Elisa Song

🔹 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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𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗨𝗻𝘀𝗼𝘃𝗮𝗯𝗹𝗲: Leveraging LLMs for Real-World Problems 💡 Before LLMs, automation mainly dealt with structured digital data, unless you invested heavily in specialized models, which still left many areas untouched. Structured (tabular) digital data was essentially the cornerstone of IT. Then came LLMs. They impressed me with their ability to understand and contextualize virtually any data within their training scope. This capability allows us to extract structure from unstructured data, interpret scenarios, and translate artefacts like never before. To harness this potential for maximum value, consider these points: 🤔 What is unstructured data? Any data request not yet translated into code is unstructured as much as more typically identified irregular invoicing formats. The mind-stretch of what is unstructured is key to ideation with LLMs. 🔎 Identify human-driven, sequential, and frequent processes that involve interpretation and restructuring. Rather than asking for “structured digital” (like we did with RPA), we should focus on “sequential and frequent” processes. 🧐 Recognize data and artefacts we currently lack insights into and determine which might be covered by LLM training data (and thus workable by them). At Basico we tackle real-world problems using these points of enquiry. The best part? We solve the problems swiftly, because we work with pretrained models that are easy to configure. By combining these models with our automation experience and best practices, we expand the realm of digital and IT. This expansion marks a genuine, immediate paradigm shift in IT and automation💡 #ai #llm #genai #technology #rpa #automation 🤝 #Connect or #Follow for a blend of serious insights and lighthearted humor about everything IT and business 🎧 #Listen to The Only Constant podcast

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🚀 Exciting News: Open-Sourcing a Game-Changing Keras Data Preprocessing Tool! 🐍📊 After years of closed development, I'm thrilled to announce the release of my TensorFlow Keras-based data preprocessing package (KDP) v1.8.0 (finally had some time to release it). This tool streamlines ML workflows, making data preparation more efficient and scalable. Key features: • Built on TensorFlow Keras • Innovative use of Keras preprocessing layers • Suitable for both beginners and seasoned data scientists • Integrates directly as first layers of your Keras models or as a separate one ! You can read some more here: https://lnkd.in/gRY2AFGe I'm open-sourcing this project to empower the community. Your contributions can help take it to the next level! Check it out and let's revolutionize data preprocessing together! 💡 #DataScience #MachineLearning #OpenSource #Python #TensorFlow

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A virtual coffee and my personal rediscovery of graph neural networks! Last week, I had the pleasure of catching up with Christoph Boehm over a virtual coffee. Christoph and I, we both completed our PhDs together in Würzburg and hadn’t met for quite some time. So there was a lot to discuss... Among several topics, there was one that has occupied my thoughts for the past few days. Graph Neural Networks. A field which I now view with fresh eyes! Thank you Christoph Boehm for your insights and your inspirations! Many use cases seem to benefit greatly from graph-based approaches, often acting as the missing piece of the puzzle. Even when focusing on forecasting use cases, e.g. 👉 Predictive maintenance: Detecting anomalies and critical process behavior across a production workflow with complex dynamics and dependencies. 👉 Traffic management: Optimizing routing by traffic prediction Transport & Logistics: Estimating time of arrival and quantifying risks along the transportation process. 👉 Environmental analysis: Analyzing environmental data collected from sensors distributed across geographical regions to predict phenomena like air quality, weather patterns, or natural disaster risks. Right now, I am refreshing some basics with a super script on graph theory! 😃 Happy to further exchange ideas and best practices from real-world use cases! #GraphNeuralNetworks #Inspiration

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Hello data-bubble in Berlin, #nerdalert my partners in crime Arash and David are 2 of the smartest people I know in this whole genAI field, and if they are concerned about the robustness of large deep neural networks - I am, too. So we decided to gather likeminded folks to approach the topic from two ends at the first meetup in our new office: ➡️: Look into AI algorithms, explore limitations of current robustness frameworks and how these could be mitigated. ⬅️: Examine traditional cybersecurity frameworks, assess how they address AI-induced threats and pinpoint their shortcomings. Would you like to join the fun? 📍 diconium data | studio berlin, Skalitzer Straße 126, Berlin 🕘 Oct 16, 2024; 16:45 – 20:30 To sign up, follow the link in the comment below. 👀 Thomas Dr. Vladimir Maximilian Puneetha Markus Tom Aakash ?

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Awesome example of how reproducible dev environments with Jetify Devbox speeds up developer onboarding: "Linus demonstrated how he used Devbox from Jetify to significantly reduce the onboarding time for his students – from days to just a few hours – by setting up his computing environment in a re-runnable way."

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I still remember very clearly how I trained a semantic bilingual German-English embedding model almost four years ago. Back then for T-Systems on site services GmbH. Nowadays, with the hype of #RAG, it is probably one of the most popular German-language open source models, measured by the more than one million downloads per month. This success fills me with joy and pride and also with some doubts. But why the doubts? I think it is important to understand that there is a big difference between semantic embeddings and Q/A retrieval models. Semantic embeddings can be used to cluster texts. Or, for example, to search large texts on the basis of a few keywords. However, they are less suitable for finding answers to questions. The reason is that the semantic similarity between a question and a text with the answer does not necessarily have to be high. For this reason, Q/A retrieval models that are trained to embed questions and potential answers close to each other are primarily suitable for retrieval in RAG systems. I'm afraid many are using my semantic embedding as a replacement for a Q/A retrieval model in a RAG application. This should not be done. At Telekom, we use self-trained Q/A retrieval models for our RAG retrieval. We also have our own data sets for this. For other EU languages, by the way, we have had very good experiences with the intfloat/multilingual-e5-large model. Incidentally, this also works very well for German. - my semantic bilingual German-English embedding called "T-Systems-onsite/cross-en-de-roberta-sentence-transformer": https://lnkd.in/eSx6kc6m - the "intfloat/multilingual-e5-large model" model: https://lnkd.in/e7aetC3t Deutsche Telekom #AICC #iHub #PIX #weloveai #VTI #GenAI #LLM

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GraphRAG Microsoft recently open-sourced GraphRAG, https://lnkd.in/en_Qa5hU, a tool to develop pipelines to extract knowledge graphs from raw text. A decade ago, I worked on a project with Vineel Gujjar to construct knowledge graphs from aircraft technician notes. A cascaded design involved a system extracting entities such as "issues," "parts," and "fixes" and encoding their relationship in a knowledge graph to ultimately figure out the best "fix" for an "issue" involving a "part." LLM-based NLP systems augmented with GraphRAG approach would have made it much easier to rapidly develop and deploy complex systems like the one we developed a decade ago. Ramasubramanian (Ramsu) Sundararajan Debasis Bal

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𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗡𝗲𝘄𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗲 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) 𝗦𝗽𝗮𝗰𝗲! PyTorch has officially released TorchTune, a new tool designed to fine-tune Large Language Models (LLMs) with ease and efficiency. This groundbreaking tool promises to streamline the fine-tuning process, making it more accessible to researchers and practitioners alike. https://lnkd.in/eMf6dNZJ 𝗧𝗼𝗿𝗰𝗵𝘁𝘂𝗻𝗲 𝗢𝗳𝗳𝗲𝗿𝘀: ▪ 𝗣𝘆𝗧𝗼𝗿𝗰𝗵-𝗻𝗮𝘁𝗶𝘃𝗲 𝗟𝗟𝗠 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Build on familiar ground with composable building blocks for popular LLMs. ️ ▪ 𝗠𝗲𝗺𝗼𝗿𝘆-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗿𝗲𝗰𝗶𝗽𝗲𝘀: Train on consumer-grade GPUs with 24GB VRAM – no beefy machines needed! ▪ 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗲𝗮𝘀𝗲 𝗼𝗳 𝘂𝘀𝗲:  YAML configs and support for common dataset formats streamline your workflow. 𝗙𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗲𝗱 𝗶𝗻 𝗱𝗶𝘃𝗶𝗻𝗴 𝗱𝗲𝗲𝗽𝗲𝗿, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Code: https://lnkd.in/gEgETe5t Blog: https://lnkd.in/gP3mctA6 Tutorials: https://lnkd.in/gvh2eEsU This is a fantastic opportunity to explore and innovate, I'd be very interested in hearing your thoughts. ▪ 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲 𝗼𝗻 𝘁𝗵𝗶𝘀?  ▪ 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗧𝗼𝗿𝗰𝗵𝗧𝘂𝗻𝗲 𝗶𝗺𝗽𝗮𝗰𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗼𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵? ▪ 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗧𝗼𝗿𝗰𝗵𝗧𝘂𝗻𝗲 𝘄𝗶𝗹𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁? #AI #PyTorch #TorchTune #LLMs #MachineLearning

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𝐃𝐀𝐓𝐀 𝐒𝐂𝐈𝐄𝐍𝐂𝐄 & 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐈𝐕𝐄 𝐀𝐈 📣 Learn and Master Data Science & Generative AI in live online classes with our experts at 𝐨𝐧𝐥𝐲 $𝟐𝟐𝟓.𝟎𝟎 Only at https://lnkd.in/eGT33Mpf 📑 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐠𝐞𝐭: 👉𝟐𝟑 𝐌𝐨𝐝𝐮𝐥𝐞𝐬 with exercises 👉𝐋𝐢𝐯𝐞 𝐎𝐧𝐥𝐢𝐧𝐞 𝐂𝐥𝐚𝐬𝐬𝐞𝐬 for around 𝟓𝟏 𝐡𝐨𝐮𝐫𝐬 with Industry expert 👉Conceptual understanding of 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 👉Basics and advanced 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐨𝐟 𝐏𝐲𝐭𝐡𝐨𝐧 👉Basic and advanced 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 👉Supervised and Unsupervised 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 👉Basics and advanced 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 Subscribe to our YouTube channel - https://lnkd.in/eP6CXXtX #abclearnings #onlinecourses #learning #learninganddevelopment #learnings #learningjourney #learningopportunity #trainings #training #traininganddevelopment #onlineclass #livesessions #datascience #machinelearning #python #dataanalytics #analytics #data #statistics #deeplearning #datavisualization #powerbi #ai #artificialintelligence #genai #generative #generativeai

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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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🎉 Exciting news! I've just unlocked the scikit-learn superhero achievement with #DataCamp's supervised learning certification! 💫 Now I can teach machines... and maybe convince them to put their world domination plans on hold (for now). 😄🤖 #DataScience #MachineLearning #SupervisedLearning #ScikitLearn #Certification

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Apple targets Google staff to build artificial intelligence team - SWI swissinfo.ch https://buff.ly/4biSyYK - 🔍 Apple has been hiring AI experts from Google to expand its global AI and Machine Learning team - 🏢 Apple has a secretive European laboratory in Zurich, Switzerland - 📉 Apple's shares have slipped, putting pressure on the company to announce game-changing AI features - 💬 Apple is focusing on deploying generative AI on mobile devices - 🧠 Apple has been developing AI products, such as Siri, based on neural networks - 🤝 Apple's top AI team includes former key figures from Google - 🧑‍💻 Apple has acquired numerous AI start-ups in the past decade - 📱 The next big thing for Apple could be AI smartphones with more powerful chips - 🤐 Apple has been tight-lipped about its AI plans - 💡 Apple's foray into generative AI features may be revealed at its Worldwide Developers Conference in June

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Elevate Your Q&A with LangChain's RAG LangChain's latest guide explores how to build sophisticated Q&A applications using Retrieval Augmented Generation (RAG). This method enhances LLMs by integrating private or post-cutoff data, making them more effective for specific queries. The guide covers the RAG architecture, including indexing data and the retrieval-generation process, ensuring comprehensive understanding for developers. Key topics include: · RAG fundamentals and architecture · Data indexing and retrieval · Enhancing LLMs with specific information · RAG Architecture Breakdown The RAG architecture combines retrieval and generation capabilities to improve the accuracy and relevance of answers provided by language models. The architecture involves two main components: · Retriever: This component is responsible for searching a large corpus of documents to find relevant information based on the input query. It indexes the data and retrieves documents that are most likely to contain the answer. · Generator: Once relevant documents are retrieved, the generator uses these documents to generate a precise and coherent answer. This integration ensures that the answers are not only contextually accurate but also grounded in the specific data available. By combining these components, RAG leverages both the extensive knowledge embedded in LLMs and the specific information contained in private or domain-specific datasets. This hybrid approach ensures that the generated responses are both relevant and up-to-date, making RAG a powerful tool for developing advanced Q&A systems. https://lnkd.in/df5uAE7K #AI #MachineLearning #LangChain #DataScience #QandA

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Another meetup with great topics at the excellent host Prodato. I wish I could make it to learn more about MLOps pipelines!

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Meta Launches LLaMA 3: The Next Frontier in AI Innovation Key Points: Open Source Advantage: Meta introduces LLaMA 3, an open-source large language model (LLM), positioning it as a direct competitor to OpenAI's ChatGPT. The model aims to democratize AI development by being accessible to developers, researchers, and businesses worldwide. Enhanced Capabilities: LLaMA 3 is engineered to excel in complex reasoning, follow instructions precisely, and generate creative content. It marks a significant advancement over its predecessors by integrating capabilities to handle a broader range of inquiries, including more nuanced and complex questions. Multimodal Integration: Planned future enhancements include multimodal functionalities that will allow LLaMA 3 to interact through various inputs like text, voice, and images, broadening the use-case scenarios. Strategic Deployment: Meta plans to embed LLaMA 3 across its extensive suite of applications and upcoming smart devices, including Ray-Ban Meta smart glasses and Quest 3, aiming to create a more integrated user experience. #MetaAI #LLaMA3 #AI #OpenSource #TechInnovation #ArtificialIntelligence #MachineLearning #SmartTechnology #FutureOfAI https://lnkd.in/ded9kwjg

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