"AI-ready data means that your data must be representative of the use case, of every pattern, errors, outliers and unexpected emergence that is needed to train or run the AI model for the specific use. Data readiness for AI is not something you can build once and for all nor that you can build ahead of time for all your data. It is a process and a practice based on availability of metadata to align, qualify and govern the data." Is your data AI-ready? 🤷♀️ Find out with our latest white paper! 📑 #alationaiready #ai #whitepaper #artificialintelligence Monte Carlo Anomalo Experian
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"AI-ready data means that your data must be representative of the use case, of every pattern, errors, outliers and unexpected emergence that is needed to train or run the AI model for the specific use. Data readiness for AI is not something you can build once and for all nor that you can build ahead of time for all your data. It is a process and a practice based on availability of metadata to align, qualify and govern the data." Is your data AI-ready? 🤷♀️ Find out with our latest white paper! 📑 https://lnkd.in/eqJPNa4a #alationaiready #ai #whitepaper #artificialintelligence Monte Carlo Anomalo Experian Data Quality
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Start your engines: the AI data race is on! 🏎️ 🏁 However, to make sure your #AI initiatives can cross the finish line, you'll need high-quality data. Check out this white paper for your go-to resource on how to get AI-ready. We cover: 📄 Why data quality is so important for AI initiatives today 📄 How to create a data quality framework to support AI at scale 📄 Best practices for data governance and goal-setting for your AI use case Included are also special technology spotlights to our partners, Monte Carlo, Experian Data Quality, and Anomalo 🔦. Read on to also learn how they deliver data quality for AI-readiness. https://lnkd.in/eqJPNa4a #alationaiready #alationpartners #dataquality
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Great insights on the challenges of RAG systems! At Context Analytics, we've been hard at work addressing these pain points, especially in the realm of data pre-processing. Our collaboration with S&P Global has resulted in the Global Machine Readable Filings product, which is an excellent resource for LLMs seeking to leverage corporate filings worldwide. Additionally, our Universal Document Processor can help with parsing internal documents for use in your LLM, freeing up data scientists to focus on more high-value implementations. Reach out to us if you have any questions about how we can help streamline your AI processes! #AI #DataProcessing #FinancialServices
There's plenty of excitement about AI and Retrieval Augmented Generation, but we rarely hear about the tricky parts. Let’s change that! Inspired by "Seven Failure Points When Engineering a Retrieval Augmented Generation System" by Barnett et al., I've put together a list of the top 10 challenges that RAG systems face, along with practical solutions to tackle them. Top 10 Pain Points & Solutions: Missing Content: Keep your data rich and accurate to avoid giving wrong answers that seem right. Missed Top Documents: Fine-tune parameters like chunk_size and similarity_top_k to make sure important documents don't get overlooked. Contextual Limitations: Use smarter strategies for consolidating and reranking data to make the context as clear as possible. Extraction Failures: Clean your data thoroughly and compress information to pull out the right answers. Format Mismatch: Make your prompts clear and use advanced parsing tools to get responses in the format you need. Vague Specificity: Employ deeper retrieval techniques to match the detail your users are expecting. Incomplete Responses: Modify your queries to ensure they're comprehensive and really dig into the details. Scaling Issues in Data Ingestion: Ensure your data ingestion can handle large volumes without slowing down. QA for Structured Data: Improve how you handle structured data with step-by-step reasoning and intelligent transformations. Challenges with Complex PDFs: Use specialized tools to pull data from complex PDFs effectively. If this generates enough interest, I'll write a deep dive into any one of these challenges so that we can explore these topics in a more technical way. #AI #MachineLearning #DataScience #RAG #LLM #TechInnovation
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Join us on January 17 for a Qlik webinar, and learn more about the top trends around #data , #analytics and #AI that will impact your organization in 2024! Register now and receive an eBook exploring practical applications of the 2024 data, analytics, and AI trends.
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Craxel delivers unprecedented price/performance for petabyte scale data analytics. Our patented Black Forest technology is incredibly fast and efficient, delivering an entirely new way to organize data called time series graphs. Organizations can build time series graphs as the data arrives so that insight is immediately available to both analysts and AI algorithms. This next generation data analytics platform delivers massive productivity gains through dramatically faster queries and rapid time to insight. Using a fraction of the compute of traditional algorithms, organizations can extract value faster and more efficiently. It is so powerful, it fundamentally changes the game for the world's largest organizations with the largest data challenges. Learn more at www.Craxel.com #timeseriesgraphs #bigdata #AI
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#6: Evaluate and Refine Evaluating and refining your model ensures it performs well on new, unseen data. Evaluation Metrics #ModelEvaluation #Metrics Accuracy: The percentage of correct predictions. Example: Use accuracy for classification tasks. Precision, Recall, F1 Score: Metrics for evaluating classification performance, especially with imbalanced datasets. Example: Use F1 score to balance precision and recall in imbalanced datasets. Fine-tuning #FineTuning #Hyperparameters Hyperparameter Tuning: Adjust parameters like learning rate, batch size, and the number of layers. Example: Use Grid Search or Random Search to find optimal hyperparameters. Data Augmentation: Enhance your dataset to improve model robustness. Example: Apply transformations to increase the diversity of your image data. Model Improvement #ModelImprovement #IterativeProcess Iterative Process: Continually improve by retraining with more data, refining model architecture, and tweaking hyperparameters. Example: Iterate between training and evaluation, making incremental improvements each time. #EncephAI #ArtificialIntelligence #MachineLearning #AI #NeuralNetworks #DeepLearning
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Join Nasuni to gain Insight into how AI is reshaping the perception of unstructured data #nasuni https://lnkd.in/eXE8-rbn
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This is happening tomorrow. Register to gain insight into the impact of AI on your unstructured data #nasuni
Join Nasuni to gain Insight into how AI is reshaping the perception of unstructured data #nasuni https://lnkd.in/eXE8-rbn
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Ever wonder why AI/ML models sometimes seem impossible to implement properly or directly influence the bottom line? The answer is usually the quality of the underlying data. Poor data quality can not only affect model creation but potentially lead businesses astray, by impacting even the simplest use case. Our recent blog post illustrates the value of prioritizing the data ingestion process, to ensure the team is collecting clean, complete and current data. Link in the comments
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