Precisely’s Post

Don’t miss this session at Amazon Web Services (AWS) #ReInvent2024 if you want to learn about how #generativeAI is transforming mainframe modernization. Zhe (Maggie) Li Arianna Valentini-Huffield Jeff Littlejohn John de Saint Phalle Fernanda Tavares Jaime Anaya #AI #cloudmodernization #partnership #hybridcloud #dataintegration #dataintegrity

Planning to attend #AWS reInvent 2024? Check out our data workshop to learn how you can partner with Precisely to stream mainframe data to AWS in real-time. #reInvent2024

In this video, Lara Rachidi and I covered the latest Databricks updates in the machine learning and GenAI space from the past month. Here are the key highlights: - Llama 3 is now available on Databricks, deployable via a managed API. Explore Llama 3 and learn how to use it in databricks. - Deep dive into DBRX’s architecture, gain practical tips on interacting with the model, and discover effective prompt strategies. - Exciting news: Ray has joined the Databricks ML Runtime! Learn about Ray, its functionality, and when to leverage it. Stay updated on the latest developments in #machinelearning #genai #llm #Databricks #CloudComputing #Azure #AWSDatabricks

#Couchbase releases AI vector search and Capella Columnar on AWS - via Blocks and Files

A dive into the Databricks vector search feature, now in public preview. #vectordatabase #vectorsearch #ai #llm #bigdata #bigdataanalytics #vectordb #machinelearning #deeplearning #databricks #rag #retrievalaugmentedgeneration

🦙 Llama 3.2 on Databricks! 🧱 Faster and now multi-modal, with: 🦙 Smaller models for lower latency + cost - on local devices! 🦙 Larger multimodal models for visual understanding Available now wherever you get your LLMs (Databricks)

#ExperienceTheNextGeneration #AmazonAurora #AmazonBedrock Attention, GenAI enthusiasts! Brace yourselves for an exciting episode of AWS OnAir that will revolutionize the way you leverage your data for generative AI applications. #StayTuned In this insightful session, AWS experts will unveil the power of using Amazon Aurora as a Knowledge Base for Amazon Bedrock, the cutting-edge Retrieval-Augmented Generation (RAG) framework. #GameChanger Imagine seamlessly integrating your document data into an Aurora database, creating an effortless pipeline for fuelling your generative AI applications. #StreamlinedProcesses With this innovative approach, you'll unlock the ability to build intelligent, data-driven applications that harness the full potential of your existing information. #DataDrivenInnovation Don't miss this opportunity to gain valuable insights and stay ahead of the curve in the rapidly evolving world of generative AI. #StayAhead

Check out our new Graviton support for Databricks ML Runtime (esp. w/Photon) for faster Machine Learning on Databricks!

Transitioning to Real-Time machine learning? Follow these tips 🚀 Making the leap to real-time machine learning inference? Here are 5 key steps to smoother sailing: 🚢 Assess use cases - Does your app truly require instant predictions? Lightning speed isn't always necessary. ⚡️ Optimize ruthlessly - Simplify models and prioritize efficiency over complexity for swift outputs. 🏃♂️ Leverage stream processing - Platforms like Kafka enable concurrent data handling for speed. 👓 Prioritize data quality - With instant inference comes instant preprocessing. Plan for the unexpected. 📈 Monitor tirelessly - Track latency, throughput, drift, errors. Establish alerts for rapid incident response. With the right preparation, migrating to real-time can be achievable. Utilize these steps to reach seamless integration. Check out the full guide for more insights: https://lnkd.in/d7MibWMK #MachineLearning #MLOps #DataScience