AWS Machine Learning Blog

Category: Artificial Intelligence

Introducing PII identification and redaction in streaming transcriptions using Amazon Transcribe

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capabilities to their applications. Since launching in 2017, Amazon Transcribe has added numerous features to enhance its capabilities around converting speech to text. Some of these features include automatic language detection, custom language models, vocabulary […]

How to redact personally identifiable information from audio files with Amazon Transcribe

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to your applications. Speech or audio data is virtually impossible for computers to search and analyze. Therefore, recorded speech needs to be converted to text before it can be used in applications. Automatic content redaction is a feature […]

Get value from every customer touchpoint using Amazon Connect as a data gathering mechanism

The recent pandemic and the impossibility of meeting customers in person has made two-way contact centers an effective tool for sales representatives. Amazon Connect is the ideal service to manage these contacts, and its adoption gives you the opportunity to gather new business insights. Thanks to Amazon Connect, you can program outbound calls to reach […]

Manage your Amazon Fraud Detector resources in an automated and secure manner using AWS CloudFormation

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Amazon Fraud Detector combines your data, the latest in ML science, […]

The development of Bundesliga Match Fact Passing Profile, a deep dive into passing in football

This post was authored by Simon Rolfes. Simon played 288 Bundesliga games as a central midfielder, scored 41 goals, and won 26 caps for Germany. Currently, he serves as Sporting Director at Bayer 04 Leverkusen, where he oversees and develops the pro player roster, the scouting department, and the club’s youth development. Simon also writes […]

Boost transcription accuracy of class lectures with custom language models for Amazon Transcribe

Many universities like transcribing their recorded class lectures and later creating captions out of these transcriptions. Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. Transcribe assists in increasing accessibility and improving content engagement and learning outcomes by connecting with both auditory and […]

Fully customizable action space now available on the AWS DeepRacer console

AWS DeepRacer is the fastest way to get rolling with machine learning (ML) through a global racing league, cloud-based 3D racing simulator, and fully autonomous 1/18th scale race car driven by reinforcement learning. Starting today, the model action space is fully customizable yet simplified with new dynamic graphics so developers have greater control and can […]

Announcing the Amazon S3 plugin for PyTorch

November 2023: On 11/22/2023, AWS announced the Amazon S3 Connector for PyTorch ─ a new connector that delivers high throughput for PyTorch training jobs that access data in Amazon S3. We recommend customers use the new connector for PyTorch training jobs that read and write data in Amazon S3. The Amazon S3 Connector for PyTorch […]

Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language

May 2024: This post was reviewed and updated for accuracy. You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose […]

Build machine learning at the edge applications using Amazon SageMaker Edge Manager and AWS IoT Greengrass V2

Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the cloud. Although there are numerous ways to train ML models for countless applications, effectively optimizing and deploying these models for IoT devices can present many […]