Sometimes, throwing more processing power or finding more data or applying another trick of the AI practitioner’s book can improve the accuracy of a model.
However, always keep in mind that you may be overfitting, or even that the data can not be linked to the expected results in 100% of the cases
Little things are needed for people to get offset by a product or idea. They got in a fight with their partner, they are very busy at work, or they just have something else on their mind. This will influence their buying decision greatly but will not necessarily transpire from the data in your data set as you probably don’t have this information in your data set.
So keep in mind that a model’s accuracy is capped by the variables you have in your data set, and that sometimes you may need to be content with a 85% accuracy…
The objective: automatically deploy Spacy on EC2 while creating a VPC, some SubNets, LoadBalancers and so on.
Why would you do this if you can do it using server less technology? One word: performance.
Here’s a diagram representing the architecture we’ll instantiate:
And using LaunchConfigurations and CloudFormation we build this in a matter of minutes.
The LaunchConfiguration first performs software updates using yum. It then downloads a jar file containing a Jetty server class that gets launched and listens to http requests. They then get proxied by the API Gateway.
You have to make your own proxying Java class, but other than feel free to get inspiration from the rest of the template, here: