Callminer

OCTOBER 11, 2018

FCR on social/text needs to be amended to first conversation resolution as customers rarely provide all info needed to resolve a query upfront, but measuring this provides a benchmark you can use against other channels. Smitha obtained her license as CPA in 2007 from the California Board of Accountancy. Reuben Kats @grab_results.

Call Center 561

Call Center Metrics Contact Center Wait times 561

AWS Machine Learning

MARCH 6, 2025

It simplifies data integration from various sources and provides tools for data indexing, engines, agents, and application integrations. Prerequisites To implement this solution, you need the following: An AWS account with privileges to create AWS Identity and Access Management (IAM) roles and policies.

Metrics 91

Metrics Enterprise APIs Engineering 91

AWS Machine Learning

JUNE 11, 2024

For example, for mixed AI workloads, the AI inference is part of the search engine service with real-time latency requirements. First, we had to experiment and benchmark in order to determine that Graviton3 was indeed the right solution for us. After that was confirmed, we had to perform the actual migration.

Engineering 98

Engineering Benchmark Accountability Best practices 98

AWS Machine Learning

MAY 2, 2024

A common way to select an embedding model (or any model) is to look at public benchmarks; an accepted benchmark for measuring embedding quality is the MTEB leaderboard. The Massive Text Embedding Benchmark (MTEB) evaluates text embedding models across a wide range of tasks and datasets.

Benchmark 122

Benchmark Metrics Enterprise APIs 122

AWS Machine Learning

NOVEMBER 15, 2024

Use cases we have worked on include: Technical assistance for field engineers – We built a system that aggregates information about a company’s specific products and field expertise. A chatbot enables field engineers to quickly access relevant information, troubleshoot issues more effectively, and share knowledge across the organization.

APIs 113

APIs Engineering Chatbots Construction 113

AWS Machine Learning

MAY 15, 2024

Acting as a model hub, JumpStart provided a large selection of foundation models and the team quickly ran their benchmarks on candidate models. Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). The Amazon API Gateway receives the PUT request (step 1).

Advertising 122

Advertising APIs Engineering Benchmark 122

AWS Machine Learning

SEPTEMBER 25, 2024

With a focus on responsible innovation and system-level safety, these new models demonstrate state-of-the-art performance on a wide range of industry benchmarks and introduce features that help you build a new generation of AI experiences. With SageMaker, you can streamline the entire model deployment process. 32xlarge Llama-3.2-1B-Instruct

Engineering 123

Engineering Benchmark Management Accountability 123

AWS Machine Learning

JULY 15, 2024

The buffer was implemented after benchmarking the captioning model’s performance. The benchmarking revealed that the model performed optimally when processing batches of images, but underperformed when analyzing individual images. About the authors Vlad Lebedev is a Senior Technology Leader at Mixbook.

Personalization 117

Personalization Engineering Benchmark Analytics 117

AWS Machine Learning

FEBRUARY 12, 2025

Prerequisites To build the solution yourself, there are the following prerequisites: You need an AWS account with an AWS Identity and Access Management (IAM) role that has permissions to manage resources created as part of the solution (for example AmazonSageMakerFullAccess and AmazonS3FullAccess ).

Scripts 76

Scripts Metrics Engineering Accountability 76

AWS Machine Learning

FEBRUARY 11, 2025

and run inference: An AWS account that will contain all your AWS resources. Recommended instances and benchmarks The following table lists all the Meta SAM 2.1 Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker AIs machine learning and generative AI hub.

Engineering 81

Engineering Construction Benchmark Healthcare 81

Spearline

JANUARY 6, 2022

A recent AVANT “6-12” report focusing on CCaaS notes that the CCaaS market currently accounts for more than $3 billion in global sales. But, many engineering teams have had their fire fighting experiences. One survey notes that responding CIOs indicate their use of on-premise applications dropped by more than 40% in 2021.

Call flow 122

Call flow Contact Center Benchmark contact center solutions 122

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. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

Metrics 128

Metrics Engineering Accountability Benchmark 128

Callminer

AUGUST 1, 2017

Smitha obtained her license as CPA in 2007 from the California Board of Accountancy. With more than 15 years of experience in business, finance and accounting, she is also responsible for implementing financial controls and processes. Set your goals (contact concurrency or resolution time, the percentage of first time resolution, etc.)

Contact Center 334

Contact Center Call Center Average Handle Time Real estate 334

AWS Machine Learning

JUNE 24, 2022

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally.

Enterprise 109

Enterprise Engineering Accountability APIs 109

AWS Machine Learning

MAY 10, 2023

We cover computer vision (CV), natural language processing (NLP), classification, and ranking scenarios for models and ml.c6g, ml.c7g, ml.c5, and ml.c6i SageMaker instances for benchmarking. You can use the sample notebook to run the benchmarks and reproduce the results. Mohan Gandhi is a Senior Software Engineer at AWS.

Benchmark 90

Benchmark Best practices Engineering Scripts 90

AWS Machine Learning

OCTOBER 17, 2023

SageMaker JumpStart allowed the team to experiment quickly with different models, running different benchmarks and tests, failing fast as needed. The Q&A chatbot likewise has its own AWS account for role separation, isolation, and ease of monitoring for security, cost, and compliance purposes. Jangwon Kim is a Sr.

Chatbots 123

Chatbots Customer Care Engineering Feedback 123

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 125

Healthcare Engineering APIs Benchmark 125

Totango

JULY 17, 2024

Three tricks we used to accomplish this include: Be RACI: Using a responsibility assignment matrix (aka a RACI—Responsible, Accountable, Consulted, Informed—matrix) has helped us to clearly define the roles and responsibilities between our CS and sales teams. At the same time, you can build out new opportunities to drive that value.

Sales 100

Sales Metrics Accountability Benchmark 100

AWS Machine Learning

MAY 9, 2024

By taking a proactive approach , the CoE provides ethical compliance but also builds trust, enhances accountability, and mitigates potential risks such as veracity, toxicity, data misuse, and intellectual property concerns. Platform – A central platform such as Amazon SageMaker for creation, training, and deployment.

Government 126

Government Best practices Benchmark Metrics 126

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. This balance must account for the assessment of risk in terms of several factors such as quality, disclosures, or reporting.

Best practices 130

Best practices Benchmark Transportation Engineering 130

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.” If your customer churn rate is higher than these benchmarks, chances are, your company would benefit greatly by redoubling its efforts on Customer Success.

Benchmark 98

Benchmark B2B Accountability Consulting 98

AWS Machine Learning

AUGUST 26, 2024

Our field organization includes customer-facing teams (account managers, solutions architects, specialists) and internal support functions (sales operations). Personalized content will be generated at every step, and collaboration within account teams will be seamless with a complete, up-to-date view of the customer.

Sales 119

Sales Accountability Feedback Metrics 119

AWS Machine Learning

MAY 22, 2024

Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. For details, see Creating an AWS account. We use Amazon SageMaker Studio with the ml.t3.medium

Scripts 128

Scripts Engineering Accountability APIs 128

AWS Machine Learning

FEBRUARY 24, 2025

Performance metrics and benchmarks According to Mistral, the instruction-tuned version of the model achieves over 81% accuracy on Massive Multitask Language Understanding (MMLU) with 150 tokens per second latency, making it currently the most efficient model in its category.

APIs 79

APIs Enterprise Benchmark Feedback 79

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 122

Engineering Accountability Construction APIs 122

ClearAction

DECEMBER 16, 2017

This may be related to a complicated deployment such as enterprise software, or peer-to-peer, such as engineers from the supplier and customer companies meeting to work out usage details, or a customer appointee who interfaces with multiple locations of the supplier company in a single morning. B2B Customer Experience: Do This, Not That.

B2B 86

B2B Customer Experience Benchmark Journey mapping 86

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. A customer success health score is a framework used to identify the status of your customers so you can quickly prioritize accounts. Properly Weigh Your Metrics.

Metrics 90

Metrics Upselling Accountability Enterprise 90

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 115

Engineering Chatbots Technical Support Best practices 115

Totango

AUGUST 23, 2022

Touchpoints may involve any medium you use to interact with customers, including: Search engine marketing. This may occur through encountering your brand or product through a search engine result, a search engine ad, a social media post, a video, a review on a technology website, word-of-mouth or other means. Blog content.

B2B 122

B2B Journey mapping SaaS Upselling 122

AWS Machine Learning

NOVEMBER 19, 2024

Leave the session inspired to bring Amazon Q Apps to supercharge your teams’ productivity engines. Learn how they created specialized agents for different tasks like account management, repos, pipeline management, and more to help their developers go faster.

APIs 88

APIs Enterprise Best practices Government 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. Products & Engineering. Let’s start with the basics.

Journey mapping 98

Journey mapping Benchmark Feedback Metrics 98

AWS Machine Learning

SEPTEMBER 25, 2023

You can fine-tune the following parameters in serving.properties of the LMI container for using continuous batching: engine – The runtime engine of the code. The following diagram shows the dynamic batching of requests with different input sequence lengths being processed together by the model. Use MPI to enable continuous batching.

Engineering 114

Engineering Benchmark Enterprise Management 114

AWS Machine Learning

SEPTEMBER 21, 2023

In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering. How can your generative AI project support sustainable innovation?

Best practices 120

Best practices Engineering Metrics APIs 120

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 116

Scripts Engineering Metrics Big data 116

AWS Machine Learning

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

Best practices 125

Best practices Engineering Chatbots Enterprise 125

AWS Machine Learning

FEBRUARY 8, 2023

The procedure is further simplified with the use of Inference Recommender , a right-sizing and benchmarking tool built inside SageMaker. However, you can use any other benchmarking tool. Benchmarking To derive the right scaling policy, the first step in the plan is to determine application behavior on the chosen hardware.

Benchmark 90

Benchmark Metrics APIs Engineering 90

AWS Machine Learning

APRIL 17, 2023

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

Engineering 93

Engineering Benchmark Scripts industry standards 93