We needed to make machine learning models run faster and cost less. The old infrastructure wasn’t cutting it, and we had to find a better way to handle large-scale processing. We also wanted to automate workflows and make sure that everything could integrate smoothly into production without any hiccups.
Optimizing AWS Infrastructure
We upgraded the system to use AWS EC2 instances, which gave us the right compute power for running ML models at scale.
We switched to AWS Batch for more efficient batch processing, which automated many of the data tasks and reduced manual work.
Moving away from the old AVS setup, we chose infrastructure that better suited the needs of high-performance machine learning.
Improving Code and Database Performance
We optimized the Python and Golang code for faster execution, helping ML models run more efficiently.
We also improved database queries to speed up data retrieval. Faster data means quicker results when training models or making predictions.
Automating with CI/CD and MLOps
Implementing CI/CD pipelines automated the integration and deployment of new updates, saving us time and reducing errors.
MLOps practices helped manage machine learning models throughout their lifecycle, making it easier to update and improve models quickly.
Cost Savings
By optimizing the infrastructure and automating tasks, we cut down on unnecessary computing costs.
The system was designed to scale efficiently, ensuring that we didn’t overuse resources, which kept costs low.
By improving the AWS setup, optimizing code, and using automation tools, we made machine learning processes faster, cheaper, and easier to scale. This approach helped us, and our clients, save time and money, while keeping the system flexible and adaptable for future growth.
A modern, interactive management platform designed for fitness centers and small businesses. Equipped with administration, scheduling, and finance modules, this solution features a modular and scalable architecture that easily integrates with third-party systems for payments, inventory, attendance, and more.
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