Overview
Designed to quickly help you launch GenAI workloads, DoiT’s Accelerator provides you with senior cloud architects, training, and assets to develop production-ready GenAI workloads on AWS. Tailored to your needs within a structured framework of three phases, taking you from technical validation to Proof-of-Concept (PoC) development, and then to production-ready workloads your team is trained to maintain self-sufficiently.
Discovery Meet with experts to further develop and validate your ideas, connecting your most pressing business challenges with how you can address them using GenAI solutions on AWS. Deliverables include:
- Technical and use-case validation
- Fundamentals and deep-dive training on AWS Bedrock, LLMs, and Sagemaker
- Written recommendation
Realize Turn your refined, prioritized ideas into a PoC, built with AWS best practices and sound architecture principles. Deliverables include:
- Target architecture working sessions
- Architecture validation
- Training on evaluating Foundational Models and adaptation strategies
- Reusable artifacts that help you quickly develop a PoC
- Development of a functional Proof of Concept environment on AWS
Launch Refine, clarify, and monitor your developed PoC to get it production-ready. Deliverables include:
- Prod-ready documentation, architecture diagrams and IaC assets
- Pre-production architecture review and Gen AI deployment audit
- Deployment code outlining the deployed environment
- Customizing Foundational Models
- Production-ready GenAI pipeline
Highlights
- Quickly go from idea to PoC: You’ll be paired with GenAI and ML experts to help you brainstorm and/or validate your ideas, select the right Foundational Model for your needs, and adapt to your needs via acquired learnings from training sessions to quickly transition into a PoC.
- Accelerated time-to-market: Through prompt design sessions and target deployment audits, you'll fine-tune your PoC into a scalable production-ready GenAI pipeline.
- Enablement for Day-2 and beyond: Your team will be enabled to self-sufficiently launch your workload and manage it in production through knowledge transfer, reusable IaC artifacts, and co-developed documentation and architecture diagrams.
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