Maria Vechtomova

Speeding up index creation Let's talk about speeding up index creation in PostgreSQL. If you've ever had the pleasure of waiting for hours while your database churns away at creating indexes on a large dataset, you know the feeling. Fortunately, Hans-Jürgen Schönig has some insights that may help you cut that time significantly. Indexes are crucial for optimal query performance. But creating them can feel like watching paint dry. Thankfully, there are techniques floating around that can make the process a bit less torturous. Speaking of insights, PGConf.dev 2024 had a lineup of talks worth tuning into if you missed it. Topics ranged from autovacuum mishaps to scaling databases at petabyte levels. Definitely a treasure trove for any PostgreSQL enthusiast. Also, if you fancy learning some magic tricks for psql, Elizabeth Christensen shares some nifty settings and presets that can make your life easier. So, whether you're indexing or attending conferences, the PostgreSQL community is here to keep you entertained and informed. Cheers to faster queries and smarter database management! Source: https://lnkd.in/e8SSrvJd

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My colleague Jeroen Overschie made me finally understand how you can feed relevant documents into a large language model to enhance its context and provide better information and how important a good, relevant search engine still is for this method. In short: if your search engine and the results it turns up are better, the quality of answers from the LLM will be better.

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in Pandas 2.2 one can set pyarrow as the default backend. in this presentation the advantages of arrow vs numpy are presented: 1. Faster string operations 2. Consistent missing value handling 3. Efficient data serialization Minute 24 https://lnkd.in/gbbGpR56

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Closing thoughts on PyData Eindhoven, data scientists have a very good understanding regarding good uses cases for Generative AI, and, as well, use cases that don't really fit Generative AI and are better served by other approaches. For those use cases, the discussion is about monitoring, quality control and management of AI applications. My talk was the first talk in the back-up list, however, none of the titular roster speakers needed to excuse him/her self, so it would be next year. For those wondering what my talk was to be about, check the following repo :) https://lnkd.in/ehwC7h4H

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We're only just scratching the surface of how you use vector databases. Bob van Luijt described what could be the next generation during our conversation in amsterdam. RAG, as originally defined https://lnkd.in/exMYbCJb was essentially about single instance knowledge retrieval. But what happens when what you want is computation on knowledge that results from a computation? Now that we have reasoning AI I agree with Ilya that fine-tuning is going to be a thing of the past and vector databases are going to become even more important. Watch the full episode to learn more about the potential for vector databases: https://lnkd.in/eC6CnVfb

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𝐓𝐡𝐞 𝐑𝐀𝐆 𝐃𝐢𝐥𝐞𝐦𝐦𝐚: 𝐆𝐫𝐞𝐚𝐭 𝐏𝐨𝐰𝐞𝐫, 𝐁𝐮𝐭 𝐚𝐭 𝐖𝐡𝐚𝐭 𝐂𝐨𝐬𝐭? 🤔 Retrieval-Augmented Generation (RAG) has unlocked incredible possibilities for language models, but it's not without its challenges. We're talking retrieval latency, errors, and complex system architectures. These issues can significantly impact performance and scalability. So, what's the solution? Enter 𝐂𝐚𝐜𝐡𝐞-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐂𝐀𝐆)! 💡 CAG offers a refreshing approach by leveraging the power of extended context windows. We preload relevant resources into the model's context and cache runtime parameters, eliminating the need for real-time retrieval. The benefits are clear: 1. 𝐅𝐚𝐬𝐭𝐞𝐫 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞: No more retrieval bottlenecks slowing you down. 2. 𝐌𝐨𝐫𝐞 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐑𝐞𝐬𝐮𝐥𝐭𝐬: Minimize retrieval errors and maintain context relevance. 3. 𝐒𝐢𝐦𝐩𝐥𝐞𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A streamlined, efficient design that's easier to manage. Of course, CAG has its limitations, particularly with massive datasets and extremely long contexts. But for many use cases, it offers a compelling alternative to RAG. Read more about it here :- https://lnkd.in/dhGbbu-f What are your thoughts on CAG vs. RAG? Share your insights in the comments! #AI #LLM #RAG #CAG #MachineLearning #Innovation #DeepLearning #NLP #ArtificialIntelligence

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When invited to the Super Data Science Podcast, Ritchie Vink, author of Polars, mentioned an upcoming Polars book set to launch in January 2025: "Python Polars: The Definitive Guide" by Jeroen Janssens and Thijs Nieuwdorp. The official Github repository already has a lot of code examples that worth checking out: https://lnkd.in/dJGbwQnM The Early Release version can also be read on O'Reilly Learning platform: https://lnkd.in/daTEf8Tv The book is offering guidance for you to:  - Process larger-than-memory datasets at record speed - Apply the eager, lazy, and streaming APIs of Polars and decide when to use which - Transition smoothly from Pandas or Spark to Polars - Integrate Polars into your existing codebase - Work with Arrow and Parquet to efficiently read and write data - Translate complex ETL tasks into efficient and elegant queries I started reading the Early Release version and I am enjoying it so far. I am looking forward to the final version of the book. #Python #DataFrameLibrary #Polars

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Mom! I'm on YouTube again. This time it's not my own uploaded 😅 PyCon Lithuania uploaded my session (https://lnkd.in/dY2szb_U you can also check out other talks here: https://lnkd.in/dWwxdcWc) Happy watching! ---- Stay in the loop for the latest insights from Uncle Data! * Click the notification bell icon 🔔 to receive updates on Linkedin * Subscribe to my Newsletter on Substack ✏ * Listen to Uncle Data Podcast ⌨️ Listen. Learn. Connect ---- #dataengineering #patterns #python #spark #iceberg

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Neural network activation functions

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Can LLM preference tuning degrade Multi-Agent system performance? In a recent tweet, Andrej Karpathy questioned why all LLMs sound similar, prompting Sebastian Rascha to speculate about the uniformity of alignment datasets. Aligning user preferences is a last-mile step in training large language models. Regardless of the pre-training datasets used, could the preference datasets used during alignment compromise the effectiveness of LLMs? This issue could adversely affect multi-agent systems incorporating multiple LLMs in their design.

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"Are we living in an AI bubble?"   As someone working in the data industry with a clear focus on AI, this question crosses my mind from time to time. Yesterday, a simple interaction at the supermarket provided a clear answer.   An elderly lady, unable to speak Dutch or English, effortlessly communicated with a cashier using her smartphone. She spoke in her native language, showed the screen to the employee, who then responded in Dutch. The phone translated both ways in real-time, facilitating a smooth conversation. Both seemed happy with the interaction they had just had, which wouldn't be possible without AI technology.   This everyday scene showcases how AI is quietly revolutionizing communication, even for those we might not consider early adopters. It's a powerful reminder that AI's impact extends far beyond tech companies and academia.   Have you noticed similar AI-powered interactions in unexpected places? Share your experiences below!

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Since I had some spare time during the weekends I put something together: https://lnkd.in/eyjqrzbn This is the start of a new journey where I work on my passion of applying modern ML/NLP/AI/GNN techniques for Source Code (and Binary) Vunerability detection and Reverse Engineering. It features a new site, that I personally built, and where I planning to publish my personal Research and Tools. #NLP, #BERT, #CodeBERT, #GraphCodeBERT, #Transformers, #Cybersecurity

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Automatically creating a knowledge graph with LLMs is hard - one issue is you end up with a lot of duplicates. Here’s a seriously cool Cypher snippet by Tomaz Bratanic and others at Neo4j that performs entity deduplication for you, using a combination of text embeddings and word distance.

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In context of debate if large context #LLM models will eventually negate the need for #RAG, an article from Databricks has some good insights - • Longer contexts boost RAG accuracy... to a point • Performance often peaks at 32k-64k tokens, then declines • Top models (GPT-4, Claude 3.5) maintain consistency at scale • Others show unique failure modes (e.g., copyright concerns, summarization instead of Q&A) • Optimal context size varies by model and task • Lack of long-context training data may explain some issues Key takeaway: Long context and RAG are synergistic, but LLMs need refinement to fully leverage extended contexts. https://lnkd.in/eehdKtCm

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🚀 New Challenger - China’s AI startup DeepSeek-Coder-V2 outperforms GPT4-Turbo in coding and math benchmarks! DeepSeek-Coder-V2 an open-source mixture of experts (MoE) code language model, scored 90.2% on the HumanEval benchmark and 76.2% on the MBPP benchmark, setting new state-of-the-art results in these coding tests. Maintains robust performance in general language tasks comparable to its predecessors and competing models. The model is available in two sizes/configurations: 16 billion and 236 billion parameters with total 338 supported programming languages. Context length with up to 128,000 tokens and good news is the model is fully open-sourced. https://lnkd.in/g-MXtpet #llm #generativeai #artificialintelligence

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