Amazon Lookout for Vision provides a machine learning (ML)-based anomaly detection service to identify normal images (i.e., images of objects without defects) vs anomalous images (i.e., images of objects with defects), types of anomalies (e.g., missing piece), and the location of these anomalies. Therefore, Lookout for Vision is popular among customers that look for automated […]

To train a machine learning (ML) model, you need a large, high-quality, labeled dataset. Amazon SageMaker Ground Truth helps you build high-quality training datasets for your ML models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company of your choosing, or an internal, private workforce to enable you to create a labeled dataset. You can use the labeled dataset output from Ground Truth to train your own models. You can also use the output as a training dataset for an Amazon SageMaker model.

Then I used the p3.2xlarge instance for model training by setting instance_type "ml.p3.2xlarge". The training completed in 8 minutes. The best MIoU (Mean Intersection over Union) of 0.846 is achieved at epoch 11 with a pix_acc (the percent of pixels in your image that are classified correctly) of 0.925, which is a pretty good result for this small dataset. I hosted the model on a low-cost ml.c5.xlarge instance:

Today, I am happy to announce that you can now use Amazon SageMaker Ground Truth to generate labeled synthetic image data. Building machine learning (ML) models is an iterative process that, at a high level, starts with data collection and preparation, followed by model training and model deployment. And especially the first step, collecting large, diverse, and accurately labeled datasets for your model training, is often challenging and time-consuming. Let's take computer vision (CV) applications as an example. CV applications have come to play a key role in the industrial landscape.

In a follow-up to new compute, network and data service offerings announced by Amazon Web Services (AWS) CEO Adam Selipsky, AWS vice president of AI, Swami Sivasubramanian, pulled the covers off some updates to database, machine learning and serverless offerings. Taking a cue from Selipsky's theme of simplifying AWS' array of services in order to make them easier to consume for developers and enterprises, Sivasubramanian announced three new updates to AWS' plethora of database offerings. They include a new managed database service for business applications that allows developers and enterprises to customise the underlying database and operating system; a new table class for Amazon DynamoDB designed to reduce storage costs for infrequently accessed data; and a service that uses machine learning to better diagnose and remediate database-related performance issues. The new managed database service, Amazon RDS (Relational Database Service) Custom, is aimed at customers whose applications require customisation at the database level and thus are responsible for administrative tasks such as provisioning, database setup, patching and backups that take up a lot of time, Sivasubramanian said. Amazon RDS Custom automates these administrative processes while allowing customisation to the database and underlying operating system these applications require, Sivasubramanian said.

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.

Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.

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.

DICOM (Digital Imaging and Communications in Medicine) is an image format that contains visualizations of X-Rays and MRIs as well as any associated metadata. DICOM is the standard for medical professionals and healthcare researchers for visualizing and interpreting X-Rays and MRIs. For this post, we use a chest X-Ray DICOM images dataset from the MIMIC Chest X-Ray (MIMIC-CXR) Database, a publicly available database of chest X-Ray images in DICOM format and the associated radiology reports as free text files. To access the files, you must be a registered user and sign the data use agreement. We label the images through the Ground Truth private workforce.

AWS(Amazon Web Services) is the most popular and widely used cloud service provider. In 2017 AWS released its fully managed machine learning platform on cloud called Amazon Sagemaker, that allows developers to create, train and deploy their models quickly. In 2018, Amazon Sagemaker Ground Truth was launched to fully manage data labelling services for generating high-quality ground truth datasets to be trained into machine learning models. Ground Truth can integrate Amazon Mechanical Turk(the crowdsourcing platform) or internal data labelling team or external 3rd party vendors to get the labelling job done. Workflows can be customized or made use of built-in.