“BUILD ANYTHING YOU CAN IMAGINE. IMAGINE THINGS THAT SEEM IMPOSSIBLE” was the clear take out from the AWS Summit London 2025 at the Excel London.
AWS VP of Compute Services, Dave Brown introduced his KEYNOTE by emphasizing the AWS Building block- what you can imagine, you can build. We joined Dave Brown in celebration of increased AWS network capacity by 80% registered in 2024 over the last 12 months. Brown further highlighted:
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Virtual servers—Amazon EC2 Amazon Lightspeed
Containers—Amazon ECS, Amazon EKS
Serverless- AWS Lambda, AWS Fargate
Edge/ hybrid- AWS Wavelength, AWS OUTPOSTS
Brown emphasized that the right instance for any workload demanding huge compute power such as for scientific modelling, video transcoding, ML and mission critical enterprise workloads have the capacity to support large in-memory databases up to 32 TB RAM alongside complex analytics.
AWS AND NVIDIA PARTNERSHIP REACHES 14 YEARS
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AWS’s 14-year partnership with NVIDIA offers even more capability with EC2 capacity blocks facilitating testing short term training and fine tuning workloads, to run experiments and prototypes. Brown highlighted the benefits of AWS cornerstone storage solutions, announcing that a total of 53 internet storage/ data use access facilities now existed across:
Amazon Bedrock= Knowledge databases
Amazon Quicksight= Business dashboards
Amazon Redshift= High performance data analytics
Brown also introduced the audience to Apache Iceberg, the open source high performing table format for large analytical data sets stored in parquet. Iceberg extends parquet, ensuring users can build fully featured table data structures, supporting mutation snapshots and schema evolution. Assigning a hierarchy of meta data, scaling and maintenance without overheads is now possible thanks to Amazon S3, Brown declared.
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UNDERSTANDING MODELS—
Amazon Nova—Micro
Amazon Nova—Pro
Amazon Nova –Lite
Amazon Nova— Premier (launching soon)
CREATIVE CONTENT GENERATOR MODELS
Amazon Nova—Canvas
Amazon Nova—Reel
SPEECH TO SPEECH MODEL
Amazon Nova—Sonic (new)
DETERMINING OPTIONS—quality, speed and accessibility
7 Amazon Nova models with capabilities as follows:
Understanding and reasoning
Image and video generation
Speech to speech synthesis (feature recently launched)
https://negocioseinversionecuador.com/caso-practico/ COST OPTIMIZATION AND CUSTOMIZING MODELS
DATA CUSTOMIZATION is optimized by implementing a CUSTOMIZED MODEL into the workstream. Customization can be achieved through retrieval augmented generation (RAG), Prompt engineering, Fine Tuning, and Pre training of models.
DATA IMPLEMENTATION- into model and GenAI response outputs relevant to customer needs will increasingly deploy RAG, that combines the power of the LLM with the accuracy of content. RAG is known to work at optimal levels and can be deployed beyond unstructured data.
DIVERSIFYING BEDROCK— knowledge bases
Unstructured data (always supported historically with BEDROCK)
Structured data
Multimodal data
Knowledge graphs
Every data type can be easily managed within Bedrock that offers secure models to data integration systems, and the delivery of accurate and explainable responses to customers.
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Amazon Bedrock Guardrails enjoy the following plus points:
1. Word filters
2. Topic filters
3. Harmful content filters
PII filters (configured to block or mask sensitive information namely Personally Identifiable Information -PII). These blocks respond to any request that includes sensitive data sets so that a custom message may be generated when PII is detected in the prompt or response. This function is especially helpful for applications such as general Q&A platforms and publicly accessible documents.
4. Prompt injection
5. Security
The ROBIN AI case study was highlighted by Brown as an example of the robust technological response to a highly regulated industry, through the company’s use of Bedrock. The deployment of a finely tuned LLM by injecting proprietary legal knowledge to make data more scalable and accurate has been adopted. The results are as follows:
82% reduction in contract review time
Used model distillation to increase performance while reducing costs and improving latency
AI powered legal assistant capabilities- analysis of contracts, edit suggestions
Hallucinations- measuring automated reasoning and mathematical models at speed
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Mathematical methodology- aims to drive down uncertainty of Gen AI responses
Automated reasoning (policy)
Automated reasoning via Bedrock prevents factual errors generated due to hallucinations
Responses can be adapted to policy or guidelines set by automated reasoning parameters
The new suite of AWS cloud services has been designed to address customer concerns such as:
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Inference costs that may be too high
Cost versus performance trade off typically based on size of model
Model distillation= that leverages output of high parameter models to train low parameter models
Furthermore, smaller models are known to mimic the accuracy of larger models, but knowledge is focused on specific task/use cases. Distilled models are 500% faster and 75% less expensive than larger models. A wide range of complexity and types of responses that users need to generate include:
INTELLIGENT PROMPT ROUTING with deployment considerations required such as:
Compromising on data accuracy
Utility= Agents
Prompt= Response User
Agents Actions= Work through problems
The AWS cloud suite of solutions is destined to plan, select tools, generate results and reflect in order to determine if further iterations are required. Development lifecycle agent usage enables:
BUG TRACKING
BUILDING APPLICATIONS
Further advantages were determined as:
Agentic AI powers Amazon Q Development cycles thanks to the capacity to autonomously takes steps to generate new code.
Review Q Build then merge into application
Amazon Q can generate tests and documentation
Collectively, these factors facilitate AMAZON BEDROCK adoption that has been proven to be an easier and faster way to build to scale Gen AI applications.
In summary, AWS VP of Compute Services, Dave Brown communicated that the broadest choice of leading models were accessible as a fully managed and custom import option. This opens the door to four key benefits:
CUSTOMIZE WITH YOUR DATA— Through AWS knowledge bases
APPLY SAFETY AND RESPONSIBLE AI CHECKS- Guardrails enabled
OPTIMIZE FOR COST- Latency and accuracy, distillation and prompt routing facilitated
BUILD AND ORCHESTRATE AGENTS- Agent deployment