For organizations looking for a way to “democratize” data science, it is a must that data models are accessible to the enterprise in a very simple way. In our context, this is part of “model operationalization.” There are other solutions out there to serve data models which is a very common problem for data scientists. We’ve thought of having a REST API to handle running data models on a HTTP post with new data. Then, came — which allows serving data models as REST API without the complicated setup. MLFlow To serve models using MLFlow, we did the following: 1. Save R models as RDS format using the saveRDS function. 2. Convert RDS format into MLFLOW flavor using the mlflow_save_model(). model = readRDS(“./LR_Model.rds”) predictor <- crate(~ stats::predict.lm(model, as.data.frame(.x)), model) mlflow_save_model(predictor, “< path_to_save >”) 3. Commit to Git. 4. Setup a server to host the model. Clone git repo. 5. Serve model using the mlflow_rfunc_serve(). mlflow_rfunc_serve( , run_uuid = , host = ,port = ) "< path_to_save >" NULL "127.0.0.1" 8090 6. On users’ jupyter notebook, we have the following R lines to query the rest api endpoint with the variable storing the model response. output request_body_json <- toJSON(feature_dataframe[column_name_features],dataframe= ) request_body_json result = POST(url = paste0( , ),body=request_body_json,add_headers(.headers = c( = , = ))) output <- content(result) 'rows' 'http://127.0.0.1:8090/' 'predict/' "Content-Type" "application/json" "Accept" "application/json" 7. On python, a similar function is available import requests import json def mlflow_predict(dd,model_name= ): url = headers = { : , : } resp = requests.post(url=url,json=dd,headers=headers) res=json.loads(resp.content.decode( )) res[ ] 'LR12' "http://127.0.0.1:8090/predict/" "Accept" "application/json" "Content-Type" "application/json" "utf-8" return 'predictions' If you’re deploying on AWS, make sure to set host=”0.0.0.0" to make it listen to the public internet address. For Python-based models, MLFLow supports deploying to SageMaker. If you have multiple models to serve, MLFLOW assigns a port to each of them. Make sure you have a working R and Python installations. We used anaconda3 to setup the environment. Also, at least 1GB of RAM is needed to get R running with MLFlow in AWS LightSail.
