Banias Baabe

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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🔹 𝗟𝗟𝗠 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 𝘄𝗶𝘁𝗵 𝗔𝘇𝘂𝗿𝗲’𝘀 𝗕𝗮𝘁𝗰𝗵 𝗔𝗣𝗜 🔹 Every AI prompt comes with a price tag, especially when working with vast datasets. Azure’s Batch API brings a game-changing approach for companies tackling large, non-urgent AI tasks at a fraction of the cost. 🚀 In our latest project, we used the Batch API for entity extraction from PDFs—a perfect fit for token-heavy operations like document analysis and large-scale information extraction.. ✅ 𝗧𝗵𝗲 𝗪𝗶𝗻𝘀: • Significant cost reduction: Batch jobs are 50% cheaper than real-time processing. • API architecture that scales effortlessly with document load, delivering results in a manageable 24-hour window. • Robust error management: Each task’s status is logged separately, ensuring seamless job handling. ⚠️ 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: • Extended processing time (up to 24hrs) may limit agility in development. • Azure’s current lack of embedding support narrows some RAG (Retrieval-Augmented Generation) use cases. Azure’s Batch API is growing fast, and for teams aiming to balance cost with capability, it’s a powerful tool to keep in your AI toolkit. Curious? Dive into our full experience and learnings in our blog post to see how we’re optimizing AI deployments. 💡 Link to Blogpost (German only): https://lnkd.in/gyDKEAF9  #LLM #BatchProcessing #AICostSavings #AzureAI #AzureOpenAI #OpenAI

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🚨𝐆𝐏𝐓-5 / 𝐎𝐩𝐞𝐧𝐀𝐈 𝐨1 is out now You have to admit this one was good ... Latest OpenAI model has some good insights. Kind of seems like a very complex system around Chain-of-thought, more than a model itself, but still ... 𝐒𝐡𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐭𝐡𝐨𝐮𝐠𝐡𝐭𝐬 ‼️ #AI #GPT5 #OpenAI

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𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗶𝗺𝗲𝗚𝗣𝗧 📈 Nixtla has introduced the first foundation transformer model specifically designed for time series forecasting! TimeGPT was trained on over 100 billion data points, including time series from domains like finance, energy, web traffic, sales and more. As a foundation model, TimeGPT can generate accurate predictions for datasets not seen in training, making it a valuable tool. Furthermore, TimeGPT can be accessed via a Python library or the associated API created by Nixtla. Check the links for more information, and make sure to follow me for regular content! 𝗧𝗶𝗺𝗲𝗚𝗣𝗧 𝗤𝘂𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁: https://lnkd.in/d6-3qJV6 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗧𝗦 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝗖𝗮𝗿𝗲𝘁📚: https://lnkd.in/dyByK4F #datascience #python #machinelearning #deeplearning #linkedin

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🔍 **Data Versioning in MLOps Pipelines: A Crucial Step for Reliable Machine Learning** In the evolving landscape of Machine Learning Operations (MLOps), **data versioning** stands out as a fundamental practice. It’s not just about tracking code changes anymore; ensuring data consistency and reproducibility is equally critical. Here’s why data versioning is indispensable in MLOps pipelines: 1. **Reproducibility**: It ensures that models can be trained and validated on the same data across different environments and timelines, making experiments reliable and results comparable. 2. **Traceability**: Keeping track of data versions helps in tracing back issues and understanding the evolution of datasets, which is crucial for debugging and auditing. 3. **Collaboration**: In a team setting, data versioning allows multiple data scientists to work on different versions of the data without conflicts, facilitating seamless collaboration. 4. **Regulatory Compliance**: For industries bound by regulatory requirements, maintaining data versions is essential for compliance and audit trails. 5. **Improved Model Performance**: By versioning data, teams can analyze how changes in data affect model performance, enabling better model tuning and optimization. Implementing data versioning can be achieved through tools like DVC, Delta Lake, or even integrating version control systems like Git with data storage solutions. Investing in robust data versioning practices not only enhances the reliability of your ML models but also ensures a smoother and more collaborative workflow. 🌟 Embrace data versioning in your MLOps pipelines to unlock the full potential of your machine learning projects! #MLOps #DataScience #MachineLearning #DataVersioning #AI

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arXiv -> alphaXiv alphaXiv is an open research discussion directly on top of arXiv alphaXiv is built by Stanford University students.

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