scikit-learn demo

In this demo, we'll go through deploying a pre-trained scikit-learn model, querying this model to make inferences, how to host your data on Algorithmia, and finally, we'll call the algorithm in batch mode on the data we uploaded to Algorithmia.

This demo features:

• Hosting models in the Algorithmia platform
• Hosting prediction data in the Algorithmia platform
• Use of multiple query types, on this case: real time and batch inference

Prerequisites

Clone this repository so you have access to the code and data files. While we will use the Algorithmia Web IDE in this demo note that you can also use a repository from Github or you can use the CLI to deploy your model instead once you've created an algorithm and cloned your repository.

Upload pre-trained model to Data Collections

In this demo, we are going to host the serialized pre-trained model and the test data on the Algorithmia platform, for more info look at the Data Collections docs.

To create a Data Collection:

• Log in to your Algorithmia account and click on Data Sources on the left navigation sidebar
• Click on New Data Source on the top right corner, select Hosted Data Collection and name it scikit_learn_demo
• You can now either select or drag and drop the files data/boston-regression.joblib and data/boston_test_data.csv to upload them to the Algorithmia Hosted Collection

Create the Algorithm

From the Algorithmia home page click the Create new button at the top right of the navbar and select Algorithm. Let's go through the form together to create our algorithm.

• Name the Algorithm scikit_learn_demo
• On the Source Code section select Algorithmia and Source Code visibility: Restricted
• On the Environment section select Language: Python 3 and Environment: Python 3.7.
• You can leave the other values as the default ones
• Click on Create new algorithm and click on Web IDE on the pop up

Add the source code

If you are not in the Web IDE already go to the new Algorithm page and click on the Source Code tab to go to the Web IDE

• Open the file with the same name as the algorithm, in this case, it would be: scikit_learn_demo.py
• Delete that code, and copy and paste the code from the file src/boston-housing-prices.py
• Change the path to the serialized model on the Data Collection. Recall our data collection is called scikit_learn_demo and you'll also need to change USERNAME to your own username or organization on this line:

model_file_path = "data://<USERNAME>/scikit_learn_demo/boston-regression.joblib"

Be sure to save these changes.

Note that you always want to initialize the model outside of the apply function. This way, after the model is initially loaded, subsequent calls will be much faster within that session. That is something we do on this example source code.

Add Dependencies

Click the Dependencies button in the top navigation. The new dependencies should be:

algorithmia>=1.0.0,<2.0
numpy==1.19.4
scipy==1.5.3
scikit-learn==0.23.2

Click on Save dependencies so that it gets saved to the requirements.txt file.

Compile Algorithm

Click the Build button in the top right to compile the algorithm, this will take around 1 minute. You will see something like Algorithm version algo://<USERNAME>/scikit_learn_demo/ad9bb8c0ae1d9c267ea3803bfa9b7c2652bb1921 is now online when the model its built, now we can use it to make predictions.

Making predictions

Now you can make a real time inference on the model by, for example:

> {"predict": [[0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45]]}
[31.048342563320485]

Finally, you can pass the path to the file we uploaded earlier to make a batch prediction:

> {"batch": "data://danielfrg/scikit_learn_demo/boston_test_data.csv"}
[31.048342563320485,28.91587252133459 ... ]

Note that these different query types are defined in the source code we copy/pasted earlier you can make the algorithms as simple or complex as you need.

More documentation

• Documentation for Scikit-Learn
• Working with Data