Organizations face challenges with managing on-premises and open source data science solutions
The problem:
- Many organizations use a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.
- Data science and DevOps teams may struggle to manage isolated tool stacks and systems.
- There is a need for a unified platform to streamline ML model management.
The solution:
- Wipro’s AWS AI/ML Practice suggests leveraging AWS services to create a unified platform for ML model management.
- Building on AWS services like AWS Lambda, Step Functions, and S3, organizations can simplify ML model management by automating the deployment, monitoring, and scaling of models.
- This approach also enables teams to leverage AI/ML capabilities provided by AWS, such as AWS SageMaker, to develop, train, and deploy ML models.
The benefits:
- A unified platform built on AWS services simplifies ML model management, reducing operational complexities.
- Automation with AWS services enables faster deployment and scalability of ML models.
- Teams can take advantage of AWS AI/ML capabilities to enhance their ML development process.
Author’s take:
Managing on-premises and open source data science solutions can be challenging for organizations. By leveraging AWS services, such as AWS Lambda, Step Functions, and S3, organizations can simplify ML model management and take advantage of AI/ML capabilities. With a unified platform built on AWS, organizations can automate deployment, monitoring, and scaling of ML models, reducing operational complexities and enabling faster development and scalability.