article thumbnail

Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The Amazon D&C team implemented the solution in a pilot for Amazon engineers and collected user feedback. of overall responses) can be addressed by user education and prompt engineering.

article thumbnail

Customer Satisfaction Score (CSAT) Industry Benchmarks

GetFeedback

A new list of benchmarks is published each year by ACSI, with minor quarterly updates. . Below is the complete list of the newest CSAT benchmarks. Internet Search Engines and Information: 79%. Click here to download the current industry benchmarks. According to the ACSI, the current overall U.S. Airlines: 73%. Banks: 81%.

Benchmark 117
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

7 Strategies to Benchmark SaaS Customers to Success

Amity

Customer benchmarking — the practice of identifying where a customer can improve or is already doing well by comparing to other customers – helps Customer Success Managers to deliver unique value to their customers. I’ve found that SaaS vendors use seven distinct strategies to empower CSMs with customer benchmarking.

article thumbnail

Enable faster training with Amazon SageMaker data parallel library

AWS Machine Learning

Model training benchmarks In large-scale training jobs where GPU communication is a significant bottleneck, SMDDP can markedly improve training speeds, as measured by model TFLOPS/GPU. Karan Dhiman is a Software Development Engineer at AWS, based in Toronto, Canada. 24xlarge nodes (512 NVIDIA A100 GPUs) PyTorch FSDP 97.89

article thumbnail

Reduce energy consumption of your machine learning workloads by up to 90% with AWS purpose-built accelerators

AWS Machine Learning

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators.

article thumbnail

Achieve four times higher ML inference throughput at three times lower cost per inference with Amazon EC2 G5 instances for NLP and CV PyTorch models

AWS Machine Learning

With G5 instances, ML customers get high performance and a cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing (NLP), computer vision (CV), and recommender engine use cases. Benchmarking approach. We also study the impact of full precision vs. mixed precision.

article thumbnail

Build well-architected IDP solutions with a custom lens – Part 4: Performance efficiency

AWS Machine Learning

As data and system conditions change, the model performance and efficiency metrics are tracked to ensure retraining is performed when needed. Your organization can choose the retraining mechanism—it can be quarterly, monthly, or based on science metrics, such as when accuracy drops below a given threshold.

APIs 91