Deploying a Trained CTGAN Model on an EC2 Instance: A Step-by-Step Guide

This article was written by John Patrick Laurel. Pats is the Head of Data Science at a European short-stay real estate business group. He boasts a diverse skill set in the realm of data and AI, encompassing Machine Learning Engineering, Data Engineering, and Analytics. Additionally, he serves as a Data Science Mentor at Eskwelabs.

Welcome to the initial installment in our series covering the deployment of machine learning models on AWS. As cloud computing and machine learning continually progress and converge, grasping the intricacies of deployment becomes increasingly crucial. Whether you're an enthusiast, an emerging data scientist, or an experienced professional, the insights shared in this series are designed to equip you with the knowledge to optimize the extensive ecosystem of AWS.

A common stumbling block for numerous novice machine learning practitioners is the tendency to confine their models within the confines of a Jupyter notebook. Imagine this scenario: following extensive hours or even days of data manipulation, feature refinement, model development, and validation, you've crafted a model with commendable metrics. Yet, what comes next? Frequently, these models fail to progress from the experimental stage to practical application. They linger within notebooks, neglected and underutilized despite their significant potential.

Introducing CTGAN—Conditional Generative Adversarial Networks specifically crafted for synthesizing tabular data. In this tutorial, we'll delve into CTGAN and explore the process of deploying a trained CTGAN model on an EC2 instance. But our journey doesn't end there. We'll take it a step further by establishing an API that, when activated, enables our model to produce data and effortlessly transfer it to an S3 bucket. Envision the possibilities of an instant data generator that enriches your storage with synthetic yet lifelike datasets!

Before we begin, here is a quick note for our readers: This guide presupposes that you already have an active AWS account and a fundamental grasp of AWS's basic principles and services. If you're new to AWS, it could be advantageous to acquaint yourself with its core functionalities beforehand. Now, with that clarified, let's delve into the realm of CTGAN deployment on AWS.

Prerequisites

For a seamless and trouble-free deployment, it's crucial to have all prerequisites in order. Here's a checklist of what you should have prepared before we proceed with the deployment:

AWS Account: If you haven't already, sign up for an AWS account. As mentioned in the introduction, this guide assumes a basic understanding of AWS concepts.

Python Environment: Make sure you have Python installed on your machine. For this deployment, we'll be utilizing the following Python libraries:

• sdv: For synthesizing data.
• Flask: To build our API.
• pandas: For data manipulation.
• boto3: AWS SDK for Python to interact with AWS services.

Pre-trained CTGAN Model: This serves as the foundation of our project. Ensure your CTGAN model is trained and prepared. If you don't have one, there are numerous online resources available where you can learn to train a CTGAN model or even find pre-trained models.

Docker: Since we'll be adopting containerization for this deployment, it's essential to have Docker installed on your machine. Containers enable us to encapsulate our application and all its dependencies into a unified unit, guaranteeing consistent behavior across diverse environments.

EC2 Instance Configuration:

• Instance Type: We’re using a t2.medium instance. This type offers a balance of compute, memory, and network resources, making it suitable for our deployment.
• Amazon Machine Image (AMI): The instance will run on the Amazon Linux 2 AMI, which is a general-purpose and widely used AMI.
• Security Group: Our security group, named “CTGANSynthesizerSG”, has been configured to allow SSH connections via port 22 and Flask API connections via port 5000.
• Storage: The EC2 instance has a block storage (EBS) of size 30GB with type gp2, which is a general-purpose SSD volume type.
• Key Pair: For this project, I created a key pair that allows secure SSH access to our EC2 instance, ensuring that our deployment is both safe and easily accessible.
• IAM Role: To make things simple, I granted the EC2 instance an IAM role named “EC2S3FullAccess,” which provides comprehensive permissions to interact with S3, ensuring our application can seamlessly upload generated data to the AWS storage service.

Make sure you can SSH into the EC2 instance from your local machine. If you're unfamiliar with this process, AWS offers comprehensive documentation on connecting to your EC2 instance.

Architecture Overview

At the core of any resilient solution lies its architecture. A well-defined and scalable architecture guarantees seamless operation and facilitates future upgrades or adjustments. As we delve into the deployment of our CTGAN model on an Amazon EC2 instance, comprehending the architectural flow is crucial. Below is a visual representation of the solution blueprint:

Description of the Architecture:

Users: The initial stage of our workflow involves users directing their requests to our deployed model hosted on the EC2 instance.

EC2 Instance Container:

• At the core of our deployment, the EC2 instance houses the Docker container, within which our CTGAN model operates.
• It's protected by particular security protocols to guarantee access only to essential traffic (such as SSH or certain API calls).
• Docker provides a segregated environment that guarantees the model operates in a stable configuration that is immune to external influences.

Trained CTGAN Model:

• CTGAN (Conditional Generative Adversarial Networks) is designed specifically for the creation of synthetic tabular data.
• In this instance, it has already undergone pre-training and stands prepared to produce data as soon as it receives user requests.

Amazon S3:

• We engage with S3 in two ways. Initially, our trained model may have been saved in and fetched from S3.
• Secondly, after the model creates synthetic data, this data is uploaded to an S3 bucket, facilitating effortless data storage and access.
• S3 provides a robust and scalable storage option, guaranteeing that our produced data is securely stored and easily retrievable.

This design's sophistication stems from its modular structure. Each component operates independently yet harmonizes with the others, providing flexibility and streamlining troubleshooting in the face of challenges.

API Code Explanation

Transitioning from our architectural overview, we now shift our focus to the code underlying our synthetic data generation API. This API is designed to oversee the complete data synthesis lifecycle, spanning from model initialization to data generation and quality assessment.

Although this code may initially seem intricate, it's organized for clarity and modularity. This guarantees ease of comprehension and facilitates straightforward modifications in the future.

This section involves preparing the essential imports, initializing the Flask application, and configuring logging and warning filters. We have also established a connection with Amazon S3, which stores and retrieves models and data.

Setup

Before delving into the core functionality, the environment is prepared by importing essential libraries. Flask is initialized to construct the API, and logging is set up to monitor crucial information. Additionally, the connection with Amazon S3 is established using boto3 to manage models and data efficiently.

Model Initialization

Following the API setup, our focus shifts to preparing our machine learning model, specifically CTGAN, for utilization. This phase involves retrieving the most recent model from Amazon S3 and initializing it. In the event that no model is detected, a new synthesizer is trained to ensure functionality.

When our application starts, we try to retrieve the latest model from our S3 bucket. If a model cannot be found, we resort to training a new synthesizer, ensuring that our API always operates with a model available.

Data Generation and Validation

This section focuses on the core functionality of the API, which involves generating synthetic data using CTGAN. Moreover, it includes code for validating the generated synthetic data to guarantee its quality.

Utilizing our trained model, we are able to produce synthetic data samples. Additionally, we have integrated a validation function to evaluate the quality and consistency of the generated synthetic data, ensuring it meets our predefined expectations.

Data Quality Evaluation and Cloud Metrics

In this section, the quality of the generated synthetic data is assessed, and the quality score is transmitted to Amazon CloudWatch. If the quality falls below a specified threshold, the synthesizer undergoes retraining.

Ensuring the high quality of our synthetic data is essential. Once the data has been validated, we assess its quality and benchmark it against actual data. This quality rating is then transmitted to Amazon CloudWatch. Should the quality fall short of expectations, we implement corrective actions by retraining the synthesizer.

Docker Local Testing and Deployment on Amazon EC2

Ensuring that applications perform seamlessly across all environments is essential in software deployment, particularly with intricate architectures and data. Docker facilitates this by enabling the creation, deployment, and operation of applications within containers, streamlining the process. Combined with Amazon Web Services (AWS), it offers a solid foundation for scaling your application deployments. This guide will cover how to test a synthetic data generation API locally using Docker before deploying it on an Amazon EC2 instance.

Docker Local Testing

Testing our application locally before deploying it to a production or cloud environment is crucial to identify any potential issues early. This approach guarantees that the application operates as intended and facilitates troubleshooting if needed.

Build the Docker Image:

This command generates a Docker image for our application. An image is a compact, self-contained executable package that includes everything needed to operate a specific software application.

Run the Docker Image:

After building our image, we can execute it. The -p flag is used to map a port on your local machine to the corresponding port on which your application operates within the Docker container.

Test the Data Generation API:

With the Docker container operational, we're now in a position to test the API endpoint that facilitates data generation.

Test the Data Quality Evaluation Endpoint:

Likely, we proceed to test the endpoint dedicated to assessing the quality of the data we've generated.

Deploying to an Amazon EC2 Instance

After conducting local tests, we're now prepared to deploy our application on an Amazon EC2 instance. EC2 provides scalable computing capacity in the cloud, facilitating the straightforward deployment of applications.

Note: This section is based on the assumption that you have an EC2 instance configured and the ability to SSH into it.

Prepare Your Deployment Files:

• For file transfer to EC2, compressing them into a single .zip file is a practical approach

Transfer the Zip to the EC2 Instance:

• Employ the scp command to transfer the zip file to your EC2 instance.

SSH into the EC2 Instance:

• Access your EC2 instance.

Update and Install Docker on EC2:

• On a new instance, make sure to update the system and then proceed to install Docker.

Unzip the Transferred Files:

• Extract the files from the zip we transferred.

Start Docker and Build the Image:

• Initiate Docker, and then move forward with building the Docker image.

Run the Docker Image with AWS Logging:

• Execute the Docker image, this time setting it up with Amazon CloudWatch logging to monitor the logs.

Test the Data Generation API on EC2:

• Just as we did with our local tests, let's proceed to test our endpoints. Now, we will perform these tests in the cloud environment.

Test the Data Quality Evaluation Endpoint on EC2:

• Finally, a test on the endpoint will be conducted to evaluate the quality of the data.

By following these steps, you've successfully tested your application locally with Docker and then deployed it on an Amazon EC2 instance. This process establishes a solid testing and deployment cycle, ensuring your application's reliability and scalability in real-world scenarios.

Data Quality Evaluation and Monitoring

Many discussions on model deployment tend to focus exclusively on the setup and deployment process, frequently neglecting a crucial component: the ongoing assessment and monitoring of data quality. Within the realms of Machine Learning Operations (MLOps) and data-centric applications, recognizing and preserving the integrity of your data after deployment is essential.

In the context of synthetic data generation, the quality of the generated data plays a crucial role in determining the effectiveness of machine learning models. Poor quality data can result in misleading outputs from models, jeopardizing the reliability of systems that depend on this data.

The Importance of Monitoring

1. Assurance of Data Integrity: Data serves as the foundation of any data-driven system, and its quality directly influences its performance. Continuous monitoring ensures that the system operates based on reliable data.
2. Early Detection of Anomalies: Regular monitoring is crucial for identifying and correcting anomalies or outliers in the data. If these issues are not addressed, they can distort the outcomes and predictions of machine learning models.
3. Adaptability: In dynamic environments, where data streams may evolve, monitoring ensures that your system adjusts to these changes, maintaining its accuracy. Additionally, consistent monitoring and reporting build stakeholder trust by affirming the system's reliability and precision.
4. Stakeholder Trust: Regular monitoring and reporting enhance stakeholders' confidence by assuring them of the system's dependability and precision.
5. Compliance and Regulations: Particularly in sectors such as finance and healthcare, maintaining data quality and its continuous monitoring are not merely best practices but are also essential for adhering to regulatory compliance requirements.

Pushing Quality Metrics to CloudWatch

Amazon CloudWatch offers real-time monitoring services for AWS resources and applications. By sending quality metrics to CloudWatch, you gain a comprehensive perspective on the performance of your synthetic data generation process over time.

Here’s a function that helps push these metrics to CloudWatch:

Practical Use of Monitoring

Envision developing a financial application that employs machine learning algorithms to forecast stock market prices. The accuracy of these predictions hinges on several factors, including the creation of synthetic data to simulate potential future market conditions. Given the volatile nature of financial markets, it's crucial that the data underpinning these forecasts is of impeccable quality.

If synthetic data quality begins to decline due to certain problems and no effective monitoring system is established, this deterioration might remain undetected. Such a scenario could lead to erroneous stock price predictions, potentially causing substantial financial repercussions and eroding user confidence.

By integrating CloudWatch metrics, any decrease in synthetic data quality is promptly identified through an alert system. This allows the team to swiftly investigate and address the underlying issue, ensuring the stock price prediction model remains unaffected and continues to operate with high accuracy.

Essentially, the ongoing assessment and surveillance of data quality transcend mere performance upkeep; they are pivotal for risk reduction and guaranteeing the precision and dependability of the insights derived from the data. Adopting a forward-looking stance, fortified by immediate monitoring solutions such as CloudWatch, guarantees swift intervention for any concerns that arise, thus preserving the whole system's integrity.

Final Remarks

Implementing a machine learning model in a production setting is an intricate process that extends well beyond the initial coding and training stages. Throughout this article, we've explored the various dimensions of building a robust system, including the design of the architecture, integrating APIs, utilizing Docker for containerization, deploying on cloud services such as Amazon EC2, and the crucial task of continuously monitoring data quality in real time.

Our exploration started with an in-depth look at the system architecture, offering a broad perspective on the interplay between various elements. We then delved into the specifics of the API code, examining its functions and its critical role in our deployment process. Following this, we covered the practice of local testing using Docker and the steps for deploying onto an EC2 instance, providing practical insights into operationalizing our machine learning models in a real-world scenario.

However, as emphasized, the journey doesn't end with deployment. Ongoing monitoring of data quality, particularly crucial in applications that use synthetic data, becomes the cornerstone for upholding the system's reliability and performance.

For data scientists, developers, or anyone keen on machine learning, this article offers a thorough roadmap for model deployment, highlighting not only the technical procedures but also the importance of constant monitoring after deployment. It reinforces the notion that in the realm of MLOps, the deployment of a model marks not the end but the start of an ongoing commitment. As the technological and data landscapes shift, our approaches and techniques must also adapt, ensuring we deliver the most effective and reliable outcomes.

Whether you're just beginning your journey in model deployment or you're an experienced professional, this article aims to illuminate the complexities involved and encourage a comprehensive approach to MLOps, from the initial stages through to ongoing monitoring. In the ever-evolving field of machine learning, the keys to success are staying informed, being flexible, and remaining vigilant.

* This newsletter was sourced from this Tutorials Dojo article.