MarketingCopy AI
Aegasis Labs helped MarketingCopy AI design the architecture of a multi tenant AI powered Software as a service application with Amazon ECS, Amazon Lambda, Sagemaker, Serverless architecture and more.
- Reliable system developed with 100% uptime and auto scaling capability
- Scalable machine learning services deployed in the cloud using AWS Sagemaker
- Micro-services architecture used for creating and deploying various services of the application
The Project
Our team was tasked with building an AI-powered marketing copywriter that would help businesses create high-quality content efficiently. The goal was to create a product that was not only effective but also cost-efficient and scalable. To achieve this, the team decided to use large language models (LLMs) and deploy the web app on AWS using serverless architecture.
The challenge
The challenge was to build an AI-powered marketing copywriter that was both effective and cost-efficient. The team also had to ensure that the product was scalable and could handle increasing traffic and usage. To achieve this, the team had to carefully choose the right AWS services and deploy the web app in a manner that was both efficient and cost-effective.
Our solution
Step behind the curtain and explore the heart of our projects on the Case Study Details Page. This curated collection delves into the intricacies of our design processes, strategic thinking, and collaborative endeavors.
Using MlOps architecture to deploy AI models
The AI models were deployed using an MLOps architecture. MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The key components of an MLOps solution were
- Version control to store, track and version changes for the AI code and training datasets
- ML Based workload to execute machine learning predictions
- Infrastructure as code (IaC) to automate the provisioning and configuration of cloud-based ML workloads and other IT infrastructure resources
- An ML (training/retraining) pipeline to automate the steps required to train/retrain and deploy ML models.
- A model monitoring solution to monitor production models’ performance to protect against both model and data drift. You can also use the performance metrics as feedback to help improve the models’ future development and training
Cloud web app
A service based architecture was used for frontend, backend and other micro-services. Listed below are the technologies that were used for UI, UX and backend development.
- Python Tensorflow
- Python for REST API
- Angular for UI of the platform
- Docker
- Kubernetes
- Various AWS Services (Sagemaker, Elastic Container Service, Lambdas, Step Functions, AWS Batch and more…)
Results
The results of the project were outstanding. The AI-powered marketing copywriter was not only effective but also cost-efficient and scalable. The use of LLMs and serverless architecture on AWS allowed the team to build a product that was able to handle increasing traffic and usage. The product was well received by businesses, and many of them reported significant improvements in their content marketing efforts. The cost-efficient deployment on AWS also allowed the businesses to save money and focus their resources on other areas of their business.
In conclusion, the AI-powered marketing copywriter was a success due to the use of LLMs and the deployment on AWS using serverless architecture. The product was able to deliver outstanding results, and the cost-efficient deployment on AWS allowed businesses to save money while still getting the benefits of a high-quality product. The team’s decision to use serverless architecture on AWS proved to be a smart move, and the results speak for themselves.