Enterprises perform quality control to ensure products meet production standards and avoid potential brand reputation damage. As the cost of sensors decreases and connectivity increases, industries adopt real-time imagery analysis to detect quality issues. At the same time, artificial intelligence (AI) advancements enable advanced automation, reduce overall cost and project time, and produce accurate defect detection results in manufacturing plants. As these technologies mature, AI-driven inspections are more common outside of the plant environment. This post describes our SOLVED (Solar Roving Eye Detector) project leveraging machine learning (ML) to identify damaged solar panels using Amazon Rekognition Custom Labels and alert operators to take corrective action.

Renewable resources like sunlight provide a sustainable and carbon neutral mechanism to generate power. Governments in many countries are providing incentives and subsidies to households to install solar panels as part of small-scale renewable energy schemes. This has created a huge demand for solar panels. Reaching out to potential customers at the right time, through the right channel, and with attractive offers is very crucial for solar and energy companies. They're looking for cost-efficient approaches and tools to conduct targeted marketing to proactively reach out to potential customers.

Only 9 days until AWS re:Invent 2021, and we're very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders' sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge.

For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human intelligence. However, manual dataset annotation can be tedious and tiring for a human, especially on a large dataset. Even with labels that are obvious to a human to annotate, the process can still be error-prone due to fatigue. As a result, building training datasets takes up to 80% of a data scientist's time.

In our Community Showcase, Amazon Web Services (AWS) highlights projects created by AWS Heroes and AWS Community Builders. Each month AWS ML Heroes and AWS ML Community Builders bring to life projects and use cases for the full range of machine learning skills from beginner to expert through deep dive tutorials, podcasts, videos, and other content that show how to use AWS Machine Learning (ML) solutions such as Amazon SageMaker, pertained AI services such as Amazon Rekognition, and AI learning devices such as AWS DeepRacer. The AWS ML community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and ML concepts, contribute with real-world experiences, and collaborate on building projects together. Here are a few highlights of externally published getting started guides and tutorials curated by our AWS ML Evangelist team led by Julien Simon. Making My Toddler's Dream of Flying Come True with AI Tech (with code samples).

Building your own computer vision model from scratch can be fun and fulfilling. You get to decide your preferred choice of machine learning framework and platform for training and deployment, design your data pipeline and neural network architecture, write custom training and inference scripts, and fine-tune your model algorithm's hyperparameters to get the optimal model performance. On the other hand, this can also be a daunting task for someone who has no or little computer vision and machine learning expertise. This post shows a step-by-step guide on how to build a natural flower classifier using Amazon Rekognition Custom Labels with AWS best practices. Amazon Rekognition Custom Labels is a feature of Amazon Rekognition, one of the AWS AI services for automated image and video analysis with machine learning. It provides Automated Machine Learning (AutoML) capability for custom computer vision end-to-end machine learning workflows.

Building your own computer vision model from scratch can be fun and fulfilling. You get to decide your preferred choice of machine learning framework and platform for training and deployment, design your data pipeline and neural network architecture, write custom training and inference scripts, and fine-tune your model algorithm's hyperparameters to get the optimal model performance. On the other hand, this can also be a daunting task for someone who has no or little computer vision and machine learning expertise. This post shows a step-by-step guide on how to build a natural flower classifier using Amazon Rekognition Custom Labels with AWS best practices. Amazon Rekognition Custom Labels is a feature of Amazon Rekognition, one of the AWS AI services for automated image and video analysis with machine learning.

The following image shows an example input image and its corresponding output from the DetectProtectiveEquipment as seen on the Amazon Rekognition PPE detection console. In this example, we supply face cover as the required PPE and 80% as the required minimum confidence threshold as part of summarizationattributes. We receive a summarization result that indicates that there are four persons in the image that are wearing face covers at a confidence score of over 80% [person identifiers 0, 1,2, 3]. It also provides the full fidelity API response in the per-person results. Note that this feature doesn't perform facial recognition or facial comparison and can't identify the detected persons.

Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.

Your email address will not be published.