Learn how we helped a technology company scale their Machine Learning (ML) platform.
Learn how Amazon SageMaker AI accelerates computer vision projects by providing powerful tools for rapid experimentation and seamless scaling from development to production.
New methodologies, frameworks, and solutions for delivering the next generation of cloud services will cut migration and modernization timelines from years to months.
Brian is an AWS Community Hero, Alexa Champion, runs the Boston AWS User Group, has ten US patents and a bunch of certifications. He's also part of the New Voices mentorship program where Heros teach traditionally underrepresented engineers how to give presentations. He is a private pilot, a rescue scuba diver and got his Masters in Cognitive Psychology working with bottlenosed dolphins.
View Brian's articlesA technology company specializing in email signature parsing and contact data extraction recently engaged with Caylent to solve ML scaling challenges. Their software helps businesses automatically capture and organize contact information from email signatures, streamlining the process of managing customer and prospect data. By leveraging advanced parsing algorithms, the company enables businesses to efficiently extract valuable contact details from email communications, reducing manual data entry and improving the accuracy of customer relationship management systems.
The company recently experienced rapid growth, and had challenges scaling their machine learning operations (MLOps) to meet increasing demand. Being a small/medium-sized business (SMB), they encountered difficulties in efficiently training and deploying ML models while maintaining high performance standards. The company needed a solution that would allow them to implement a scalable ML platform, automate model training and deployment processes, improve model versioning and performance tracking, and reduce manual efforts in their ML workflows.
The company's existing infrastructure was struggling to keep up with the growing volume of data and the need for more frequent model updates. They required a robust system that could handle their expanding needs while remaining cost-effective and manageable for their small team.
To address the company's needs, a comprehensive MLOps solution was designed and implemented using AWS services and open-source tools. The solution focused on creating a scalable, reproducible, and automated workflow for machine learning operations.
The tech stack included Kubeflow for MLOps orchestration, Amazon SageMaker AI for ML workloads such as training and deployment, AWS CDK for Infrastructure as Code, GitHub for code sourcing, and GitHub Actions for CI/CD pipelines. Additional AWS services like Lambda, SNS, and S3 were utilized for storage, supporting pipelines, and triggers.
The solution implemented an end-to-end MLOps pipeline consisting of three main components:
Key features of the solution included automated CI/CD for data and models, SageMaker Model Registry for version tracking and comparison, real-time machine learning releases, and an automated retraining process with minimal manual intervention.
The design prioritized scalability and reproducibility, ensuring that the company could easily manage multiple models and expand their ML capabilities as their business grew. By leveraging AWS services like SageMaker AI, the solution optimized infrastructure costs while maintaining high performance.
The implementation of the new ML platform significantly improved the company's machine learning operations across several key areas:
These improvements positioned the company to better serve their growing customer base and maintain their competitive edge in the email signature parsing and contact data extraction market. The new MLOps platform provided a solid foundation for future growth and innovation, allowing the company to focus on expanding their product offerings and entering new markets with confidence in their ML infrastructure.
At Caylent, we understand the complexities of scaling machine learning operations and the transformative impact they can have on your business. Our suite of Caylent Catalysts, including our MLOps with SageMaker AI and MLOps Strategy, are designed to help organizations like yours streamline and enhance their ML workflows. Whether you're dealing with rapid growth, seeking improved model performance, or aiming to reduce manual efforts, our team is equipped to provide customized solutions that meet your unique needs. Contact us today to discover how we can help you transform your machine learning operations.
Caylent Catalysts™
Plan and implement an MLOps strategy unique to your team's needs, capabilities, and current state, unlocking the next steps in tactical execution by offloading the infrastructure, data, operations, and automation work from data scientists.
Caylent Catalysts™
Leverage Amazon SageMaker AI's MLOps toolset to reduce your time-to-market, streamline administrative tasks, lower your operational costs, and free up valuable time for data scientists and engineers to focus on innovation and differentiation.
Brian Tarbox