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https://github.com/mikesparr/ai-demo-predict

Simple Golang API for AI startup demo that views data from Redis cache and publishes to Google Pub/Sub
https://github.com/mikesparr/ai-demo-predict

Last synced: about 6 hours ago
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Simple Golang API for AI startup demo that views data from Redis cache and publishes to Google Pub/Sub

Host: GitHub
URL: https://github.com/mikesparr/ai-demo-predict
Owner: mikesparr
License: mit
Created: 2020-12-21T03:50:23.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2021-01-18T16:54:27.000Z (almost 4 years ago)
Last Synced: 2024-12-21T19:02:28.387Z (20 days ago)
Language: Go
Size: 5.03 MB
Stars: 1
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=mikesparr_ai-demo-predict&metric=alert_status)](https://sonarcloud.io/dashboard?id=mikesparr_ai-demo-predict)

This API views batch predictions submitted by [ingest API](https://github.com/mikesparr/ai-demo-ingest) and allows the user to submit feedback, or corrections, to predictions to trigger model retraining jobs. This is 

created for demo purposes using [go-chi](https://github.com/go-chi/chi) HTTP framework for Golang.

# Demo (prediction, feedback, and retrain job)

![API Demo](./img_demo.gif)

# Architecture

![AI demo architecture](./img_arch.png)

# Components

- [Config](https://#) (pending)

- [Web App](https://github.com/mikesparr/ai-demo-web)

- [Ingest API](https://github.com/mikesparr/ai-demo-ingest)

- [Predict API](https://github.com/mikesparr/ai-demo-predict) (this repo)

- [Processors](https://github.com/mikesparr/ai-demo-functions)

# Prerequisites

You must be familiar with Google Cloud Platform and have the [Google Cloud SDK](https://cloud.google.com/sdk/docs/install) (`gcloud` CLI) installed. 

If you want to recreate the **AI Demo** then you will want an active project with billing enabled.

* NOTE: when you are done remember to **DELETE THE PROJECT** to avoid unneccessary billing.

# Install

The demo publishes a Docker image to container registry and deploys the app to Cloud Run. 

These are the steps to recreate this in your own environment.

```bash

export PROJECT_ID=$(gcloud config get-value project)

export TOPIC_ID="feedback"

export GCP_REGION="us-central1"

export VPC_CONN_NAME="serverless-connector" # needed to bridge networks

# create pubsub topics

gcloud pubsub topics create $TOPIC_ID

# create pubsub subscriptions

gcloud pubsub subscriptions create feedback-sub --topic $TOPIC_ID

# enable services

gcloud services enable compute.googleapis.com \

    run.googleapis.com \

    redis.googleapis.com \

    cloudbuild.googleapis.com

# fetch cache (memorystore: redis w/ vpc connector) info

# assume cache and vpc connector already created with backend processors

export REDIS_HOST=$(gcloud redis instances describe ml-cache1 --region us-central1 --format="value(host)")

export REDIS_PORT=$(gcloud redis instances describe ml-cache1 --region us-central1 --format="value(port)")

export AUTH_NETWORK=$(gcloud redis instances describe ml-cache1 --region us-central1 --format="value(authorizedNetwork)")

echo "Redis instance at ${REDIS_HOST}:${REDIS_PORT} on network ${AUTH_NETWORK}"

# clone repo and change to directory

git clone [email protected]:mikesparr/ai-demo-predict.git

cd ai-demo-predict

# build the api image

gcloud builds submit --tag gcr.io/${PROJECT_ID}/ai-demo-predict

# deploy the api to cloud run

gcloud run deploy ai-demo-predict \

    --image gcr.io/${PROJECT_ID}/ai-demo-predict \

    --region $GCP_REGION \

    --allow-unauthenticated \

    --platform managed \

    --vpc-connector $VPC_CONN_NAME \

    --update-env-vars PROJECT_ID=$PROJECT_ID,TOPIC_ID=$TOPIC_ID,REDISHOST=$REDIS_HOST,REDISPORT=$REDIS_PORT

```

# Usage

Once deployed, you can fetch the `$PREDICT_URL` from Cloud Run and `GET` or `PATCH` data to the API. For updates, since it just publishes data to a Pub/Sub topic, you will just receive either the submitted record, or error message.

```bash

# get URL of service

export PREDICT_URL=$(gcloud run services describe ai-demo-predict --format="value(status.url)" --platform managed --region $GCP_REGION)

# test the API

# view batches

curl $PREDICT_URL/batches

# view jobs

curl $PREDICT_URL/jobs

# submit batch corrections (feedback for retraining)

curl -XPATCH $PREDICT_URL/batches/f913f035-431a-11eb-b467-b9171009f67c \

    -H "Content-Type: application/json" \

    -d '{"subjects":["miketest-6"],"ratings":[1]}'

```

# Spec

See the OAS2/Swagger specification `config.yaml` for more details

# Validation

In an attempt to minimize *"garbage in"* but make the API user-friendly, adding thorough input checks to the `{model}.Bind()` with useful error message responses.

![AI demo architecture](./img_validation.png)

# Other considerations

Although this is only a demo, a few additional features that should be added would be:

- automated tests

- standardized Request / Response

- pagination

- retry logic with exponential backoff

- tracing, metrics using [opentelemetry](https://opentelemetry.io/)

- messages using [cloudevents](https://cloudevents.io/)

# Contributing

This is just a demo so fork and use at your own discretion.