Ravindra K. B.

A cool use of the “contribution analysis” ML model to answer the “why” kind of business questions. Combined with Gen AI, it enables quick insights for any enterprise.

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Customers with existing data and documents currently managed on #AWS can now easily set up a #RAG application using SearchBlox. Setting up a RAG solution for documents stored within S3 buckets or filesystems, etc., requires developers to go through multiple steps: #Data crawling and ingestion, processing the suitable chunks for embedding and storing within a vector database, converting the user query into a vector search against stored chunks and then retrieving the most relevant chunks of information for augmenting a personalized response with a large language model (#LLM). https://lnkd.in/esjaGgWA

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This is a much easier way to get and load the ONYX model for generating 23AI Vector Embeddings. https://lnkd.in/gMgKPNgC

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These 10 capabilities can be found in the modern data lake reference architecture, along with vendor tools and libraries for each one. https://lnkd.in/gD5PQ8mE #DevOps #GenerativeAI #LargeLanguageModels by Keith Pijanowski thanks to MinIO

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Gemini in BigQuery changes the game for data engineers and analysts! More productivity ✔️ Improved query performance ✔️ Optimized costs throughout the analytics lifecycle—from ingestion and pipeline creation to deriving insights with AI ✔️ #GoogleCloudNext

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5 Tips for Kickstarting Your AWS Generative AI Strategy 1. Define Clear Objectives: Establish specific goals for your Generative AI initiatives. Whether it’s enhancing customer service, streamlining content creation, or optimizing operations, having clear objectives will guide your strategy and measure success. 2. Leverage AWS Resources: Utilize AWS’s comprehensive suite of AI and machine learning services, such as Amazon SageMaker, AWS Lambda, and AWS AI services. These tools can help you build, train, and deploy your generative models efficiently. 3. Focus on Data Quality: Ensure your data is clean, relevant, and comprehensive. High-quality data is crucial for training effective generative models. Consider using AWS data lakes and data analytics services to manage and preprocess your data. 4. Embrace a Collaborative Approach: Involve cross-functional teams in your Generative AI projects. Collaboration between data scientists, engineers, and domain experts can provide diverse perspectives and improve the outcomes of your AI initiatives. 5. Invest in Continuous Learning: Generative AI is an evolving field. Encourage your team to stay updated with the latest trends and advancements. AWS offers training programs and certifications that can help your team enhance their skills and stay ahead of the curve.

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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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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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In the rapidly evolving landscape of database management systems (DBMS), one constant has emerged: despite the introduction of numerous new technologies and data models, relational database management systems (RDBMS) have maintained their dominance. From the early hierarchical and network models to the rise of NoSQL and Columnar databases, each innovation has not replaced but rather contributed to the evolution of RDBMS. This article explores the progression of various database technologies and examines why RDBMS continues to be the backbone of data management. EQengineered Author: Ranjan Bhattacharya, Chief Data Officer. #databasemanagement #dataengineering #CIOs #CTOs #CDOs #CDAOs #CMOs #CFOs #CEOs #techinsights #EQengineered

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Databricks teamed up with AWS to power customized, production-quality #GenAI applications through AWS Trainium AI chips! The collaboration builds on Databricks’ newly expanded #MosaicAI capabilities — including Mosaic AI Model Serving, which supports a wide variety of model providers. Together, Databricks + Amazon Web Services (AWS) will make it possible for thousands of joint customers to build, deploy and monitor high-quality, customized #AI applications without sacrificing control over their data or IP.

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Anthropic has open-sourced the Model Context Protocol (MCP), a standardized interface enabling AI assistants to securely access diverse data sources. MCP streamlines integration, replacing custom connections with a single protocol for improved efficiency and scalability. The initiative includes SDKs, pre-built servers for common platforms (like Google Drive and Slack), and an open-source repository. Early adopters are already leveraging MCP to enhance their AI tools, demonstrating its potential to improve context awareness and functionality. This open-source approach fosters collaboration and accelerates the development of context-aware AI. The goal is to create a more connected and sustainable AI ecosystem.

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LakeFlow + Delta Live Tables + Serverless Autoscaling Compute sound like a perfect solution to load all required data with data expectations / monitoring / alerting to the Lakehouse :)

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Just out! Chapter 2.3, Domain modeling, explores how you can defend against poorly thought out, poorly understood architectures. In this chapter of the playbook, we walk through applying domain modeling to our model use case. https://bit.ly/3z8Sc9y #modeling #DDD

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Learn how to leverage vector search in vCore-based #AzureCosmosDB for #MongoDB for efficient data retrieval and improved performance: https://lnkd.in/guxWj4rr ⚙️💡

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This is interesting! Now, PostgreSQL functions (and procedures) can be written in something called PRQL: see https://prql-lang.org/ PRQL is some kind of modern declarative data language that compiles directly to SQL. Here's how that looks like in PostgreSQL: https://lnkd.in/d5UXQwsw create function match_stats(int) returns table(player text, kd_ratio float) as $$ from matches filter match_id == $1 group player ( aggregate { total_kills = sum kills, total_deaths = sum deaths } ) filter total_deaths > 0 derive kd_ratio = total_kills / total_deaths select { player, kd_ratio } $$ language plprql; Note that select part comes last, and from first, probably to utilize IDE capabilities batter. In any case the list of languages that can be used to write PostgreSQL functions (and procedures) is as follows: - SQL - plpgSQL (PostgreSQL procedural SQL) - This new PRQL thing I just mentioned. - C and C++ - JavaScript - Python - Rust - C# - GO (I think, probably) And that is only that I'm aware of, probably even more. And then you can use NpgsqlRest client (download here: https://lnkd.in/d3UqP_Sq) to turn those functions (and procedures) into REST endpoint with one command. I'm just going to say it: PostgreSQL functions (and procedures) are what microservices were meant to be.

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