How to deploy machine learning algorithm in production environment?

Question

I'm new to machine learning algorithm. I'm learning basic algorithms like regression, classification, clustering, sequence modelling, on-line algorithms. All the article that are available on internet shows how to use these algorithm with specific data. There is no article regarding deployment of those algorithm in production environment. So my questions are

1) How to deploy machine learning algorithm in production environment?

2) The typical approach follows in machine learning tutorial is to build the model using some training data, use it for testing data. But is it advisable to use that kind of model in production environment? Incoming data may keep changing so the model will be ineffective. What should be duration for the model refresh cycle to accommodate such changes?

Answer 1

DISCLAIMER: I work for this company, Datmo building a better workflow for ML. We’re always looking to help fellow developers working on ML so feel free to reach out to me at [email protected] if you have any questions.

1) In order to deploy, you should first split up your code into preprocessing, training and test. This way you can easily encapsulate the required components for deployment. Usually, you will then want to take your preprocessing, test, as well as your weights file (the output of your training process) and put them in one folder. Next, you will want to host this on a server and wrap an API server around this. I would suggest a Flask Restful API so that you can use query parameters as your inputs and output your response in standard JSON blobs.

To host it on a server, you can use this article which talks about how you can deploy a Flask API on EC2.

You can load and model and serve it as API as given in this code.

2) Hard for me to answer without more details. It's highly dependent on the type of data and the type of model. For example, for deep learning, there is no such thing as online learning.

Answer 2

I am sorry that my comments does not include too much detail* since I am also a newbie in "deployment" of ML. But since the author is also new in ML, I hope these basic guidance could be helpful as well. For "deployment", you should

1. Have ML algorithms: You may use free-tools, or develop your own tool using libraries in Python, R, Java, .Net, .. or use a system on cloud..)
2. Train those ML models using training datasets
3. Save those trained models (You should search this topic based on your development environment. There are some file formats that Tensorflow/Keras provide, or formats like pickle, ONNX,.. I would like to write a whole list here, with their supporting language & environment, advantage&disadvantage and loadability but I am also trying to investigate this topic, as a newbie)

And THEN, you can deploy these saved-models on production. On production you should either have your own-developed application to run the saved model (For example: an application that you developed with Python that takes trained&saved .pickle file and TestData as input; and simply gives "prediction for the test data" as output) or you should have an environment/framework that runs the saved models (search for ML environments/frameworks on cloud). At first, you should clarify your need: Do you need a stand-alone program on production, or will you serve a internal web-service, or via-cloud, etc.

For the second question; as above answers indicate the issue is "online training ability" of the models. Please additionally note that; for "online learning", your production environment has to feed your production tool/system with the real-correct label of the test data as well. Will you have that capability?

Note: All above are just small "comments" instead of a clear answer, but technically I am not able to write comments yet. Thanks for not de-voting :)

Answer 3

Regarding the first question, my service mlrequest makes deploying models to production simple. You can get started with a free API key that provides 50k model transactions a month.

This code will train and deploy, or update your model across 5 global data centers.

from mlrequest import Classifier
classifier = Classifier('my-api-key')
features = {'feature1': 'val1', 'feature2': 45}
training_data = {'features': features, 'label': 2}
r = classifier.learn(training_data=training_data, model_name='my-model', class_count=2)

This is how you make predictions, latency-routed to the nearest data center to get the quickest response.

features = {'feature1': 'val1', 'feature2': 77}
r = classifier.predict(features=features, model_name='my-model', class_count=2)
r.predict_result

Regarding your second question, it completely depends on the problem you are solving. Some models need to be frequently updated, while others almost never need to be updated.

Answer 4

I am not sure if this is a good question (since it is too general and not formulated good), but I suggest you to read about bias - variance tradeoff. Long story short, you could have low bias\high variance machine-learning model and get 100% accurate results on your test data (the data you used to implement a model), but you could cause your model to overfit the training data. As result, when you will try to use it on data which you haven't used during training it will lead to poor performance. On the other hand, you may have high bias\low variance model, which will be poorly fit to your training data and will also perform just as bad on new production data. Keeping this in mind general guideline will be:

1) Obtain some good amount of data which you could use to build a prototype of machine-learning system

2) Split your data into train set, cross-validation set and test set

3) Create a model which will have relatively low bias (good accuracy, actually - good F1 score) on your test data. Then try this model on cross-validation set to see the results. If the results are bad - you have a high variance problem, you used a model which overfit the data and can't generalize well. Re-write your model, play with model parameters or use different algorithm. Repeat until you get a good result on CV set

4) Since we played with the model in order to get a good result on CV set, you want to test your final model on test set. If it is good - that's it, you have a final version of model and could use it on prod environment.

Second question has no answer, it is based on your data and your application. But 2 general approaches might be used:

1) Do everything I mentioned earlier to build a model with a good performance on test set. Re-train your model on new data once in some period (try different periods, but you could try to re-train your model once you see that performance of model dropped down).

2) Use online-learning approach. This is not applicable for many algorithms, but for some cases it could be used. Generally, if you see that you could use stochastic gradient descent learning method - you could use online-learning and just keep your model up-to-date with the newest production data.

Keep in mind that even if you use #2 (online-learning approach) you can't be sure that your model will be good forever. Sooner or later the data you get may change significantly and you may want to use whole different model (for example switch to ANN instead of SWM or logistic regression).