The Improve AI Tracker/Trainer is a stack of serverless components that trains updated contextual multi-armed bandit models for scoring, ranking, and decisions. The stack runs on AWS to cheaply and easily track JSON items and their rewards from Improve AI libraries. These rewards are joined with the tracked items that they're associated with and used as input to training new scoring and ranking models.

Make a private fork of this repo. This way your model configuration is stored in revision control.

$ npm install -g serverless

$ npm install

$ nano config/config.yml

Deploy a new dev stage in us-east-1

$ serverless deploy --stage dev

Deploying improveai-acme-demo to stage dev (us-east-1)

✔ Service deployed to stack improveai-acme-demo-dev (111s)

endpoint: https://xxxx.lambda-url.us-east-1.on.aws/

The output of the deployment will list the track endpoint URL like https://xxxx.lambda-url.us-east-1.on.aws. The track endpoint URL may be used directly by the client SDKs to track decisions and rewards. Alternately, a CDN may be configured in front of the track endpoint URL for greater administrative control.

The deployment will also create a models S3 bucket in the form of improveai-{organization}-{project}-{stage}-models. After each round of training, updated models are automatically uploaded to the models bucket.

The models bucket is private by default. Make the '/models/latest/' directory public to serve models directly from S3. Alternatively, a CDN may be configured in front of the models S3 bucket.

Model URLs follow the template of https://{modelsBucket}.s3.amazonaws.com/models/latest/{modelName}.{mlmodel|xgb}.gz. The Android and Python SDKs use .xgb.gz models and the iOS SDK uses .mlmodel.gz models.

Improve AI libraries are currently available for Swift, Java, and Python.

The reinforcement learning algorithm is a contextual multi-armed bandit with XGBoost acting as the core regression algorithm. As such, it is ideal for making decisions on structured data, such as JSON or native objects in Swift/Objective-C, Java/Kotlin, and Python. Unlike deep reinforcement learning algorithms, which often require simulator environments and hundreds of millions of decisions, this algorithm performs well with the more modest amounts of data found in real world applications. Compared to A/B testing it requires exponentially less data for good results.