Using genetic algorithms on AWS for optimization problems

aws.amazon.com 8h

Machine learning (ML)-based solutions are capable of solving complex problems, from voice recognition to finding and identifying faces in video clips or photographs. Usually, these solutions use large amounts of training data, which results in a model that processes input data and produces numeric output that can be interpreted as a word, face, or classification […]

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Creating a BankingBot on Amazon Lex V2 Console with support for English and Spanish

aws.amazon.com 1d

Amazon Lex is a service for building conversational interfaces into any application. The new Amazon Lex V2 Console and APIs make it easier to build, deploy, and manage bots. In this post, you will learn about about the 3 main benefits of Amazon Lex V2 Console and API, basic bot building concepts, and how to […]

Using Amazon Translate to provide language support to Amazon Kendra

aws.amazon.com 1d

Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). Amazon Kendra supports English. This post provides a set of techniques to provide non-English language support when using Amazon Kendra. We demonstrate these techniques within the context of a question-answer chatbot use case (Q&A bot) where a user can […]

Using the AWS DeepRacer new Soft Actor Critic algorithm with continuous action spaces

aws.amazon.com 1d

AWS DeepRacer is the fastest way to get started with machine learning (ML). You can train reinforcement learning (RL) models by using a 1/18th scale autonomous vehicle in a cloud-based virtual simulator and compete for prizes and glory in the global AWS DeepRacer League. We’re excited to bring you two new features available on the […]

Scheduling work meetings in Slack with Amazon Lex

aws.amazon.com 1d

Imagine being able to schedule a meeting or get notified about updates in your code repositories without leaving your preferred messaging platform. This could save you time and increase productivity. With the advent of chatbots, these mundane tasks are now easier than ever. Amazon Lex, a service for building chatbots, offers native integration with popular […]

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Automating complex deep learning model training using Amazon SageMaker Debugger and AWS Step Functions

aws.amazon.com 2d

Amazon SageMaker Debugger can monitor ML model parameters, metrics, and computation resources as the model optimization is in progress. You can use it to identify issues during training, gain insights, and take actions like stopping the training or sending notifications through built-in or custom actions. Debugger is particularly useful in training challenging deep learning model […]

Setting up an IVR to collect customer feedback via phone using Amazon Connect and AWS AI Services

aws.amazon.com 2d

As many companies place their focus on customer centricity, customer feedback becomes a top priority. However, as new laws are formed, for instance GDPR in Europe, collecting feedback from customers can become increasingly difficult. One means of collecting this feedback is via phone. When a customer calls an agency or call center, feedback may be […]

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This month in AWS Machine Learning: January edition

aws.amazon.com 3d

Hello and welcome to our first “This month in AWS Machine Learning” of 2021! Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each […]

Get ready to roll! AWS DeepRacer pre-season racing is now open

aws.amazon.com 3d

AWS DeepRacer allows you to get hands on with machine learning (ML) through a fully autonomous 1/18th scale race car driven by reinforcement learning, a 3D racing simulator on the AWS DeepRacer console, a global racing league, and hundreds of customer-initiated community races. Pre-season qualifying underway We’re excited to announce that racing action is right […]

Introducing spaCy v3.0 · Explosion

explosion.ai 3d

spaCy v3.0 is a huge release! It features new transformer-based pipelines that get spaCy's accuracy right up to the current state-of-the-art, and a new workflow system to help you take projects from prototype to production. It's much easier to configure and train your pipeline, and there are lots of new and improved integrations with the rest of the NLP ecosystem....

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SPACY v3: Design concepts explained (behind the scenes)

youtube.com 3d

SPACY v3: Custom trainable relation extraction component

youtube.com 3d

SPACY v3: State-of-the-art NLP from Prototype to Production

youtube.com 3d

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On decoupling

medium.com 4d

What are the pros (and cons) of decoupling systems and organizations? When and how should you decouple? Is it worth it?

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Performing anomaly detection on industrial equipment using audio signals

aws.amazon.com 6d

Industrial companies have been collecting a massive amount of time-series data about operating processes, manufacturing production lines, and industrial equipment. You might store years of data in historian systems or in your factory information system at large. Whether you’re looking to prevent equipment breakdown that would stop a production line, avoid catastrophic failures in a […]

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Forecasting AWS spend using the AWS Cost and Usage Reports, AWS Glue DataBrew, and Amazon Forecast

aws.amazon.com 7d

AWS Cost Explorer enables you to view and analyze your AWS Cost and Usage Reports (AWS CUR). You can also predict your overall cost associated with AWS services in the future by creating a forecast of AWS Cost Explorer, but you can’t view historical data beyond 12 months. Moreover, running custom machine learning (ML) models […]

Managing your machine learning lifecycle with MLflow and Amazon SageMaker

aws.amazon.com 7d

With the rapid adoption of machine learning (ML) and MLOps, enterprises want to increase the velocity of ML projects from experimentation to production. During the initial phase of an ML project, data scientists collaborate and share experiment results in order to find a solution to a business need. During the operational phase, you also need […]

Understanding the key capabilities of Amazon SageMaker Feature Store

aws.amazon.com 7d

One of the challenging parts of machine learning (ML) is feature engineering, the process of transforming data to create features for ML. Features are processed data signals used for training ML models and for deployed models to make accurate predictions. Data scientists and ML engineers can spend up to 60-70% of their time on feature […]

Saving time with personalized videos using AWS machine learning

aws.amazon.com 7d

CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually […]

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Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA

aws.amazon.com 8d

This is a guest post from deepset (creators of the open source frameworks FARM and Haystack), and was contributed to by authors from NVIDIA and AWS.  At deepset, we’re building the next-level search engine for business documents. Our core product, Haystack, is an open-source framework that enables developers to utilize the latest NLP models for […]

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How to deliver natural conversational experiences using Amazon Lex Streaming APIs

aws.amazon.com 9d

Natural conversations often include pauses and interruptions. During customer service calls, a caller may ask to pause the conversation or hold the line while they look up the necessary information before continuing to answer a question. For example, callers often need time to retrieve credit card details when making bill payments. Interruptions are also common. […]

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Model serving in Java with AWS Elastic Beanstalk made easy with Deep Java Library

aws.amazon.com 10d

Deploying your machine learning (ML) models to run on a REST endpoint has never been easier. Using AWS Elastic Beanstalk and Amazon Elastic Compute Cloud (Amazon EC2) to host your endpoint and Deep Java Library (DJL) to load your deep learning models for inference makes the model deployment process extremely easy to set up. Setting […]

Building your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels

aws.amazon.com 10d

According to Gartner, 58% of marketing leaders believe brand is a critical driver of buyer behavior for prospects, and 65% believe it’s a critical driver of buyer behavior for existing customers. Companies spend huge amounts of money on advertisement to raise brand visibility and awareness. In fact, as per Gartner, CMO spends over 21% of […]

Model serving made easier with Deep Java Library and AWS Lambda

aws.amazon.com 10d

Developing and deploying a deep learning model involves many steps: gathering and cleansing data, designing the model, fine-tuning model parameters, evaluating the results, and going through it again until a desirable result is achieved. Then comes the final step: deploying the model. AWS Lambda is one of the most cost effective service that lets you run code without […]

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Multi-account model deployment with Amazon SageMaker Pipelines

aws.amazon.com 13d

Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps). Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account […]

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Amazon Lex Introduces an Enhanced Console Experience and New V2 APIs

aws.amazon.com 14d

Today, the Amazon Lex team has released a new console experience that makes it easier to build, deploy, and manage conversational experiences. Along with the new console, we have also introduced new V2 APIs, including continuous streaming capability. These improvements allow you to reach new audiences, have more natural conversations, and develop and iterate faster. […]

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Redacting PII from application log output with Amazon Comprehend

aws.amazon.com 15d

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in text. The service can extract people, places, sentiments, and topics in unstructured data. You can now use Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII) in application logs, customer emails, support […]

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Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines

aws.amazon.com 16d

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model […]

Labeling mixed-source, industrial datasets with Amazon SageMaker Ground Truth

aws.amazon.com 16d

Prior to using any kind of supervised machine learning (ML) algorithm, data has to be labeled. Amazon SageMaker Ground Truth simplifies and accelerates this task. Ground Truth uses pre-defined templates to assign labels that classify the content of images or videos or verify existing labels. Ground Truth allows you to define workflows for labeling various […]

Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data

aws.amazon.com 16d

In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset. This dataset was developed by the MIT lab for Computational Physiology and consists of de-identified healthcare […]

ML and NLP Research Highlights of 2020

ruder.io 16d

This post summarizes progress in 10 exciting and impactful directions in ML and NLP in 2020.

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Automating Amazon Personalize solution using the AWS Step Functions Data Science SDK

aws.amazon.com 21d

Machine learning (ML)-based recommender systems aren’t a new concept across organizations such as retail, media and entertainment, and education, but developing such a system can be a resource-intensive task—from data labelling, training and inference, to scaling. You also need to apply continuous integration, continuous deployment, and continuous training to your ML model, or MLOps. The […]

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Using machine learning to predict vessel time of arrival with Amazon SageMaker

aws.amazon.com 22d

According to the International Chamber of Shipping, 90% of world commerce happens at sea. Vessels are transporting every possible kind of commodity, including raw materials and semi-finished and finished goods, making ocean transportation a key component of the global supply chain. Manufacturers, retailers, and the end consumer are reliant on hundreds of thousands of ships […]

Creating high-quality machine learning models for financial services using Amazon SageMaker Autopilot

aws.amazon.com 22d

Machine learning (ML) is used throughout the financial services industry to perform a wide variety of tasks, such as fraud detection, market surveillance, portfolio optimization, loan solvency prediction, direct marketing, and many others. This breadth of use cases has created a need for lines of business to quickly generate high-quality and performant models that can […]

How to train procedurally generated game-like environments at scale with Amazon SageMaker RL

aws.amazon.com 22d

A gym is a toolkit for developing and comparing reinforcement learning algorithms. Procgen Benchmark is a suite of 16 procedurally-generated gym environments designed to benchmark both sample efficiency and generalization in reinforcement learning.  These environments are associated with the paper Leveraging Procedural Generation to Benchmark Reinforcement Learning (citation). Compared to Gym Retro, these environments have […]

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AWS Announces the global expansion of AWS CCI Solutions

aws.amazon.com 23d

We’re excited to announce the global availability of AWS Contact Center Intelligence (AWS CCI) solutions powered by AWS AI Services and made available through the AWS Partner Network. AWS CCI solutions enable you to leverage AWS machine learning (ML) capabilities with your current contact center provider to gain greater efficiencies and deliver increasingly tailored customer […]

Hardware for Deep Learning. Part 4: ASIC

blog.inten.to 23d

This is a part about ASICs from the “Hardware for Deep Learning” series. The content of the series is here.

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Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC

aws.amazon.com 24d

Amazon SageMaker Studio notebooks provide a full-featured integrated development environment (IDE) for flexible machine learning (ML) experimentation and development. Security measures secure and support a versatile and collaborative environment. In some cases, such as to protect sensitive data or meet regulatory requirements, security protocols require that public internet access be disabled in the development environment. […]

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Artificial intelligence and machine learning continues at AWS re:Invent

aws.amazon.com 27d

A fresh new year is here, and we wish you all a wonderful 2021. We signed off last year at AWS re:Invent on the artificial intelligence (AI) and machine learning (ML) track with the first ever machine learning keynote and over 50 AI/ML focused technical sessions covering industries, use cases, applications, and more. You can […]

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Accelerating MLOps at Bayer Crop Science with Open Source Workflow Managers and Amazon SageMaker

aws.amazon.com on 5 January

This is a guest post by the data science team at Bayer Crop Science.  Farmers have always collected and evaluated a large amount of data with each growing season: seeds planted, crop protection inputs applied, crops harvested, and much more. The rise of data science and digital technologies provides farmers with a wealth of new […]

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Implementing a custom labeling GUI with built-in processing logic with Amazon SageMaker Ground Truth

aws.amazon.com on 5 January

Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. It offers easy access to Amazon Mechanical Turk and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. A labeling team may wish to use […]

Implementing a custom labeling GUI with built-in processing logic with Amazon SageMaker Ground Truth

aws.amazon.com on 4 January

Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. It offers easy access to Amazon Mechanical Turk and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. A labeling team may wish to use […]

Building a secure search application with access controls using Amazon Kendra

aws.amazon.com on 4 January

For many enterprises, critical business information is often stored as unstructured data scattered across multiple content repositories. Not only is it challenging for organizations to make this information available to employees when they need it, but it’s also difficult to do so securely so relevant information is available to the right employees or employee groups. […]

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Explosion in 2020: Our Year in Review · Explosion

explosion.ai on 31 December

While 2020 hasn’t been easy for anyone, at Explosion we’ve considered ourselves relatively fortunate in this most interesting year. We’ve always worked remotely, so we’ve been able to take both pride and comfort in continuing to ship good software. Here’s a look back at what we’ve been up to.

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Extracting buildings and roads from AWS Open Data using Amazon SageMaker

aws.amazon.com on 30 December

Sharing data and computing in the cloud allows data users to focus on data analysis rather than data access. Open Data on AWS helps you discover and share public open datasets in the cloud. The Registry of Open Data on AWS hosts a large amount of public open data. The datasets range from genomics to climate to transportation […]

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How an important change in web standards impacts your image annotation jobs

aws.amazon.com on 29 December

Earlier in 2020, widely used browsers like Chrome and Firefox changed their default behavior for rotating images based on image metadata, referred to as EXIF data. Previously, images always displayed in browsers exactly how they’re stored on disk, which is typically unrotated. After the change, images now rotate according to a piece of image metadata […]

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How Foxconn built an end-to-end forecasting solution in two months with Amazon Forecast

aws.amazon.com on 23 December

This is a guest post by Foxconn. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.  In their own words, “Established in Taiwan in 1974, Hon Hai Technology Group (Foxconn) is the world’s largest electronics manufacturer. Foxconn is […]

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Controlling and auditing data exploration activities with Amazon SageMaker Studio and AWS Lake Formation

aws.amazon.com on 22 December

Highly-regulated industries, such as financial services, are often required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post walks you through the steps to implement access control and auditing capabilities on a per-user basis, using Amazon […]

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Building and deploying an object detection computer vision application at the edge with AWS Panorama

aws.amazon.com on 19 December

Computer vision (CV) is sought after technology among companies looking to take advantage of machine learning (ML) to improve their business processes. Enterprises have access to large amounts of video assets from their existing cameras, but the data remains largely untapped without the right tools to gain insights from it. CV provides the tools to […]

Training a Text2Sparql Model with MK-SQuIT and NeMo

developer.nvidia.com on 18 December

Across several verticals, question answering (QA) is one of the fastest ways to deliver business value using conversational AI. Informally, QA is the task of…