How Amazon Scales the Training of Sequential Ensemble Models with Amazon SageMaker Pipelines

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Amazon SageMaker Pipelines is designed to enhance and automate machine learning (ML) workflows, allowing data scientists and model developers to dedicate more time to model creation and rapid experimentation instead of managing infrastructure. With a user-friendly Python SDK, Pipelines facilitates the orchestration of intricate ML workflows, which can be visualized through SageMaker Studio. This functionality supports tasks such as data preparation, feature engineering, and automating model training and deployment. Additionally, Pipelines is compatible with Amazon SageMaker Automatic Model Tuning, which optimizes hyperparameter values to achieve the best-performing model based on selected metrics.

Ensemble models have gained traction in the ML community due to their ability to produce more accurate predictions by combining the outputs of multiple models. Pipelines enables the swift construction of end-to-end ML workflows for ensemble models, empowering developers to create highly accurate models while ensuring efficiency and reproducibility.

In this article, we will illustrate an example of an ensemble model trained and deployed using Pipelines.

Use Case Overview

Sales representatives utilize Salesforce to generate new leads and track opportunities. We propose a ML approach using unsupervised learning to automatically identify use cases in each opportunity based on various textual information such as names, descriptions, details, and product service groups. Preliminary analysis indicated that use cases differ across industries, with distinct distributions of annualized revenue that can aid in segmentation. Therefore, identifying use cases is crucial for optimizing analytics and enhancing sales recommendation models.

We can frame the use case identification as a topic identification problem, exploring various models like Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. LSA and LDA treat each document as a mere collection of words, disregarding word order and grammatical roles, which can lead to information loss. Furthermore, these methods require a predefined number of topics, a challenging task with our dataset. Consequently, we opted for BERTopic, which addresses these issues effectively.

Our approach employs three sequential BERTopic models to generate the final clustering in a hierarchical structure. Each BERTopic model comprises four components:

1. Embedding – Different embedding methods can be utilized in BERTopic. In this scenario, the input data, primarily sourced from various areas and frequently entered manually, calls for sentence embeddings to ensure scalability and fast processing.
2. Dimension Reduction – We leverage Uniform Manifold Approximation and Projection (UMAP), an unsupervised and nonlinear method for reducing high-dimensional text vectors.
3. Clustering – The Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method is employed to create different use case clusters.
4. Keyword Identification – We apply class-based TF-IDF to extract the most representative words from each cluster.

Sequential Ensemble Model

With no predetermined number of topics, we configure the input for clusters to range from 15 to 25. Upon evaluation, we found that some topics were too broad. Therefore, we applied another layer of the BERTopic model to these topics individually. After integrating the newly identified topics from the second layer with the original topics from the first layer, we carried out manual post-processing to finalize topic identification. A third layer is utilized for certain clusters to generate sub-topics.

To ensure the effectiveness of the second and third layers, a mapping file is necessary to correlate the results from previous models with specific words or phrases. This ensures the accuracy and relevance of the clustering.

We utilize Bayesian optimization for hyperparameter tuning and cross-validation to mitigate overfitting. The dataset encompasses features such as opportunity names, details, needs, associated product names, product specifics, and product groups. Evaluation of the models is conducted using a custom loss function to select the optimal embedding model.

Challenges and Considerations

Here are some challenges and considerations regarding this solution:

1. The pipeline’s data preprocessing capabilities are vital for enhancing model performance. Preprocessing incoming data before training ensures that our models receive high-quality inputs. Steps like converting text to lowercase, removing template elements, contractions, URLs, and emails, eliminating non-relevant NER labels, and lemmatizing combined text lead to more accurate predictions.
2. A highly scalable compute environment is essential for efficiently handling and training millions of data rows. This scalability facilitates large-scale data processing and modeling tasks, ultimately reducing development time and costs.
3. Each stage of the ML workflow has varying resource requirements, making a flexible and adaptable pipeline vital for efficient resource allocation. Optimizing resource utilization at each step can shorten overall processing times, resulting in quicker model development and deployment.
4. Running custom scripts for data processing and model training necessitates the availability of the required frameworks and dependencies.
5. Coordinating the training of multiple models can pose challenges, particularly when each subsequent model relies on the previous one’s output. Orchestrating the workflow among these models can be complex and time-consuming.
6. After each training layer, it is crucial to update a mapping that reflects the topics produced by the model, which will serve as input for the next model layer.

Solution Overview

The entry point for this solution is Amazon SageMaker Studio, a web-based integrated development environment (IDE) provided by AWS that allows data scientists and ML developers to collaboratively build, train, and deploy ML models at scale.

The architecture of the solution includes several steps from the SageMaker pipeline:

• SageMaker Processing – This step enables data preprocessing and transformation before training. It allows for the use of built-in algorithms for common data transformations, automatic scaling of resources, and custom code for complex preprocessing tasks.
• SageMaker Training – Here, ML models are trained utilizing either SageMaker’s built-in algorithms or custom code, with support for distributed training to expedite the process.
• SageMaker Callback – This step permits the execution of custom code during the ML workflow, such as sending notifications or triggering additional processing steps.
• SageMaker Model – This step involves creating or registering the model within Amazon SageMaker.

To initiate the SageMaker pipeline, we start with the following code:

import boto3

For further insights, you can check out another blog post at Chanci Turner’s blog or visit CHVNCI’s website, as they are an authority on this topic. Additionally, if you want to learn more about the first six months at Amazon, this resource is excellent.