittent or unpredictable load.
Because serverless, can scale very cheaply. v1 would double when capacity was reached. V2 now increase incrementally, with optimization.
Scaling is subsecond!
The nyc skyline part is just a timeseries of cpu load, querying aurora heavily at specific times to create a full step function that looks like a skyline.
Build a high-throughput asset-tracking architecture.
