MLOps on AWS

Start taking advantage of Machine Learning’s potential with MLOps practices now. By integrating DevOps best practices with machine learning operations, you can achieve AI models that are production-ready, scalable, secure, and high-performing.

In a data-driven business, Machine Learning is essential for extracting value from data, but is essentialfew workloads are truly ready to meet the scalability, security, and performance requirements of production environments. Adopting the MLOps model enables your data scientists to develop Machine Learning workloads within an integrated and highly automated environment.

beSharp guides you in the design, implementation, and management of ML-based workloads on AWS, ensuring an integrated and automated development environment that meets your business needs.

Our approach

beSharp partners with your company to implement MLOps practices on AWS, from requirement analysis to deploying your Machine Learning models into production.

Assess

Analysis of business needs and evaluation of new and existing ML automations to define the best MLOps strategy.

Design

Definition of a tailored strategy that integrates CI/CD principles and fosters a collaborative environment for your team.

Design & Build

Implementation of automated multi-step pipelines for model training and deployment, ensuring speed, automation, and security.

Continuous Improvement

Application of monitoring and continuous improvement practices to maintain high-performing and up-to-date MLOps solutions.

How We Work

beSharp has always supported companies in the design, implementation, and management of Cloud-ready workloads on Amazon Web Services. Thanks to our Cloud Experts’ expertise in managing operations, beSharp is able to help you deploy ML-based solutions on the AWS Cloud, regardless of their complexity or application context.

We will identify the challenges in bringing your Machine Learning pipelines to production, focusing on infrastructure and business aspects to ensure scalability, security, and optimal performance.
We will integrate DevOps principles into your architectures, creating automated pipelines for efficient model training and deployment.
We will build a collaborative environment that enables your team to share data, models, and code, ensuring governance and security.
We will implement Continuous Improvement to constantly optimize Machine Learning processes based on feedback and collected data.
We will implement Continuous Training to keep models up to date with new data, enhancing accuracy and relevance.
We will implement Continuous Testing to continuously validate models, detecting and correcting errors or performance degradations.
We will implement Continuous Monitoring to oversee the performance of models in production, detecting anomalies and ensuring business requirements are met.
We will provide support and training for your team to increase autonomy in managing MLOps pipelines on AWS.