Many companies face challenges when commencing their AI/ML journey, such as evaluating how AI/ML can add value to their organization and quantifying the value of solutions. In addition, companies are often limited by a lack of expertise and experience to truly maximize the ability of the ever-evolving technology landscape. To deliver true value, a solid foundation is required to reduce the risk of technical debt and ensure your data science and business unit leads can continue scaling and adapting to future requirements.

Based on our extensive experience, SoftServe has created a series of templates and reference architectures that complement the available services from AWS Cloud (e.g., SageMaker) that can be quickly adapted and tuned to specific client needs. Together with you, SoftServe will accelerate your journey to generate value from AI/ML, whilst reducing the risk and total cost of ownership of migrating to AWS Cloud-based AI/ML Solutions.

Solution Benefits: • Leverage AWS Cloud’s specialized AI/ML products to ensure fast and reliable delivery and adoption of AI/ML models for products • Customize a strategy for migration and optimization and provide CI/CD infrastructure for seamless deployment of AI/ ML models, so you can be confident that your models will deliver optimal performance in the cloud environment • Reduce costs by moving from on-prem to cloud-based environments

SoftServe experts will help you with: • Collaborative assessment: Work closely with your team to assess your organization's needs and challenges in embarking on the AI/ML journey • Customized strategy development: Tailor templates and reference architectures, leveraging AWS Cloud services like SageMaker to migrate and optimize your AI/ML solutions seamlessly. Fast and reliable delivery: Leverage specialized AI/ML products from AWS Cloud for efficient delivery and adoption of AI/ML models • Cost reduction through migration: Transition from on-premises to cloud-based environments, reducing costs and benefiting from AWS infrastructure

SoftServe’s ML LiftOff consists of three stages:

1. Model migration to AWS Cloud: The model object is integrated as an endpoint and migrated to AWS from another cloud or on-premises environment for inference by the solutions using SageMaker pipelines and accelerators. Benefits: Fast and reliable collection of model predictions using AWS infrastructure and low latency networking.

2. Minimum viable MLOps platform (CI/CD/CT): The AI/ML solution is redesigned to match the MLOps maturity level scalable/optimized (according to AWS approaches) using Terraform/CloudFormation templates and SageMaker accelerators. Benefits: The optimized migration strategy will be used with CI/CD, data transformation, and ML pipelines.

3. Governance and automated lifecycle ML architecture is optimized and extended to support a customized governance framework: deployment, monitoring, approvals, maintenance, and feedback processes. Benefits: The model delivers optimal results based on a continuous stream of data, and costs are optimized.