Alation’s Post

"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

This is happening tomorrow. Register to gain insight into the impact of AI on your unstructured data #nasuni

#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

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

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.

Please tell me something about The Data Swiss... We are a small boutique, comprised by some collaborators that are ready to help your business get control over your data. The founder, Tomás Denis Reyes Sánchez has one thing quite clear: data and ml engineering disciplines are far from being taken over by AI (or not needed). Many companies, in the last 2 years, with the proliferation and hype around AI think they are ready to use models and leverage AI everywhere in their processes. Unfortunately without a proper Data and ML foundations this is a daunting- if not- impossible chimera to achieve. More info, we invite you over https://lnkd.in/dyBs3Gxb

Join Nasuni to gain Insight into how AI is reshaping the perception of unstructured data #nasuni https://lnkd.in/eXE8-rbn

Check out my recent blog post on my two favorite topics: Graphs and AI. It's a fascinating and growing topic. #KnowledgeGraphs #GenAI

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