Videos
Watch Andrew Ng’s BUILD 2024 Keynote on the rise of AI agents and agentic reasoning. Discover how these advancements are shaping the future of artificial intelligence.
In this tutorial video, discover four ways of evaluating the performance of a Classification computer vision model in LandingLens on Snowflake: Model metrics such as F1, Precision and Recall Confusion matrix, confidence scores and visual inspection of misclassifications Heatmap overlays on image pixels Evaluations sets and side-by-side comparisons
In this video tutorial, you will see how to configure four computer vision models and begin model training jobs in LandingLens on Snowflake. Learn about data partitioning, GPU provisioning, and model training configurations. Monitor training progress through loss charts.
Bring images into a Snowflake stage and sync the contents with LandingLens on Snowflake. This tutorial uses pre-labeled images for a binary classification project. It shows how to create the stage in Snowsight and how to grant the required permissions for the image sync to succeed.
Artificial intelligence is revolutionizing the Energy and Utilities industry, transforming critical areas such as Asset Inspection, Condition Monitoring, Predictive Maintenance, Load Optimization, and Infrastructure Planning. Join industry experts Titiaan Palazzi (Head of Power & Utilities GTM at Snowflake), Sonia García Fominaya (Data Scientist at Capgemini Engineering), and Carl Lewis (Sr. Director of Partnerships at LandingAI) […]
When your model encounters something new, it may be time to retrain. This video shows how a model in production is confused by an object it has not seen before – horses. In LandingLens it is easy to create a new class, label a small number of images and retrain the model to detect […]
Assign images to be labeled to one or more labelers. Create a labeling task for a set of images and choose how many labelers should complete each task. Track the team’s progress labeling the assigned images for your machine learning or computer vision project.
LandingLens has built-in tools to assist with labeling images that have poor contrast. Auto-contrast and histogram equalization are especially helpful for low-light and infrared images used in machine learning and computer vision.
See how to apply the concept of continuous learning when working with LandingLens and LandingEdge. Mark images that have been sent for inference correct or incorrect with human judgement. Then use the incorrect images to improve your model. You can automatically add images that you’ve run inference on using LandingEdge into your dataset in LandingLens, […]
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