Perficient's ML AMP offering

Engagement Process Perficient’s ML AMP engagement follows our Envision Framework, guiding clients from ideation to scalable machine learning deployment on AWS. The process typically spans 6 to 10 weeks, depending on the complexity of the use case and the size of the opportunity. It begins with strategic workshops that align business and technical stakeholders around transformation goals and high-impact ML use cases. These sessions use tailored AWS demos and structured exercises to foster clarity and prioritize opportunities. Once a use case is selected, we assess data readiness by evaluating source availability, quality, and relevance to ensure a strong foundation for model development. Rapid prototyping follows, where functional ML models are built using real or representative data to validate hypotheses and demonstrate early value. This agile phase helps de-risk investment decisions and accelerates learning. Throughout the engagement, Perficient provides enablement through coaching, reusable templates, and best practices, empowering internal teams to sustain and evolve ML capabilities. As the engagement concludes, we deliver a comprehensive implementation roadmap that outlines next steps, required resources, and a realistic timeline for scaling the solution. This roadmap supports enterprise-wide adoption and operationalization of AI. The entire process is designed for speed, transparency, and measurable impact, helping organizations unlock the full potential of machine learning on AWS.

Architectural Approach The AMP architecture employs a cloud-native, modular approach leveraging AWS’s robust portfolio of AI services for rapid development and flexible deployment. Amazon SageMaker forms the backbone for model building, training, and hosting, while Amazon Bedrock provides managed access to industry-leading foundation models suited for generative AI workloads. Data pipelines, built using AWS Glue and Lambda, orchestrate data ingestion, cleaning, transformation, and annotation to deliver reliable, curated data into both prototyping and production environments. Security and compliance are addressed with AWS IAM, encryption at rest and in transit, and built-in auditing through AWS CloudTrail. CloudWatch monitoring and automated scaling ensure operational excellence, with all architecture components following the AWS Well-Architected Framework for reliability and resilience.

Business Outcomes With Perficient’s ML AMP, clients realize strategic gains such as accelerated time-to-value for AI projects and lower risk through early-stage validation and prototyping. The solution enables improved decision-making with actionable predictive insights, automated analytics, and enhanced customer experiences driven by next-generation AI. The approach ensures tangible ROI by tying technical outputs directly to business KPIs and transformation objectives.