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Dan Sullivan reposted thisDan Sullivan reposted thisA must read for Cloud Architects!!: https://google.smh.re/5R8q This paper explains how to embed identity, discovery, and authorization directly into the cloud fabric. Using open standards like SPIFFE and Envoy ensures your agents scale safely into production. Written by Googler Victor Moreno and team. ✅ https://google.smh.re/5R8q #ai #infrastructure #googlecloud https://google.smh.re/5R8q techperts Mark (VJ) Vijaykumar Saurabh Salem Sathish Dan Marcus Yujun Stacy Mark
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Dan Sullivan reposted thisDan Sullivan reposted thisPetabyte-scale data comes with a long list of challenges. Here are two of the biggest. High costs: The cost of ingesting, storing, and analyzing massive volumes of data forces teams to choose between their data and their bottom line. The bottom line usually wins, forcing teams to sample, aggregate, or discard data, leading to significant gaps. Lack of access and domain knowledge: Even when teams have complete data, it’s often difficult to reason about and understand. Data is only valuable if teams can get insights from it. Hydrolix solves the first problem. Model Context Protocol (MCP) solves the second. The Hydrolix MCP Server is now available for server-side deployment. Any MCP-compatible AI assistant can connect to your Hydrolix cluster through a simple URL. No local installs. No tool-switching. No waiting on the one person who fully understands the SQL and the schema. To see what that looks like under pressure: one enterprise engineer joined an incident call, connected their AI assistant, and asked a single question about elevated 5XX errors. The MCP server queried full-fidelity log data, surfaced the pattern, and the team had a fix in minutes. That's what happens when complete data meets complete access. Read more: https://hubs.la/Q045Xf9H0
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Dan Sullivan reposted thisDan Sullivan reposted this
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Dan Sullivan reposted thisDan Sullivan reposted thisI hosted my first LinkedIn Learning Office Hours today for the AI Challenge: Build an AI Agent in 7 Steps in 7 Days with Amazon Web Services (AWS). We walked through the full architecture, what you build each day, and the errors people are running into. The 7 days in 30 seconds: create an agent in Bedrock, connect it to Lambda for real actions, add session memory, ground it with a knowledge base, attach guardrails, deploy and invoke from Python, then wire up CloudWatch logging for observability. The most common issues so far: model access not enabled, permissions between Lambda and Bedrock not set in both directions, Lambda timeouts too low, and trying to create a knowledge base as the root user. One update: Claude 3.7 Sonnet v1 has moved to legacy status. If you hit a ResourceNotFoundException, switch to Amazon Nova Pro in the Agent builder. It works with all 7 steps. We also covered where OpsAssistant goes next: multi-agent systems and Amazon Bedrock AgentCore for managed runtime, tool integration, memory, and observability at scale. The challenge is free on LinkedIn Learning until the end of June. Link in the comments. Share your progress with #7DayAWS.
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Dan Sullivan reposted thisDan Sullivan reposted thisStop "Flying Blind": Bot Strategy as a Continuous Decision Loop Listen as Hydrolix COO, Antony Falco explains that effective bot protection is not a simple on/off switch, but rather a strategic, ongoing decision loop. Learn more here https://hubs.la/Q04761BZ0
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Dan Sullivan reposted thisDan Sullivan reposted this🚨 Google just dropped a FREE guide on AI Agents Now anyone can learn how to build AI agents from the experts. Free to download, no sign-up required. -- ☁️👨💻 I post about Data & AI. I also share thoughts and resources along the way. Follow me for more. 👍 Like, 🔗 share, ➕save, 💬 comment, 👉 follow
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Dan Sullivan reposted thisDan Sullivan reposted thisDiscover how search engines are continuously changing. Srini Kadamati provides insights into the functions of both SEO and GEO. Find out more about this topic and the Hydrolix Bot Insights solution at https://hubs.la/Q0470CpM0. #bottraffic #captcha #dataanalytics #ai
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Dan Sullivan reposted thisDan Sullivan reposted thisRead my latest post on the O'Reilly blog on what OpenClaw reveals about the next phase of #AI agents. Have you played around with OpenClaw? If so, I'd love to hear about your experience in the comments below. https://lnkd.in/eM5UKNZ8What OpenClaw Reveals About the Next Phase of AI AgentsWhat OpenClaw Reveals About the Next Phase of AI Agents
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Dan Sullivan liked thisDan Sullivan liked thisA must read for Cloud Architects!!: https://google.smh.re/5R8q This paper explains how to embed identity, discovery, and authorization directly into the cloud fabric. Using open standards like SPIFFE and Envoy ensures your agents scale safely into production. Written by Googler Victor Moreno and team. ✅ https://google.smh.re/5R8q #ai #infrastructure #googlecloud https://google.smh.re/5R8q techperts Mark (VJ) Vijaykumar Saurabh Salem Sathish Dan Marcus Yujun Stacy Mark
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Dan Sullivan liked thisDan Sullivan liked thisPetabyte-scale data comes with a long list of challenges. Here are two of the biggest. High costs: The cost of ingesting, storing, and analyzing massive volumes of data forces teams to choose between their data and their bottom line. The bottom line usually wins, forcing teams to sample, aggregate, or discard data, leading to significant gaps. Lack of access and domain knowledge: Even when teams have complete data, it’s often difficult to reason about and understand. Data is only valuable if teams can get insights from it. Hydrolix solves the first problem. Model Context Protocol (MCP) solves the second. The Hydrolix MCP Server is now available for server-side deployment. Any MCP-compatible AI assistant can connect to your Hydrolix cluster through a simple URL. No local installs. No tool-switching. No waiting on the one person who fully understands the SQL and the schema. To see what that looks like under pressure: one enterprise engineer joined an incident call, connected their AI assistant, and asked a single question about elevated 5XX errors. The MCP server queried full-fidelity log data, surfaced the pattern, and the team had a fix in minutes. That's what happens when complete data meets complete access. Read more: https://hubs.la/Q045Xf9H0
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Dan Sullivan liked thisDan Sullivan liked this
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Dan Sullivan liked thisDan Sullivan liked thisI hosted my first LinkedIn Learning Office Hours today for the AI Challenge: Build an AI Agent in 7 Steps in 7 Days with Amazon Web Services (AWS). We walked through the full architecture, what you build each day, and the errors people are running into. The 7 days in 30 seconds: create an agent in Bedrock, connect it to Lambda for real actions, add session memory, ground it with a knowledge base, attach guardrails, deploy and invoke from Python, then wire up CloudWatch logging for observability. The most common issues so far: model access not enabled, permissions between Lambda and Bedrock not set in both directions, Lambda timeouts too low, and trying to create a knowledge base as the root user. One update: Claude 3.7 Sonnet v1 has moved to legacy status. If you hit a ResourceNotFoundException, switch to Amazon Nova Pro in the Agent builder. It works with all 7 steps. We also covered where OpsAssistant goes next: multi-agent systems and Amazon Bedrock AgentCore for managed runtime, tool integration, memory, and observability at scale. The challenge is free on LinkedIn Learning until the end of June. Link in the comments. Share your progress with #7DayAWS.
Experience & Education
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Hydrolix Inc.
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Licenses & Certifications
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Google Cloud Professional Machine Learning Engineer
Google Cloud
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Publications
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NoSQL for Mere Mortals
Addison Wesley
See publicationNoSQL for Mere Mortals (Addison Wiley, 2015)
This book explains the advantages, use cases, and terminology associated with all four main categories of NoSQL databases: key-value, document, column family, and graph databases. For each, he introduces pragmatic best practices for building high-value applications. Through step-by-step examples, you’ll discover how to choose the right database for each task, and use it the right way.
Coverage includes
--Getting started: What…NoSQL for Mere Mortals (Addison Wiley, 2015)
This book explains the advantages, use cases, and terminology associated with all four main categories of NoSQL databases: key-value, document, column family, and graph databases. For each, he introduces pragmatic best practices for building high-value applications. Through step-by-step examples, you’ll discover how to choose the right database for each task, and use it the right way.
Coverage includes
--Getting started: What NoSQL databases are, how they differ from relational databases, when to use them, and when not to Data management principles and design criteria: Essential knowledge for creating any database solution, NoSQL or relational
--Key-value databases: Gaining more utility from data structures
--Document databases: Schemaless databases, normalization and denormalization, mutable documents, indexing, and design patterns
--Column family databases: Google’s BigTable design, table design, indexing, partitioning, and Big Data
Graph databases: Graph/network modeling, design tips, query methods, and traps to avoid
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A Developer's Guide to Reducing Your AWS Bill (The Cost of the Cloud)
PragTech Publishing
See publicationAmazon AWS offers a wide array of computing and storage options. Choosing among these options is challenging. Even apparently reasonable decisions can lead to unexpected consequences and costs.
Whether you are a designer, developer, system administrator, or IT manager, the tips presented here will help you tailor AWS to meet your needs while keeping your costs as low as possible.
The guide includes tips on:
• Choosing the right EC2 instance for your application
• When to…Amazon AWS offers a wide array of computing and storage options. Choosing among these options is challenging. Even apparently reasonable decisions can lead to unexpected consequences and costs.
Whether you are a designer, developer, system administrator, or IT manager, the tips presented here will help you tailor AWS to meet your needs while keeping your costs as low as possible.
The guide includes tips on:
• Choosing the right EC2 instance for your application
• When to use (or not use) spot instances
• How to calculate your cost savings with reserved instances
• Using auto-scaling to maximize the efficiency of your EC2 purchases
• Trade-offs between using an AWS service, such as CloudSearch, versus running your own
• Maximizing the benefits of S3 object versioning while keeping costs down
• Exporting large volumes of data without busting your budget -
Beyond Hadoop: Graph Databases for Big Data
Tom's IT Pro
See publicationHadoop and MapReduce are often used to solve big data problems but there are other data analysis models that lend themselves to big data.
Graph databases, for example, are well suited for problems that can be described in networks, such as social networks, workflows, transportation networks, and communication patterns. -
Data Science: It's More Than Tools
Tom's IT Pro
See publicationWhen we are working with big data we are actually working on a problem that generates big data. Good data scientists aren’t necessarily an expert in every big data tool out there but they are good at thinking about problems, abstracting away unimportant details, and identifying the key elements of a problem that lend themselves to providing the kind of insight you are looking for.
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PATRIC, the Bioinformatics Resource Center for bacterial data.
Nucleic Acids Research, 42 (D1): D581-D591.
See publicationAuthors:Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T, Gabbard JL, Gillespie JJ, Gough R, Hix D, Kenyon R, Machi D, Mao C, Nordberg EK, Olson Overbeek R, Pusch GD, Shukla M, Schulman J, Stevens RL, Sullivan DE, Vonstein V, Warren A, Will R, Wilson MJC, Yoo HS, Zhang C, Zhang Y, Sobral BW.
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Tackling cloud orchestration for complex IT workflows
SearchCloudComputing
See publicationBusiness processes comprise a range of applications and involve the coordination of multiple business units. In a cloud computing environment, this process, called orchestration, involves a few very critical factors. To design for orchestration in the private cloud, IT teams must manage server runtimes, direct the process flow among applications and deal with exceptions to typical workflows.
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Using MapReduce Programming without Java
Tom's IT Pro
See publicationThe MapReduce model of parallel processing lends itself to many kinds of problems. Although Java is commonly used for MapReduce programs, you don't have to be a Java guru to get the benefits of MapReduce on Hadoop.
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Data integration for dynamic and sustainable systems biology resources: challenges and lessons learned
Chemistry & Biodiversity
See publicationAuthors: Sullivan DE, Gabbard JL Jr, Shukla M, Sobral
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A CFO's five-point cloud deployment checklist
SearchCloudComputing
See publicationThe increasing use of cloud computing and the rapid changes in cloud options can present challenges for CFOs and other executives who will ultimately sign off on -- or reject -- a cloud deployment.
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The Definitive Guide to Cloud Computing
http://nexus.realtimepublishers.com/dgcc.php
See publicationThe Definitive Guide to Cloud Computing provides IT managers, system architects, and IT service consumers with the information they need to understand the advantages of cloud computing, the options for deploying cloud infrastructure, and the means to transition to cloud-based service delivery. This Definitive Guide provides a road map to implementation that starts with defining key characteristics of cloud computing and moves through identifying, planning, and implementing cloud service…
The Definitive Guide to Cloud Computing provides IT managers, system architects, and IT service consumers with the information they need to understand the advantages of cloud computing, the options for deploying cloud infrastructure, and the means to transition to cloud-based service delivery. This Definitive Guide provides a road map to implementation that starts with defining key characteristics of cloud computing and moves through identifying, planning, and implementing cloud service delivery. Other critical topics, such as governance, security, and growth are addressed as well.
Courses
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Bioethics
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Computational Biochemsitry
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Computational Systems Biology
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Computational modeling research
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Genomics
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Statistics
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Organizations
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American Association for the Advancement of Science (AAAS), ACM
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International Society for Infectious Diseases
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Randall Shane
Randall Shane
🚀 I help SMBs and teams stand up AI pilots in weeks, not months. From private document summarization to AWS-based GenAI assistants, I deliver scoped projects that prove ROI fast.<br><br>Currently building: Fox River AI — a platform that helps small and mid-sized businesses deploy tailored, private AI assistants built on their data. Our goal is to make GenAI actually useful to small and medium sized companies — combining RAG, agents, and ML pipelines in a fully-managed stack.<br><br>⸻<br><br>💡 About Me<br>I lead AI/ML strategy, architecture, and delivery — combining 20+ years of systems-level engineering with hands-on leadership in cloud, data science, and GenAI. My work spans regulated healthcare, finance, and government.<br><br>At Fox River AI, I designed and deployed a full-stack prediction engine using LSTM, CatBoost, and LLM-driven RAG pipelines — including contextual bandits and a LangChain-based assistant for querying predictive results. It runs in a fully containerized, CI/CD-managed architecture and supports memory, vector search, and agents.<br><br>Prior to that, I led clinical AI initiatives for academic medical centers and pharma, building HL7v2-to-FHIR pipelines and risk models for claims, SDOH, and heart disease. I’ve served as Principal Architect, VP of Engineering, and AI Advisor, building production ML systems on AWS and GCP.<br><br>Earlier, I served as a DoD Program Director and Army Officer (Captain), leading high-pressure medical and technical teams — a foundation that shaped my delivery-first mindset.<br><br>⸻<br><br>🎯 I’m currently focused on:<br>• Deploying custom AI assistants for SMBs<br>• Architecting private GenAI systems with RAG, LLMs, and memory<br>• Predictive modeling in healthcare, sports, and infrastructure<br>• Proving that AI can deliver business value without hype<br><br>👥 Open to:<br>• MVP pilot partners (SMBs, startups)<br>• Director / Head of AI roles<br>• Technical co-founder opportunities<br><br>Let’s connect if you’re exploring GenAI or need help turning your data into action.
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