This Github repo contains all the assets needed to complete the TLMS Operational AI virtual workshop delivered by Continual.
For those who pre-registered to the workshop, you should have an invite in your inbox. Click on the link and sign up for a free trial of Continual! If you didn't receive an invite, please skip to the next step (Creating your own project).
After logging in, you should see a project named Topic_Classification connected to a demo Snowflake database and a classification model already created. The demo Snowflake database includes a dataset of consumer complaints filed with the Bureau of Consumer Financial Protection.
If you would like to step through the workshop yourself, create your own project, and/or didn't receive an invite to the Topic_Classification project, you can register for a trial directly by navigating here and creating your own project.
If you have been added to the aforementioned Topic_Classification project, then please skip the rest of this step.
Otherwise, connecting your own Continual project to a cloud data warehouse like Snowflake, BigQuery, Redshift, or Databricks is easy. Simply choose your vendor, enter the appropriate credentials, and then any remaining connection parameters that might be required.
If you are creating your own project, you will also want to load one or both of the consumer_complaints datasets provided from our workshop github repository here into a table in your database.
Assuming that you are creating your own project and following along, to help streamline the process for you, we have provided the following ddl.sql script that you can use to create the table definition in your target Snowflake schema and then a snowsql_staging.sql script to load the data through snowsql.
Follow these steps:
- Download the micro_consumer_complaints.csv and sample_consumer_complaints.csv files into your local directory
wget https://raw.githubusercontent.com/b-mckenna/TMLS_Operational_AI_Workshop/main/data/micro_consumer_complaints.csv
wget https://raw.githubusercontent.com/b-mckenna/TMLS_Operational_AI_Workshop/main/data/sample_consumer_complaints.csv- Make sure that you have installed SnowSQL
- Copy the SQL from the ddl.sql script into a worksheet in the Snowflake Web UI (updating the database name, schema name, and role accordingly)
- Using
snowsql, execute the provided snowsql_staging.sql script through the CLI
snowsql -a <account_name> -u <username> -w <WAREHOUSE_NAME> -f ./snowsql_staging.sqlℹ️ For the snowsql_staging.sql script, please update the script to point to the fully qualified paths of your local consumer_complaints.csv files. You will also want to update the database name and schema if they are different from "TMLS" and "CFPB" respectively. ℹ️ Your database name and schema name should ideally match what you specified when creating the connection for your project. If you left the schema blank when defining the connection for your project, the schema will be the same as your project ID (look in the URL).
Follow our documentation to install the Continual CLI into a virtual environment.
Alternatively, run the following bash commands:
virtualenv operational_ai_workshop
cd operational_ai_workshop/ && . bin/activate
pip3 install continualℹ️ Make sure you have python 3.8 or newer installed on your local machine
After the installation is complete, you can login to Continual using your credentials and select the Topic_Classification project (or your own project if doing it yourself).
Clone this github repo to proceed with the workshop.
git clone https://github.com/b-mckenna/TMLS_Operational_AI_WorkshopThe repo consists of a model definition and a custom algorithm extension. The model definition, stored as a model YAML file, contains attributes, such as the name of the model, the index being used, and a reference to the data table that contains the model spine. Model definitions can contain ids to other data entities, which Continual will then join with the model to build the training set. We typically recommend storing your features in feature sets for easier re-use and connecting your models to them via entity linking, but it's also possible to specify individual columns as input features to your model directly as we have done in this example.
Along with the core model definition, the model YAML defines the training, promotion, and prediction policies that Continual will be using with the model. In our example, we define a set of algorithms to run in the experiment to ensure our predictions are generated from the best performing model according to our evaluation metric. Other algorithm configurations, such as hyperparameters, can easily be declared as well. For each problem type, Continual has a default set of algorithms that it uses but users can specify additional custom algorithms to be added. For more details and options, please refer to the YAML reference page in our documentation.
Finally, to add a custom algorithm, users can write custom Python code to implement an algorithm of their choosing and run the following command:
continual extensions init –type algorithm –id MyCustomAlgorithmℹ️ We have provided an extension with this workshop already so you don't need to create your own (by running the above command)
In this particular workshop, we use DistilBERT, Tensorflow, and Huggingface to create a custom algorithm to include in our model experiment. All of our predictions are then written back to Snowflake for downstream applications to use / consume.
If you created your own project, you will first need to run the following command before pushing your model YAML file in the following steps:
continual push extensions/distilbert/extension.yaml
⚠️ If you are using a different python version locally than python 3.8 (e.g. python 3.9 or 3.10), you may need to edit the python version under/extensions/distilbert/pyproject.toml>tool.poetry.dependencies>pythonto match your local python version. Reach out to support@continual.ai if you need help with activating the extensions feature.
If you are following along with the workshop and using the pre-existing Topic_Classification project, feel free to skip this step.
In your favorite text editor, open and edit the /models/product_classification.yaml file. Change the name field from product_classification_example to product_classification__.
Once you've edited and saved product_classification.yaml, you can then submit it to Continual through the CLI by using the push command:
continual push ./models/product_classification.yamlℹ️ You don't need to change the file name itself. Only the "name" field in the file needs to be changed to avoid any conflicts.
Afterwards, copy and paste the resulting URL link that was generated from the continual push command into your browser to view your progress.
This URL link will take you to the Changes tab of your project, which contains all historical changes that have been made in your project. If you click into a change, you can monitor the progress.
To accomodate the time constraint of this abbreviated workshop, we've used a 100 row sample of the dataset and only 4 algorithms. Therefore, performance will be suboptimal and only meant for illustrative purposes. We will not be covering tuning model performance in this workshop.
If you'd like to train on a slightly larger and more representative dataset, you can edit your product_classification.yaml file to point to the table tmls.cfpb.sample_consumer_complaints instead. In this case, it will take roughly 2 hours to complete full model training, promotion, analysis, and deployment.
ℹ️ If you have created your own project and loaded the data into your own cloud data warehouse, update the
tableto point to the appropriate table instead (whether it's micro_consumer_complaints or sample_consumer_complaints).
ℹ️ For this workshop, we did not enable GPUs and all training will be performed on CPUs.
Once the model training has finished and you are in the Model Version page, you can review the performance of each algorithm across the evaluation metrics and train, validation, test, and evaluation datasets.
Click on Model Insights to analyze DistilBERT’s performance by reviewing the Local Interpretable Model-Agnostic Explanations (LIME). These charts, along with the models, are downloadable from the Artifacts tab.
Navigate back to the Model Overview page and scroll to the bottom to review the Snowflake tables housing our predictions.
Congratulations, our model is now live and being continually maintained / monitored!
Going forward, predictions will be refreshed on a weekly basis and the performance distribution will be tracked over time. At any point, you can easily add another algorithm, a custom SOTA or default, to the experiment based on your needs.