We incorporate ML into many aspects of Snapchat, and exploring innovation in this field is a key priority. Once we heard about Inferentia, we started collaborating with AWS to adopt Inf1/Inferentia instances to help us with ML deployment, including around performance and cost. We started with our recommendation models and look forward to adopting more models with the Inf1 instances in the future.

Sprinklr's AI-driven unified customer experience management (Unified-CXM) platform enables companies to gather and translate real-time customer feedback across multiple channels into actionable insights—resulting in proactive issue resolution, enhanced product development, improved content marketing, better customer service, and more. Using Amazon EC2 Inf1, we were able to significantly improve the performance of one of our NLP models and improve the performance of one of our computer vision models. We're looking forward to continuing to use Amazon EC2 Inf1 to better serve our global customers.

Our state-of-the-art NLP product, Finch for Text, offers users the ability to extract, disambiguate, and enrich multiple types of entities in huge volumes of text. Finch for Text requires significant computing resources to provide our clients with low-latency enrichments on global data feeds. We are now using AWS Inf1 instances in our PyTorch NLP, translation, and entity disambiguation models. We were able to reduce our inference costs by over 80% (over GPUs) with minimal optimizations while maintaining our inference speed and performance. This improvement allows our customers to enrich their French, Spanish, German, and Dutch language text in real time on streaming data feeds and at global scale—something that’s critical for our financial services, data aggregator, and public sector customers.

We alert on many types of events all over the world in many languages, in different formats (images, video, audio, text sensors, combinations of all these types) from hundreds of thousands of sources. Optimizing for speed and cost given that scale is absolutely critical for our business. With AWS Inferentia, we have lowered model latency and achieved up to 9x better throughput per dollar. This has allowed us to increase model accuracy and grow our platform's capabilities by deploying more sophisticated DL models and processing 5x more data volume while keeping our costs under control.

Autodesk is advancing the cognitive technology of our AI-powered virtual assistant, Autodesk Virtual Agent (AVA), by using Inferentia. AVA answers over 100,000 customer questions per month by applying natural language understanding (NLU) and deep learning (DL) techniques to extract the context, intent, and meaning behind inquiries. Piloting Inferentia, we are able to obtain a 4.9x higher throughput over G4dn for our NLU models, and look forward to running more workloads on the Inferentia-based Inf1 instances.

The use of ground-penetrating radar and detection of visual defects is typically the domain of expert surveyors. An AWS microservices-based architecture enables us to process videos captured by automated inspection vehicles and inspectors. By migrating our in-house–built models from traditional GPU-based instances to Inferentia, we were able to reduce costs by 50%. Moreover, we were able to see performance gains when comparing the times with a G4dn GPU instance. Our team is looking forward to running more workloads on the Inferentia-based Inf1 instances.

NTT PC developed AnyMotion, a motion analysis API platform service based on advanced posture estimation ML models. We deployed our AnyMotion platform on Amazon EC2 Inf1 instances using Amazon ECS for a fully managed container orchestration service. By deploying our AnyMotion containers on Amazon EC2 Inf1, we saw 4.5x higher throughout, a 25% lower inference latency, and 90% lower cost compared to current-generation GPU-based EC2 instances. These superior results will help to improve the quality of the AnyMotion service at scale.

The market of digital health platforms is growing at a remarkable rate. Gathering intelligence on this market is a challenging task due to the vast amounts of customer opinions data and its unstructured nature. Our application automates the generation of actionable insights from customer opinions via DL natural language models (Transformers). Our application is computationally intensive and needs to be deployed in a highly performant manner. We seamlessly deployed our DL inferencing workload onto Amazon EC2 Inf1 instances powered by the AWS Inferentia processor. The new Inf1 instances provide 2x higher throughput to GPU-based instances and allowed us to streamline our inference workloads.

Condé Nast's global portfolio encompasses over 20 leading media brands, including Wired, Vogue, and Vanity Fair. Within a few weeks, our team was able to integrate our recommendation engine with AWS Inferentia chips. This union enables multiple runtime optimizations for state-of-the-art natural language models on SageMaker's Inf1 instances. As a result, we observed a 72% reduction in cost than the previously deployed GPU instances.

Ciao is evolving conventional security cameras into high-performance analysis cameras equivalent to the capability of a human eye. Our application is advancing disaster prevention, monitoring environmental conditions using cloud-based AI camera solutions to alert before it becomes a disaster. Such alert enables reacting to the situation beforehand. Based on the object detection, we can also provide insight by estimating the number of incoming guests without staff from videos in brick and mortar stores. Ciao Camera commercially adopted AWS Inferentia-based Inf1 instances with 40% better price performance than G4dn with YOLOv4. We are looking forward to more of our services with Inf1 leveraging its significant cost efficiency.

The Asahi Shimbun is one of the most popular daily newspapers in Japan. Media Lab, established as one of our company's departments, has the missions to research the latest technology, especially AI, and connect the cutting-edge technologies for new businesses. With the launch of AWS Inferentia based Amazon EC2 Inf1 instances in Tokyo, we tested our PyTorch based text summarization AI application on these instances. This application processes a large amount of text and generates headlines and summary sentences trained on articles from the last 30 years. Using Inferentia, we lowered costs by an order of magnitude over CPU-based instances. This dramatic reduction in costs will enable us to deploy our most complex models at scale, which we previously believed was not economically feasible."

CS Disco is reinventing legal technology as a leading provider of AI solutions for e-discovery developed by lawyers for lawyers. Disco AI accelerates the thankless task of combing through terabytes of data, speeding up review times and improving review accuracy by leveraging complex NLP models, which are computationally expensive and cost-prohibitive. Disco has found that AWS Inferentia-based Inf1 instances reduce the cost of inference in Disco AI by at least 35% as compared with today's GPU instances. Based on this positive experience with Inf1 instances, CS Disco will explore opportunities for migration into Inferentia.

At Talroo, we provide our customers with a data-driven platform that enables them to attract unique job candidates so they can make hires. We are constantly exploring new technologies to ensure we offer the best products and services to our customers. Using Inferentia, we extract insights from a corpus of text data to enhance our AI-powered search-and-match technology. Talroo leverages Amazon EC2 Inf1 instances to create high-throughput NLU models with SageMaker. Talroo’s initial testing shows that the Amazon EC2 Inf1 instances deliver 40% lower inference latency and 2x higher throughput compared to G4dn GPU-based instances. Based on these results, Talroo looks forward to using Amazon EC2 Inf1 instances as part of its AWS infrastructure.

Digital Media Professionals (DMP) visualizes the future with a ZIA™ platform based on AI. DMP’s efficient computer vision classification technologies are used to build insight on large amounts of real-time image data, such as condition observation, crime prevention, and accident prevention. We recognized that our image segmentation models run four times faster on AWS Inferentia based Inf1 instances compared to GPU-based G4 instances. Due to this higher throughput and lower cost, Inferentia enables us to deploy our AI workloads, such as applications for car dashcams, at scale.

Since ML is core to our strategy, we were excited to try AWS Inferentia-based Inf1 instances. We found the Inf1 instances easy to integrate into our research and development pipeline. Most importantly, we observed impressive performance gains compared to the G4dn GPU-based instances. With our first model, the Inf1 instances yielded about 45% higher throughput and decreased cost per inference by almost 50%. We intend to work closely with the AWS team to port other models and shift most of our ML inference infrastructure to AWS Inferentia.

SkyWatch processes hundreds of trillions of pixels of earth observation data, captured from space every day. Adopting the new AWS Inferentia-based Inf1 instances using Amazon SageMaker for real-time cloud detection and image quality scoring was quick and easy. It was all a matter of switching the instance type in our deployment configuration. By switching instance types to Inferentia-based Inf1, we improved performance by 40% and decreased overall costs by 23%. This is a big win. It has enabled us to lower our overall operational costs while continuing to deliver high-quality satellite imagery to our customers, with minimal engineering overhead. We are looking forward to transitioning all of our inference endpoints and batch ML processes to use Inf1 instances to further improve our data reliability and customer experience.

Migrating our AI chatbot service to Amazon EC2 Inf1 instances was straightforward. We completed the migration within two months and launched a large-scale service on the Inf1 instances using Amazon ECS. We were able to reduce our inference latency by 97% and our inference costs by over 50% (over comparable GPU-based instances) by serving multiple models per Inf1 instance. We look forward to running more workloads on the Inferentia-based Inf1 instances.

For our text ad processing, we deploy PyTorch based BERT models globally on AWS Inferentia based Inf1 instances. By moving to Inferentia from GPUs, we were able to lower our cost by 69% with comparable performance. Compiling and testing our models for AWS Inferentia took less than three weeks. Using Amazon SageMaker to deploy our models to Inf1 instances ensured our deployment was scalable and easy to manage. When I first analyzed the compiled models, the performance with AWS Inferentia was so impressive that I actually had to re-run the benchmarks to make sure they were correct! Going forward, we plan to migrate our image ad processing models to Inferentia. We have already benchmarked 30% lower latency and 71% cost savings over comparable GPU-based instances for these models.

Amazon Alexa’s AI- and ML-based intelligence, powered by AWS, is available on more than 100 million devices today—and our promise to customers is that Alexa is always becoming smarter, more conversational, more proactive, and even more delightful. Delivering on that promise requires continuous improvements in response times and ML infrastructure costs, which is why we are excited to use Amazon EC2 Inf1 to lower inference latency and cost per inference on Alexa text-to-speech. With Amazon EC2 Inf1, we’ll be able to make the service even better for the tens of millions of customers who use Alexa each month.

Amazon Prime Video uses computer vision ML models to analyze video quality of live events to ensure an optimal viewer experience for Prime Video members. We deployed our image classification ML models on EC2 Inf1 instances and were able to see 4x improvement in performance and up to 40% savings in cost. We are now looking to leverage these cost savings to innovate and build advanced models that can detect more complex defects, such as synchronization gaps between audio and video files, to deliver more enhanced viewing experience for Prime Video members.

Amazon Rekognition is a simple and easy image and video analysis application that helps customers identify objects, people, text, and activities. Amazon Rekognition needs high-performance DL infrastructure that can analyze billions of images and videos daily for our customers. With AWS Inferentia-based Inf1 instances, running Amazon Rekognition models such as object classification resulted in 8x lower latency and 2x the throughput compared to running these models on GPUs. Based on these results, we are moving Amazon Rekognition to Inf1, enabling our customers to get accurate results faster.