Slalom First Look \\\ AWS SageMaker

The Story

If you are in technology and have not heard about Artificial Intelligence (AI) and Machine Learning (ML), you better start googling quickly. Because it’s here and the race is on. In recent years, large amounts of human and compute capital make AI and ML a big investment. Cloud platforms are racing to turn IAAS custom solutions to PAAS configurable solutions. Enter SageMaker. AWS’ service that lowers the barrier to entry for AI and Machine Learning.

Why do I care?

Computers can process data (duh) and make inferences (go on) based on data analysis of Deep Learning (DL) Models (you lost me). Think Amazon Alexa. You can gain insights, cut costs, or make more informed decisions.

How would I implement ML today

Today companies need to invest in people (Data scientists, Network Engineers, Data Engineers) to create, tune, and deploy models. These skills are not inexpensive. Companies also must build and pay for the compute (server) infrastructure that support the model, also not cheap.

Give me the value prop?

With SageMaker, you no longer have to build everything from scratch, you configure models. You no longer need experts in every discipline. You now only need people to build and tune a model. Deployment can be done by technologists on staff today.

How should I use it?

Since you remove the infrastructure setup, it costs less to get started. You can prototype to test hypothesis with much lower cost or deploy your full-fledged production models with fewer resources to maintain.

Show me the insights!

SageMaker reduces the overhead that comes with ML today. Focus your time and money on the items that provide value, not the maintenance of infrastructure. Consider it for both prototyping and operationalizing ML.