Yash Datta

Learn how you can convert a PyTorch model, trained on the web, to a mobile-optimized format. Madhur Zanwar describes the process using a few lines of code. He also discusses his journey and how one approach didn't yield satisfactory results while trying to run it in a React Native app. #machinelearning #pytorch #computervision #edgeml #reactnative #objectdetection #yolov5

14

Are you a PostgreSQL enthusiast and professional looking for advanced solutions in edge computing? If your answer was "yes," then this Webinar is for you! Join us to explore how pgEdge distributed PostgreSQL can elevate your Postgres database deployment to meet the challenges of today’s globally dispersed, latency-sensitive applications. Learn more and register today! https://hubs.ly/Q02tWYKr0 #pgedge #postgresql #dzone #postgresqlprofessionals

4

Are you a PostgreSQL enthusiast and professional looking for advanced solutions in edge computing? If your answer was "yes," then this Webinar is for you! Join us to explore how pgEdge distributed PostgreSQL can elevate your Postgres database deployment to meet the challenges of today’s globally dispersed, latency-sensitive applications. Learn more and register today! https://hubs.la/Q02vkWpl0 #pgedge #postgresql #dzone #postgresqlprofessionals

8

Today, Nexla joins the Connect with Confluent partner program! Start building Kafka streaming flows today with no-code, and use Nexla to connect any real-time or batch data in any format to Confluent streaming applications. Learn more about our integration with Confluent: https://lnkd.in/gnBvPSrG

25

2 Comments

Learn how to build a neural network in TensorFlow — just follow along Shreya Rao's step-by-step guide (with a bonus section for anyone interested in a PyTorch implementation as well).

37

Curious about the role of Generative AI in data engineering? Check out our recent blog by Gunasekaran S, the director of data engineering at Sigmoid, where we explore the latest advancements and use cases of Generative AI in data engineering. Learn how Generative AI can help data engineers streamline processes, enhance data quality, and fuel innovation like never before. Click here to read the full blog - https://lnkd.in/gh-aQFiS #artificialintelligence #generativeai #dataengineering #sigmoid

23

Is #Redis Data Persistence Useful in Practice? Redis is an in-memory database. If the server shuts down, the data will be lost. There are two ways to save Redis data to disk: - AOF (Append-Only File) - RDB (Redis Database) 𝗔𝗢𝗙 Unlike a write-ahead log, the Redis AOF log writes after commands run. Redis runs commands to change the data in memory first. Then it writes the commands to the log file. AOF logs the commands instead of the data. This simpler design makes recovering data easier. Additionally, AOF records commands after they have executed in memory. So it does not block current write operations. 𝗥𝗗𝗕 The limitation of AOF is that it saves commands instead of data. When we use the AOF log to recover data, the whole log must be scanned. When the log size is large, Redis takes longer to recover. So Redis offers another way to save data - RDB. RDB records snapshots of data at certain times. When the server needs recovery, the data snapshot can load directly into memory for fast recovery. Step 1: The main thread forks the "bgsave" sub-process, which shares all the in-memory data. "bgsave" reads the data from the main thread and writes it to the RDB file. Steps 2 and 3: If the main thread changes data, a copy is created. Steps 4 and 5: The main thread then operates on the copy. Meanwhile, the "bgsave" sub-process continues writing data to the RDB file. Source: ByteByteGo

1

🗣️ Submit to speak at PyTorch Conference in San Francisco September 18-19! We're looking for talks on the cutting-edge open source machine learning framework, deep learning, chatbots, security & MORE. We're looking for experts on: 🔥 PyTorch Framework & Ecosystem 🔥 Deep Learning Applications 🔥 Software Applications 🔥 Training & Inference 🔥 Model Lifecycle & Management 🔥 Ethical, Social, & Regulatory Aspects 🔥 AI in Industries ✏️ See alll suggested topics and submit your talk by June 7: https://hubs.la/Q02ttHzl0. 🎟️ Register by April 26 & save US$350: https://hubs.la/Q02ttGHq0. #PyTorchConf #PyTorch #PyTorchFoundation #AI #MachineLearning #ML #DeepLearning #OpenSource #OpenSourceSoftware #OpenSourceDevelopment #OpenSourceCommunity #OSS #LinuxFoundation #events #linux #software

108

1 Comment

🗣️ Submit to speak at PyTorch Conference in San Francisco September 18-19! We're looking for talks on the cutting-edge open source machine learning framework, deep learning, chatbots, security & MORE. We're looking for experts on: 🔥 PyTorch Framework & Ecosystem 🔥 Deep Learning Applications 🔥 Software Applications 🔥 Training & Inference 🔥 Model Lifecycle & Management 🔥 Ethical, Social, & Regulatory Aspects 🔥 AI in Industries ✏️ See alll suggested topics and submit your talk by June 7: https://hubs.la/Q02xs2j40. 🎟️ Register by July 12 & save US$250: https://hubs.la/Q02xs9zC0. #PyTorchConf #PyTorch #PyTorchFoundation #AI #MachineLearning #ML #DeepLearning #OpenSource #OpenSourceSoftware #OpenSourceDevelopment #OpenSourceCommunity #OSS #LinuxFoundation #events #linux #software

27

Finding the Sweet Spot 🍩: Basic RAG vs. Reranker-Enhanced RAG When building a Retrieval-Augmented Generation (RAG) system, you have two main options: basic RAG, which relies on vector search alone, or reranker-enhanced RAG, which incorporates a reranker to refine search results. Reranker-enhanced RAG offers superior answer accuracy compared to basic RAG but comes at a higher cost in terms of latency and computational resources. However, both approaches are more cost-effective than relying solely on Large Language Models (LLMs). The key is to find the sweet spot that aligns with your application's requirements and business objectives. Consider factors such as the desired level of answer accuracy, acceptable latency, and cost constraints when deciding on the best RAG architecture for your use case. Explore the trade-offs between basic RAG and reranker-enhanced RAG and make informed decisions to optimize your RAG system's performance and cost-efficiency. 🎯📈 https://lnkd.in/gxwGxdB7 #RetrievalAugmentedGeneration #Rerankers #VectorSearch #CostOptimization #PerformanceOptimization

3

Unlocking Insights 📊 with Python! In this two-part series, Ong Yi Shen, our intern from NTU, shares some nifty tricks to clean, analyse, and report data from CSV files. Click the link below to learn more! #DataScience #PythonProgramming https://lnkd.in/gn2TUgRy

1

Join PyTorch at Snowflake's #DataCloudDevDay on June 6 in San Francisco to learn how to accelerate GenAI performance with Native PyTorch. Register: https://hubs.la/Q02xQStt0

18

Thank you, Sion Smith, for having us! 🎙 In this talk, Yingjun Wu introduces RisingWave, a scalable streaming database that utilizes #S3 as its primary storage. From underlying technology to real-world scenarios, Yingjun illustrates how RisingWave supports heavy traffic workloads in enterprise-grade production environments. #risingwave #streamingdatabase

3

When *open* isn't *open source* 🙋🏼‍♂️👀 Reflecting on the open-source landscape for Large Language Models, I explore the essentials: permissive licensing, transparency in training data and model architecture & more #LLM #LLMs #AI #GenerativeAI #opensource https://lnkd.in/gSJuK_J4

1

Retrieval-augmented generation (RAG) helps address these issues, but retrieval accuracy is a major bottleneck for end-to-end performance. #VectorDatabase #AIEngineering #LargeLanguageModels by Linpeng Tang thanks to MyScale

3

DataStax ➕ Langflow make RAG 100x easier. Learn how to get started in this new video from Tejas Kumar. 🌟 Be sure to star our repo on Github! https://dtsx.io/social #RAGApplications #Langflow https://dtsx.io/3UnB7Q2

11