MLOps on AWS

MLOps on AWS

MLOps on AWS

In an increasingly data-driven business world, Machine Learning is one of the most used techniques to extract value from large amounts of data. However, despite its popularity, only a small fraction of ML-based workloads are ready to meet production environments’ requirements, especially in terms of scalability, security, and performance. Due to the extremely high complexity of Machine Learning Pipelines, data scientists are more likely to focus on models development, fine-tuning, and training while missing out on the management, infrastructural aspects, and issues deriving from the deployment of these workloads in production.  Also, the development of ML-based projects introduces an additional set of problems, such as the need to refresh both datasets and models, that need to be managed from the operations point of view. Machine Learning Operations (MLOps) is a methodology that allows the application of DevOps principles and best practices such as Continuous Integration and Continuous Deployment to the development, tuning, and training of Machine Learning models while ensuring systems management, continuous updating, and security.

MLOps on Amazon Web Services with beSharp

As an AWS Advanced Consulting Partner, beSharp supports companies in designing, implementing, and managing Cloud-ready workloads on Amazon Web Services. Thanks to our Cloud Experts’ expertise in the management of the operations, beSharp is able to help you deploy ML-based solutions successfully on the AWS Cloud regardless of their complexity and application context. Adopting an MLOps approach allows data scientists to develop Machine Learning workloads within an integrated and highly automated environment. You’ll be able to:

  • Integrate CI/CD principles and quickly implement multi-step pipelines into your Cloud architectures to train and release models rapidly and automatically.
  • Create the perfect environment for sharing data, models, and code between the different roles in your team (data analysts, Machine Learning experts, and operational teams), ensuring governance, control, and security over both the development and the releases.
  • Keep the software components and environments always fresh and up-to-date and avoid the risk of datasets and models aging leveraging on MLOps principles of Continuous Improvement, continuous training, continuous testing, and continuous monitoring.
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