Jeroen (Jay) ter Heerdt

Discover how a graph data model can help represent explicit relationships in data for use in #RAG and how that can be done with two popular #graph models: Property Graphs and RDF Knowledge Graphs, both natively supported by Oracle Graph. https://lnkd.in/eMYgZXKD

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NEW BLOG: ⚡ Confessions of a Spark Convert ⚡ The industry is moving on from compute-storage coupled architectures, are you? 🤔 https://lnkd.in/gWrr23Ci #spark #deltalake #fabric #databricks #lakehouse

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Great blog on Spark AQE!

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The new GPT4 turbo model with vision is available on Azure Open AI!

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Vectors and embeddings form the backbone of AI-powered applications, while high-quality data is essential for developing AI infused solutions. Well, similarity vector based search now coming to Azure SQL DB (currently in private preview). This will be important for those dev teams heavily invested in the SQL stack, but need to infuse AI in existing business apps. Read the blog here. https://lnkd.in/e-jPSVBk Request Access here: https://lnkd.in/eMrNgNZB Samples can be found here: https://lnkd.in/eedbraWR #azuresql #vectordb

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Exciting Updates from Azure AI! Microsoft has announced several new updates to Azure AI, including the introduction of Phi-3 fine-tuning and new generative AI models. These updates are designed to help organizations customize and scale their AI applications more effectively. Key highlights include: • Serverless fine-tuning for Phi-3-mini and Phi-3-medium models, allowing developers to customize models for cloud and edge scenarios without needing additional compute resources. • Improvements to Phi-3-mini, enhancing core quality, instruction-following, and structured output. • New model additions from OpenAI, Meta, and Mistral, providing greater choice and flexibility for developers. These advancements aim to empower developers to create more tailored and high-performing AI solutions #AzureAI

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IMHO, it's vital to move beyond talking about #GenAI risks. Without demonstrated ability to #Measure, #Trace, & #Monitor them with a #DataDriven approach, the real risk is #Business #Obsolescence! Check out this insightful article on the latest GenAI simulation & Evaluation tools available in #Azure #AI Studio: https://lnkd.in/gC7yfY8a

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📅 *Today* - Tuesday, October 22nd, 2024 11:00 AM PT [▶][▶] 👨‍💻👨‍💻Join Abhishek Narain and Wilson Lee for "Unlocking AI at Scale with Data Factory in Microsoft Fabric"! 📊 Efficiently managing high-volume, complex data is critical for building scalable AI solutions. Data Factory in Microsoft Fabric streamlines the process of ingesting, transforming, and orchestrating data, making it easier to prepare and integrate diverse data sources for AI applications 🔬Join this session to explore how Data Factory can help automate data workflows, enabling seamless data preparation for AI models. Learn how to design scalable data pipelines, automate data transformation, and optimize data integration, all within the Microsoft Fabric ecosystem. This is your chance to unlock the full potential of AI using Data Factory!. 🚀🔥Don’t miss this opportunity to learn and do more with AI in Microsoft Fabric. Register now for the Microsoft Fabric & AI Learning Hackathon and livestream series! Check out the "Microsoft Fabric & AI Learning Hackathon" on Devpost: https://lnkd.in/gwSAyWhE See the full "Microsoft Fabric & AI Learning Hackathon" livestream schedule on the MS Reactor: https://aka.ms/fabrichack Watch today's livestream event on-demand on YouTube: https://lnkd.in/gbN9qFTr

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Newly published in Google Cloud's Architecture Framework 🏗️ this new AI/ML perspective provides a roadmap for designing, building, and managing workloads on Google Cloud. 🤖 It covers best practices for achieving operational excellence, security, reliability, cost optimization, and performance optimization. 🎯 Who should read this? 🤔 Decision-makers 👔 Architects 📐 Administrators 💻 Developers 🧑‍💻 Operators 🛠️ Key takeaways: Learn how to design and build AI/ML workloads that meet your business goals. 💼 Understand the principles of operational excellence, security, reliability, cost optimization, and performance optimization in the context of AI/ML. 💡 Get practical recommendations for implementing these principles on Google Cloud. ☁️ #AI #ML #GoogleCloud #CloudArchitecture #OperationalExcellence #Security #Reliability #CostOptimization #PerformanceOptimization

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Google Cloud #Research innovates with ... #BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse. 🥇⚡🔥 Check out our Research Paper here, to be presented at next month's #SIGMOD2024 event: https://lnkd.in/dQJrZPpM Innovations include: → #BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg and Delta) first class citizens → Fine-grained #governance enforcement and performance acceleration for BigQuery and other open-source analytics engines → Design and implementation of BigLake #Object tables to integrate AI/ML for inferencing and processing over unstructured data → #Omni, a platform for deploying BigQuery on non-GCP clouds, enabling an enterprise lakehouse across cloud providers

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Dotadda has spoken AI: Hi there! I can answer any questions related to MSFT • Q4 2024 Earnings Call • 2024-07-30.Me: Talk about azure growth problemsAI:Searching... Azure growth problems in Microsoft Q4 2024 Earnings CallAzure's growth, while still robust, has encountered several challenges as highlighted in Microsoft's Q4 2024 Earnings Call:Capacity Constraints: Azure has faced persistent capacity constraints, particularly in AI services where demand has exceeded available capacity. This issue is expected to alleviate in the second half as capital investments increase available AI capacity to meet growing demand.Economic Factors: Modest softness in Europe, attributed more to economic conditions than Azure-specific issues, has impacted growth. This suggests that external economic factors can influence Azure's performance in specific regions.Sales Mix Shift: There's been a sales mix shift towards Azure, which has affected gross margins despite improvements in Azure's performance. This shift indicates a changing composition of revenue sources within Microsoft's portfolio.Capital Expenditures (CapEx): To meet the growing demand for AI and cloud products, Microsoft plans to scale its infrastructure investments. This includes significant capital expenditures expected to be higher than FY 2024. The focus is on managing these expenditures based on demand signals and adoption rates of services.Investment in Data Centers and Technology: Microsoft is expanding its data center footprint across four continents and investing in new technologies like AI accelerators from AMD and NVIDIA, as well as their own first-party silicon Azure Maya. These long-term assets are aimed at driving growth for the next decade and beyond.Partnerships for AI Services: In response to cloud capacity and AI service demands exceeding current capabilities, Microsoft is entering into partnerships with companies like Oracle and CorWee. These collaborations are part of both near-term and long-term strategies to enhance AI service offerings.These challenges underscore the complexity of scaling a global cloud infrastructure amidst rapidly increasing demand for cloud and AI services. Addressing these issues effectively requires a balance between aggressive investment in infrastructure and careful management of capital expenditures to ensure sustainable growth.

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With so many LLM options, how do we pick the best one for a particular use case? Check out Nick Brady blog which shows a way to simplify the process of comparing different model outputs

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This is awesome! A lot more for a less…love it 🙌🏽 Companies using RAG can now leverage these updates to make their data search smarter and faster, without breaking the bank. 🚀 Imagine quicker answers to complex queries and more room for your data—all while keeping costs down. 📉 It’s all about doing more with less and growing smartly. #Azure #AISearch #RAG

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Transforming Real-Time signals into Real-Time, in addition to latent, business value has always been the most complex goal in #Data, #Analytics, & #AI fields. Amazing to see Real-Time Intelligence in #MicrosoftFabric announced at #MSBuild https://lnkd.in/gV5tr_N7 Read more about it here: https://lnkd.in/gheHgE9a

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Just in time for our Global Modeling Webinar tomorrow, 9 a.m. PST! Learn how collaboration will fast track your modeling practices🌎 Link in comments! Featuring: T.y. Li 🌟 #sqldbm #datamodeling #cloud #snowflake

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The latest Azure OpenAI Service announcements from #MSBuild.

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Very interesting. Azure OpenAI supports batch processing, which can decrease costs by 50%. For everyone who is cost-conscious, I would recommend giving this a quick read. #genai #azure

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It gives us great pleasure to announce that Apache Spark in BigQuery is now officially available in Private Preview. This feature expands BigQuery’s processing capabilities by offering Apache Spark as an OSS processing engine under a unified control plane (admin, monitoring, billing, security, governance). It is the first step in delivering on the Unified Data Platform vision and was a key announcement at Next ‘24 #googlecloud #bigquery #spark

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**Exciting News 🚀** We're thrilled to announce that Mosaic AI Model Training's support for fine-tuning GenAI models is now available in Public Preview 🤩 **Unlock the Power of Your Enterprise Data 🔓** At Databricks, we believe that connecting the intelligence in general-purpose LLMs to your enterprise data is the key to building high-quality GenAI systems. With Mosaic AI Model Training, you can fine-tune a wide range of models, including Llama 3, Mistral, DBRX, and more, with your enterprise data. This results in models that are specialized for specific tasks, business contexts, or domain knowledge. **Benefits of Fine-Tuning 📈** Our customers have seen significant benefits from fine-tuning, including: * **Higher quality**: Improve model quality along with specific tasks and capabilities * **Lower latency at lower costs**: Fine-tuning small models can dramatically reduce latency and cost while maintaining quality * **Consistent, structured formatting or style**: Generate outputs that follow a specific format or style * **Lightweight, manageable system prompts**: Integrate business logic or user feedback into the model itself * **Expand the knowledge base**: Extend a model's knowledge base with Continued Pretraining **Real-World Success Stories 📊** Don't just take our word for it Our customers have achieved amazing results with Mosaic AI Model Training: * Experian reduced average training time and accelerated their GenAI development cycle * First American automated tedious manual tasks and exceeded accuracy goals * Celebal Tech built a high-quality domain-specific RAG system with fine-tuning and RAG **Get Started Today 🚀** Try Mosaic AI Model Training today and discover the power of fine-tuning for yourself. With our simple, fast, and integrated solution, you can: * Select your base model and training dataset * Start training immediately * Deploy your model with Mosaic AI Model Serving in just one click **Learn More 📚** Watch our Data and AI Summit presentation, read our documentation, and visit our pricing page to learn more about Mosaic AI Model Training. #MosaicAIModelTraining #FineTuning #GenAI #Databricks #AI #MachineLearning #EnterpriseData #DataScience #ArtificialIntelligence #Innovation #Technology #BusinessSuccess #CustomerSuccess #AICommunity #DataCommunity #DatabricksCommunity

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