With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy with anomaly detection, help provide continuous improvements, and retrain models with updated predictions. However, you’re often forced to choose between an ML-only or human-only system. Companies are looking for the best of both worlds, integrating ML systems into your workflow while keeping a human …
Detect defects and augment predictions using Amazon Lookout for Vision and Amazon A2I
"The Power of AI in Business and Entrepreneurship: Unlocking Opportunities and Driving Success"
"The Power of AI: Revolutionizing Business and Empowering Entrepreneurs"
Optimize your inference jobs using dynamic batch inference with TorchServe on Amazon SageMaker
Graph-based recommendation system with Neptune ML: An illustration on social network link prediction...