, 13 tweets, 4 min read Read on Twitter
Next talking about machine learning on serverless #ServerlessDaysBOS
ML is the practice of examining large amounts of data and finding patterns #ServerlessDaysBOS
ML starts with model training. More data = better model. Data is never in the right format so you need to format. Then you find the best statistical algorithm and get a model #ServerlessDaysBOS
One you have a trained model you deploy the model. Often training is the purvue of a data scientist and the application developer worries about deployment #ServerlessDaysBOS
With serverless you don't need the application developer in order to deploy the model #ServerlessDaysBOS
Using serverless patterns to deploy models is what serverless machine learning is all about #ServerlessDaysBOS
PaaS solution to deploy a model: using a container to wrap the scoring script and dependencies and deploy to manged service #ServerlessDaysBOS
Serverless solution to model deploy just put model and scoring script with proper configurations on the FaaS #ServerlessDaysBOS
Learnings for azure for ml: python is preferred, need it supported in azure functions; data scientists don't want containers; functions are great for event driven classification #ServerlessDaysBOS
For serverless inference and retraining - skeptical on if it works. Serverless is not great for compute intensive jobs but good for short lived low compute scenarios. #ServerlessDaysBOS
FaaS good for data processing. Don't need to worry about sdks and can use triggers and bindings #ServerlessDaysBOS
Serverless OCR? Yes! Tools beyond FaaS help here i.e. eventing infrastructure, workflow orchestration #ServerlessDaysBOS
Challenges of serverless ML: limits on time and memory; often need GPU or other special hardware; cold start times; sensitive data require more advanced security; cluster is better if incoming load is high #ServerlessDaysBOS
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