AWS Machine Learning Blog

Category: Artificial Intelligence

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

Amazon S3 plugin for PyTorch is an open-source library which is built to be used with the deep learning framework PyTorch for streaming data from Amazon Simple Storage Service (Amazon S3). With this feature available in PyTorch Deep Learning Containers, you can take advantage of using data from S3 buckets directly with PyTorch dataset and […]

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

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 pipelines in various languages (JSON, YAML, Python, and more), followed […]

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 […]

Schedule an Amazon SageMaker Data Wrangler flow to process new data periodically using AWS Lambda functions

Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

How Intel Olympic Technology Group built a smart coaching SaaS application by deploying pose estimation models – Part 1

The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software as a service (SaaS) application using computer vision (CV)-based pose estimation models. Pose estimation is a class of machine learning (ML) model that […]

Increase your machine learning success with AWS ML services and AWS Machine Learning Embark

This is a guest post from Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. In the life sciences industry, data is growing in abundance and is getting increasingly complex, which makes it challenging to use traditional analytics methodologies. At Thermo Fisher Scientific, our mission is to make the world healthier, cleaner, and safer, and […]

Fine-tune and host Hugging Face BERT models on Amazon SageMaker

The last few years have seen the rise of transformer deep learning architectures to build natural language processing (NLP) model families. The adaptations of the transformer architecture in models such as BERT, RoBERTa, T5, GPT-2, and DistilBERT outperform previous NLP models on a wide range of tasks, such as text classification, question answering, summarization, and […]

Dive deep into Amazon SageMaker Studio Notebooks architecture

Machine learning (ML) is highly iterative and complex in nature, and requires data scientists to explore multiple ways in which a business problem can be solved. Data scientists have to use tools that support interactive experimentation so you can run code, review its outputs, and annotate it, which makes it easy to work and collaborate […]