Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

-------

Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.

Topics Include:

• Introduction to MongoDB's vector search and AWS Bedrock
• Core concepts of vectors and embeddings explained
• High-dimensional space and vector similarity overview
• Embedding model use in vector creation
• Importance of distance functions in vector relations
• Vector search uses k-nearest neighbor algorithm
• Euclidean, Cosine, and Dot Product similarity functions
• Applications for different similarity functions discussed
• Large language models and vector search explained
• Introduction to retrieval-augmented generation (RAG)
• Combining external data with LLMs in RAG
• MongoDB's document model for flexible data storage
• MongoDB Atlas platform capabilities overview
• Unified interface for MongoDB document model
• Approximate nearest neighbor search for efficiency
• Vector indexing in MongoDB for fast querying
• Search nodes for scalable vector search processing
• MongoDB AI integrations with third-party libraries
• Semantic caching for efficient response retrieval
• MongoDB's private link support on AWS Bedrock
• Future potential of vector search and RAG applications
• Example use case: Metaphor Data's data catalog
• Example use case: Okta's conversational interface
• Example use case: Delivery Hero product recommendations
• Final takeaways on MongoDB Atlas vector search

Participants:

• Benjamin Flast - Director, Product Management, MongoDB

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

-------

David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.

Topics Include:

• Introduction by Dave Gildea, VP of Product at Druva.
• Focus on building generative AI applications.
• Emphasis on cost and speed optimization.
• Mention of Amazon's Matt Wood keynote.
• AI experience with kids using "Party Rock."
• Prediction: GenAI as future workplace standard.
• Overview of Druva's data security platform.
• Three key Druva components: protection, response, and compliance.
• Druva's autonomous, rapid, and guaranteed recovery.
• Benefits of Druva’s 100% SaaS platform.
• Handling 7 billion backups annually.
• Managing 450 petabytes across 20 global regions.
• Druva’s high NPS score of 89.
• Introduction to Dru Investigate AI platform.
• Generative AI for cybersecurity and threat analysis.
• Support for backup and security admins.
• Simplified cybersecurity threat detection.
• AI-based natural language query interpretation.
• Historical analogy with Charles Babbage’s steam engine.
• "Fail upwards" model for LLM optimization.
• Using small models first, escalating to larger ones.
• API security and customer data protection.
• Amazon Bedrock and security guardrails.
• Testing LLMs with Amazon’s new prompt evaluation tool.
• Speculation on $100 billion future model costs.
• Session wrap up

Participants:

· David Gildea - VP Product Generative AI, GM of CloudRanger, Druva

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

-------

Urmila Kukreja of Smartsheet and Nick Simha of AWS discuss leveraging Amazon Q’s Retrieval-Augmented Generation (RAG) solution to enhance productivity by enabling employees to quickly access relevant information within secure, integrated workflows like Slack, improving efficiency across the organization.

Topics Include:

• Introduction by Nick Simha, AWS.
• Overview of Amazon Q’s role in data analytics and Gen AI.
• Gen AI’s impact on productivity, ~30% improvement backed by Gartner study findings.
• General productivity improvement seen across various departments.
• Amazon Q’s developer code generation tool – rapid development
• Gen AI and LLMs’ challenges: security, privacy, and data relevance.
• Foundation models lack specific organizational knowledge by default.
• Empowering Gen AI to grant system access can cause issues
• Privacy concern: Sensitive data, like credit card info, can be central in data breaches
• Compliance is critical for organizational reputation and data integrity.
• Data integration techniques: prompt engineering, RAG, fine-tuning, custom training.
• RAG (Retrieval Augmented Generation) balances cost and accuracy effectively.
• Implementing RAG requires complex, resource-heavy integration steps.
• Amazon Q simplifies RAG integration with "RAG as a service."
• Amazon Q’s Gen AI stack overview, including Bedrock and model flexibility.
• Amazon Q connects to 40+ applications, including Salesforce and ServiceNow.
• Amazon Q respects existing security rules and data privacy constraints.
• Plugin functionality enables backend actions directly from Amazon Q.
• All configurations and permissions can be managed by administrators.
• Urmila Kukreja from Smartsheet explains real-world Q implementation.
• Smartsheet’s Ask Us Engineering Slack channel: origin of Q integration.
• Q integration in Slack simplifies data access and user workflow.
• "Ask Me" Slack bot lets employees query databases instantly.
• Adoption across departments is high due to integrated workflow.
• Future plans include adding data sources and personalized response features.
• Session wrap up

Participants:

• Urmila Kukreja – Director of Product Management, Smartsheet
• Nick Simha - Solutions Architecture Leader - Data, Analytics, GenAI and Emerging ISVs, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/