AWS Machine Learning

SEPTEMBER 6, 2024

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

Best practices 82

Best practices Metrics Sales Benchmark 82

AWS Machine Learning

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. This makes it difficult to apply standard evaluation metrics like BERTScore ( Zhang et al.

Metrics 117

Metrics Engineering Accountability Benchmark 117

AWS Machine Learning

MAY 9, 2024

Benchmarking and metrics – Defining standardized metrics and benchmarking to measure and compare the performance of AI models, and the business value derived. Setting KPIs and metrics is pivotal to gauge effectiveness. Performance management Setting KPIs and metrics is pivotal to gauge effectiveness.

Government 115

Government Best practices Benchmark Metrics 115

AWS Machine Learning

DECEMBER 3, 2024

The challenge: Resolving application problems before they impact customers New Relic’s 2024 Observability Forecast highlights three key operational challenges: Tool and context switching – Engineers use multiple monitoring tools, support desks, and documentation systems. New Relic AI conducts a comprehensive analysis of the checkout service.

Customer Experience 89

Customer Experience Engineering Enterprise Benchmark 89

Totango

JULY 17, 2024

Chris Dishman Stop guessing, start growing: The customer success metrics that matter If you’re only tracking metrics like usage or churn, then you’re only seeing a small piece of the puzzle. When you track outcome-based metrics that help prove value to your customers, then you can proactively identify areas for growth and expansion.

Sales 97

Sales Metrics Accountability Benchmark 97

AWS Machine Learning

FEBRUARY 8, 2023

In this option, you select an ideal value of an Amazon CloudWatch metric of your choice, such as the average CPU utilization or throughput that you want to achieve as a target, and SageMaker will automatically scale in or scale out the number of instances to achieve the target metric. However, you can use any other benchmarking tool.

Benchmark 79

Benchmark Metrics APIs Engineering 79

AWS Machine Learning

MAY 13, 2024

In this part of the blog series, we review techniques of prompt engineering and Retrieval Augmented Generation (RAG) that can be employed to accomplish the task of clinical report summarization by using Amazon Bedrock. Prompt engineering helps to effectively design and improve prompts to get better results on different tasks with LLMs.

Healthcare 114

Healthcare Engineering APIs Benchmark 114

AWS Machine Learning

JULY 24, 2024

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. To get started, see this guide. __ About the Authors Pawan Agarwal is the Senior Director of Software Engineering at Salesforce. Salesforce, Inc.

Engineering 112

Engineering CRM SaaS Sales 112

Totango

APRIL 11, 2019

An illuminated “check engine” light is scary because it doesn’t offer any solution. For many, “check engine” may as well just say “car broken”—and that’s terrifying. These categories are also represented numerically as a relative rating out of 100 based on a combination of established metric thresholds.

Metrics 88

Metrics Upselling Accountability Enterprise 88

ChurnZero

AUGUST 25, 2021

It will help you set benchmarks to get a clear picture of your performance with your customers. A Net Promoter Score (NPS) is a customer satisfaction benchmark that measures how likely your customers are to recommend you to a friend or colleague. Keeping track of this metric can also help reduce support possibly needed in the future.

Journey mapping 98

Journey mapping Benchmark Feedback Metrics 98

AWS Machine Learning

APRIL 22, 2024

Figure 5 offers an overview on generative AI modalities and optimization strategies, including prompt engineering , Retrieval Augmented Generation , and fine-tuning or continued pre-training. Establish a metrics pipeline to provide insights into the sustainability contributions of your generative AI initiatives.

Best practices 119

Best practices Benchmark Transportation Engineering 119

ChurnZero

MARCH 31, 2023

If the vendor has been smart enough to collect aggregate data about how its customers use the product or service, it can also offer useful benchmark metrics to bolster that guidance.” Among companies that do measure the experience, those metrics rarely connect with the sales process.” A case in point?

Benchmark 98

Benchmark B2B Accountability Consulting 98

AWS Machine Learning

NOVEMBER 15, 2024

All text-to-image benchmarks are evaluated using Recall@5 ; text-to-text benchmarks are evaluated using NDCG@10. Text-to-text benchmark accuracy is based on BEIR, a dataset focused on out-of-domain retrievals (14 datasets). Generic text-to-image benchmark accuracy is based on Flickr and CoCo.

Benchmark 87

Benchmark Enterprise Construction Engineering 87

AWS Machine Learning

APRIL 20, 2023

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. An advanced job is a custom load test job that allows you to perform extensive benchmarks based on your ML application SLA requirements, such as latency, concurrency, and traffic pattern.

APIs 80

APIs Metrics Benchmark Engineering 80

AWS Machine Learning

JUNE 17, 2024

PrestoDB is an open source SQL query engine that is designed for fast analytic queries against data of any size from multiple sources. For more information on the TPC-H data, its database entities, relationships, and characteristics, refer to TPC Benchmark H. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB.

Scripts 106

Scripts Engineering Metrics Big data 106

AWS Machine Learning

NOVEMBER 22, 2023

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 101

APIs Metrics Benchmark Enterprise 101

AWS Machine Learning

JUNE 20, 2023

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.

Benchmark 75

Benchmark Engineering Banking Chatbots 75

AWS Machine Learning

JANUARY 19, 2024

The concepts illustrated in this post can be applied to applications that use PLM features, such as recommendation systems, sentiment analysis, and search engines. The performance of the architecture is typically measured using metrics such as validation loss. training.py ).

Metrics 106

Metrics Scripts Benchmark Enterprise 106

AWS Machine Learning

MAY 30, 2023

Benchmarks We benchmarked evaluation metrics to ensure that the model quality didn’t deteriorate with the multi-GPU training path compared to single-GPU training. We also benchmarked on large datasets to ensure that our distributed GPU setups were performant and scalable. 8xlarge 15 1679 15.22 17 1509 15.51 19 1326 15.22

Benchmark 82

Benchmark Metrics Engineering Enterprise 82

aircall

JUNE 28, 2022

By the way, transparency is at the center of our values, that is why for the past eight years we’ve been communicating regularly to our entire team our most important metrics, our ARR, our NPS, our profit margins, and more. At this moment, we introduce NPS as our second “Core Metric That Matters” next to MRR growth. The Maturity.

SaaS 147

SaaS Metrics Engineering Telecommunications 147

AWS Machine Learning

OCTOBER 4, 2022

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.

Benchmark 67

Benchmark Engineering Metrics Technology 67

AWS Machine Learning

MAY 7, 2024

Rather than requiring extensive feature engineering and dataset labeling, LLMs can be fine-tuned on small amounts of domain-specific data to quickly adapt to new use cases. This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain.

Engineering 105

Engineering Technical Support Chatbots Best practices 105

ClearAction

MARCH 27, 2021

NPS) and customer service and success metrics are about your firm, product or people. Benchmark: Plot all available solutions by customers’ desired outcomes. Root cause analysis may identify engineering issues, service skill gaps, business process complications, or outdated policies.

Customer Experience 77

Customer Experience Metrics Benchmark Advertising 77

AWS Machine Learning

AUGUST 26, 2024

This involves benchmarking new models against our current selections across various metrics, running A/B tests, and gradually incorporating high-performing models into our production pipeline. As new models become available on Amazon Bedrock, we have a structured evaluation process in place.

Sales 109

Sales Accountability Feedback Metrics 109

AWS Machine Learning

MARCH 30, 2023

Benchmarking and evaluating the Snapper tool In practice, we find that the Snapper tool streamlines the bounding box annotation task and is very intuitive for users to pick up. Here, we apply these metrics to the validation set from the official MS-COCO dataset used for training. The following table summarizes our findings.

Engineering 83

Engineering Metrics Benchmark Advertising 83

AWS Machine Learning

SEPTEMBER 11, 2024

Each GPC has a raster engine for graphics and several TPCs. The NeuronCores contain four engines : the first three include a ScalarEngine for scalar calculations, a VectorEngine for vector calculations, and a TensorEngine for matrix calculations. And finally, there is a C++ programmable GPSIMD-engine allowing for custom operations.

Benchmark 85

Benchmark Banking Analytics Big data 85

AWS Machine Learning

MAY 29, 2024

The prospect of fine-tuning open source multimodal models like LLaVA are highly appealing because of their cost effectiveness, scalability, and impressive performance on multimodal benchmarks. The following is an example metric definition that logs training loss at each epoch, the model’s learning rate, and training throughput.

Scripts 112

Scripts Healthcare Metrics Finance 112

AWS Machine Learning

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Engineering 112

Engineering Accountability Construction APIs 112

SharpenCX

MARCH 24, 2020

But, we can’t know how we compare without some kind of standard — a grade scale, a rubric, a metric. Metrics in the workplace are incredibly helpful. According to Gallup’s Re-Engineering Performance Management research, measurement is a positive pillar for developing employees. Setting a Standard. Customer Satisfaction.

industry standards 52

industry standards Metrics Call Center Wait times 52

AWS Machine Learning

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. Forethought had to manage model inference on Amazon EKS ourselves, which was a burden on engineering efficiency. 2xlarge instances. seconds Large (550 tokens) 12.7

APIs 68

APIs Benchmark Engineering Management 68

AWS Machine Learning

NOVEMBER 1, 2024

We also provide insights on how to achieve optimal results for different dataset sizes and use cases, backed by experimental data and performance metrics. The evaluation metric is the F1 score that measures the word-to-word matching of the extracted content between the generated output and the ground truth answer.

Best practices 83

Best practices APIs Finance Engineering 83

AWS Machine Learning

OCTOBER 5, 2022

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

Calibration 94

Calibration Engineering Automotive Analytics 94

AWS Machine Learning

AUGUST 3, 2022

To showcase how this reduction can help you getting started with the creation of a custom entity recognizer, we ran some tests on a few open-source datasets and collected performance metrics. In this post, we walk you through the benchmarking process and the results we obtained while working on subsampled datasets. Dataset preparation.

Benchmark 67

Benchmark APIs Metrics Scripts 67

Pointillist

JULY 25, 2018

Success Metrics for the Team. Ultimately, the biggest success metric for the Champion is to be able to show the Executive Sponsor and key Stakeholders that real business value has been gained through the use of customer journey analytics. Success Metrics for the Project. Success Metrics for the Business. Churn Rate.

Analytics 93

Analytics Government Metrics APIs 93

Help Scout

JULY 20, 2021

This could look like a customer success team choosing to focus on churn and expansion metrics rather than product adoption scores and the stories they tell. Setting up benchmarks for the standards you’d like to achieve with your customers is an essential step for deepening your customer orientation.

Sales 145

Sales Feedback Metrics Surveys 145

AWS Machine Learning

APRIL 8, 2024

In this post, we explore the latest features introduced in this release, examine performance benchmarks, and provide a detailed guide on deploying new LLMs with LMI DLCs at high performance. TensorRT-LLM requires models to be compiled into efficient engines before deployment. For more details, refer to the GitHub repo.

Engineering 92

Engineering Calibration APIs Enterprise 92

AWS Machine Learning

MAY 8, 2023

To serve models, Triton supports various backends as engines to support the running and serving of various ML models for inference. With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Import the ONNX model into TensorRT and generate the TensorRT engine.

Engineering 85

Engineering APIs Best practices Scripts 85