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Train and deploy a SOTA model#

Let's see GreatAI in action by going over the life-cycle of a simple service.

Objectives#

  1. You will see how the [great_ai.utilities][] can integrate into your Data Science workflow.
  2. You will use [great_ai.large_file][] to version and store your trained model.
  3. You will use GreatAI to prepare your model for a robust and responsible deployment.

Overview#

You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571. We are going to outperform it using a trivial text classification model: a Linear SVM.

We use the same synthetic dataset derived from the Microsoft Academic Graph. The dataset is available here.

Success

You are ready to start the tutorial. Feel free to come back to the summary section once you're finished.

Summary#

The training notebook#

We load and preprocess the dataset while relying on [great_ai.utilities.clean][] for the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map

After training and evaluating a model, it is exported using great_ai.save_model.

Remote storage

To store your model remotely, you need to set your credentials before calling save_model.

For example, to use AWS S3: 

from great_ai.large_file import LargeFileS3

LargeFileS3.configure(
    aws_region_name='eu-west-2',
    aws_access_key_id='MY_AWS_ACCESS_KEY',
    aws_secret_access_key='MY_AWS_SECRET_KEY',
    large_files_bucket_name='my_bucket_for_models'
)

from great_ai import save_model

save_model(model, key='my-domain-predictor')

The deployment notebook#

We create an inference function that can be hardened by wrapping it in a GreatAI instance.

from great_ai import GreatAI, use_model
from great_ai.utilities import clean

@GreatAI.create
@use_model('my-domain-predictor')   #(1)
def predict_domain(sentence, model):
    inputs = [clean(sentence)]
    return str(model.predict(inputs)[0])
  1. @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that it is always given to the function.

Finally, we deploy the model, inference function, and the GreatAI wrapping all of these. For that we either use: great-ai deploy.ipynb or build a Docker image.

Last update: July 10, 2022