Loic Juillard

Chatbots spewing hate speech is a problem for engineers and security teams alike. Building safe and reliable AI systems is a problem that runs deep! Check out my latest blog to learn about how we got here and what you can do to deploy AI that you can trust. I’ve even drawn up some illustrations to keep you entertained on the way: 

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Fellow in-house taxonomists, ontologists, metadata managers, and semantic wizards: what is the name of the team or business unit that you work within? I have the good fortune of getting to rename the "Content Standards" program I lead at Kaiser Permanente IT. My team has determined that the name is too vague to convey the core activities of the program, and leads to confusion with the cross-functional teams we collaborate with. I'm looking for a clear name that will resonate with stakeholders within my org. So I've got a very classic taxonomist question: What to call the program? 🤔 For some context... What my program is responsible for: - Defining and governing standards for metadata, structured data, taxonomy, and ontology for classifying/structuring digital content - Implementing processes and platforms to good metadata hygiene - Assessment and remediation of content metadata What my program doesn't cover: - Writing or editorial style guides - Content design - Content strategy - Site maps or navigational structure - Data governance Any ideas are most welcome!

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CS Leaders based out of the SF Bay Area: Join us in Menlo Park next Friday (6/13) for our inaugural "Application of AI in CS" monthly luncheon. Our featured guest, Ashvin Vaidyanathan (VP of Customer Success and Insights at LinkedIn), will kick us off with how the LinkedIn CS team is embracing AI to increase customer value and efficiency. We will follow that with a roundtable discussion on how AI can help with your current priorities, how are you leveraging AI today, are there any adoption hurdles that you are running in to? https://lu.ma/g9u8mu8q

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#ConvergeSummit panel building on insights. Mary Rozenman discussed 4 key accelerators: - vast availability of data - novel advanced statistical models - greater computing capabilities - tremendous talent in the #biotech industry Abraham Heifets, Tim Luker, Scott Skellenger, and Thomas Miller suggesting that - cultivating data to focus and - building out reinforcement learning are also important in applying #AI in the drug development process.

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Generative AI is changing the data storage landscape as file sizes continue to grow. My latest piece for Fast Company Executive Board on why file sizes are growing and what organizations can do to manage them in a cloud environment. https://lnkd.in/gNDT3-Fh #GenerativeAI #Cloud #ContentManagement

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It is important to revisit concepts that might have eluded me. Take the Intuit Lacerte Software Development Kit (SDK). For the non-programmers, a Software Development Kit is instructions and tools for connecting to local data sources. Think of them as APIs but for accessing local data and hardware instead of cloud data. The first time I looked at the Lacerte SDK over a decade ago, I did not understand how it worked. The best I was able to do was to pull basic client information (names and address) for generating mailing labels. This was before my firm was doing digital delivery. As I started using Robot Process Automation (RPA) tools to input data into Lacerte, I learned about batch input. This let me gain a new understanding of the structure of Lacerte's data. Just last winter, I built some RPA tools to read organizer PDFs from Lacerte to build Document Requests List in SafeSend and Firm360. This let me gain a new understanding of the flow of Lacerte's data. Just a month ago, I connected with TruePrep AI to build tools for inputting data into Lacerte. My original plan was an RPA method since it was something I was familiar with. In that moment, I had decided to take another look at the Lacerte SDK. With an additional 10+ years of experience in understanding Lacerte's data structure and data flow, the SDK all makes sense. In my excitement of applying new knowledge, I can now generate those previous Document Request Lists more efficiently as well as add, update, and delete data from Lacerte. What does this mean? It means, custom import tools that can be catered to a firm's workflow like trial balances, depreciation, etc. It is always important to revisit concepts that have been previously elusive. We are the sum of our experiences and even a few months (from winter to spring) can change how we find solutions to problems.

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What’s Next: RunWhen's Kyle Forster talks open source and microservices SDxCentral CEO Matt Palmer speaks with RunWhen's Kyle Forster about a better way for companies to support gigantic code bases.

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Wondering about #PlatformEngineering hidden traps? I share some thoughts on the matter with health tech magazine. To me this reculumination of central SOA team with a twist. Your customer are internal developers and business users. DM if you would like to discuss more.

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I just wrote a blog titled "Millisecond Scale to Zero and the Quest to Never Pay for Idle Again", where we explain how at kraft.cloud we achieve super fast scale to 0 (and wake ups) so that you only pay for when your app is running -- not for your cloud infra's overheads. SIgn up now (closed beta) at kraft.cloud/signup . https://lnkd.in/d3TvZvpy #innovation #technology #entrepreneurship #startups

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I have been leveraging GCP for TrussHealth's AI and data storage, along with addressing customer data related issues in previous roles. Google's transparency in addressing and resolving recent data loss incidents instills confidence in their commitment to customer support and functional operational processes. Although data loss is a significant concern for enterprise storage, it is commendable to see cloud providers like Google openly acknowledge such challenges and demonstrate dedication to continuous improvement. Learn more about how Google Cloud responded to accidental data deletions: [Link to the article](https://lnkd.in/gTiRkg6X)

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pgvectorscale: A Game-Changer for Vector Similarity Search 🚀 Timescale's #pgvectorscale extension for PostgreSQL has achieved a remarkable milestone, matching the performance of Pinecone, a dedicated vector database, while being 75% more cost-effective. 💰 In their latest blog post, Timescale showcases the impressive speed and efficiency of pgvectorscale, which leverages the power of PostgreSQL to deliver lightning-fast vector similarity search. This breakthrough opens up new possibilities for applications like semantic search, recommendation systems, and more. 🔍 Key takeaways: 🌟 pgvectorscale now matches Pinecone's performance in vector similarity search 💸 pgvectorscale is 75% more cost-effective compared to Pinecone 🔧 Easy integration with existing PostgreSQL databases 🌐 Scalable solution for handling billions of vectors ⚡ Optimized for fast and efficient search results pgvectorscale's seamless integration with PostgreSQL allows developers to incorporate vector similarity search into their applications with ease, while benefiting from the robustness and scalability of a proven database system. 🗄️ If you're looking for a high-performance, cost-effective solution for vector similarity search, pgvectorscale is definitely worth exploring. 🔍💡 #pgvectorscale #postgresql #vectorsimilaritysearch #semanticsearch #recommendationsystems #timescale https://lnkd.in/gsfgCK32

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We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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Exactly one month ago, Intuit's Kalyan Kolachala (Director, Product Development), gave a Lightning Talk: Federated Search Over Distributed Observability Data at #ArgoCon | #KubeCon EU. In this talk, he gives insight into the use of technologies like, #OpenTelemetry, #ApacheArrow and #ApachePresto and how it can be used to improve speed, performance and significantly bring down Observability costs (for compute, networking and storage) and illustrates the same using a case study from Intuit. https://lnkd.in/gnv3vESJ #Intuit #IntuitOpenSource #OpenSourceCommunity #Observability #OpenSource #CloudNativeCon

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New post on what happens after you add a control plane and local storage for observability: you get 1000x the telemetry when it matters. https://lnkd.in/gAvkk-qP

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At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

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If you're running a data or AI-focused startup, connect with Stephan Goupille and his team! When three of the industry's brightest data minds join forces to back your venture, you get more than just funding – you gain invaluable operator expertise. They understand the challenges and opportunities you face because they've been there themselves.

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