AWS Machine Learning

FEBRUARY 29, 2024

framework/modelmetrics/ – This directory contains a Python script that creates an Amazon SageMaker Processing job for generating a model metrics JSON report for a trained model based on results of a SageMaker batch transform job performed on test data. The model_unit.py script is used by pipeline_service.py The pipeline_service.py

Scripts 124

Scripts Engineering Metrics Best practices 124

AWS Machine Learning

JULY 29, 2022

Customer satisfaction is a potent metric that directly influences the profitability of an organization. Reviewing the Account Balance chatbot. As an example, this demo deploys a bot to perform three automated tasks, or intents : Check Balance , Transfer Funds , and Open Account. Account Type. Deploying the solution.

Contact Center 131

Contact Center Interactive Voice Response Self service Accountability 131

AWS Machine Learning

FEBRUARY 1, 2024

Ingesting from these sources is different from the typical data sources like log data in an Amazon Simple Storage Service (Amazon S3) bucket or structured data from a relational database. In the low-latency case, you need to account for the time it takes to generate the embedding vectors.

Best practices 135

Best practices Engineering SaaS APIs 135

TechSee

AUGUST 7, 2024

Today, CXA encompasses various technologies such as AI, machine learning, and big data analytics to provide personalized and efficient customer experiences. Over time, additional interactive solutions like IVR systems added the ability to automate basic queries like account balances or simple troubleshooting.

Customer Experience 124

Customer Experience Customer Service Interactive Voice Response Chatbots 124

AWS Machine Learning

OCTOBER 2, 2024

Provide control through transparency of models, guardrails, and costs using metrics, logs, and traces The control pillar of the generative AI framework focuses on observability, cost management, and governance, making sure enterprises can deploy and operate their generative AI solutions securely and efficiently.

Government 127

Government APIs Enterprise Best practices 127

Totango

DECEMBER 1, 2021

An agile approach brings the full power of big data analytics to bear on customer success. This provides transparency and accountability and empowers a data-driven approach to customer success. This should reference your KPI metrics and lay out a path to achieve each. Define How to Measure Success.

Government 103

Government Journey mapping Accountability Benchmark 103

Calltools

DECEMBER 12, 2018

Metrics drive the success of any call center. In today’s IoT (Internet of Things) landscape, analyzing big data is now a crucial factor that must be embraced by call centers for collections, customer service, and sales. This accelerates your conversion cycle and improves your metrics. How does this work?

Metrics 45

Metrics Analytics Call Center Sales 45

AWS Machine Learning

JUNE 17, 2024

Prerequisites To implement the solution provided in this post, you should have an AWS account , a SageMaker domain to access Amazon SageMaker Studio , and familiarity with SageMaker, Amazon S3, and PrestoDB. to set up PrestoDB on an Amazon Elastic Compute Cloud (Amazon EC2) instance in your account.

Scripts 119

Scripts Engineering Metrics Big data 119

AWS Machine Learning

JUNE 3, 2024

For Objective metric , leave as the default F1. F1 averages two important metrics: precision and recall. Review model metrics Let’s focus on the first tab, Overview. The advanced metrics suggest we can trust the resulting model. Choose Configure model to set configurations. For Training method , select Auto.

Surveys 123

Surveys Metrics Engineering Analytics 123

ClearAction

JUNE 8, 2015

Account teams, customer service and accounts receivable departments, customer reference managers, market researchers and others throughout the company are a loose confederation of a CX team. Teams must meet often to checkpoint key metric: "Are customers truly happy with us?" " — @jameskobielus.

Customer Experience 94

Customer Experience Chief Customer Officer Customer centricity Employee engagement 94

AWS Machine Learning

FEBRUARY 2, 2023

The player data was used to derive features for model development: X – Player position along the long axis of the field Y – Player position along the short axis of the field S – Speed in yards/second; replaced by Dis*10 to make it more accurate (Dis is the distance in the past 0.1

Calibration 98

Calibration Engineering Big data Automotive 98

AWS Machine Learning

FEBRUARY 13, 2024

Before moving to full-scale production, BigBasket tried a pilot on SageMaker to evaluate performance, cost, and convenience metrics. Use SageMaker Distributed Data Parallelism (SMDDP) for accelerated distributed training. Log model training metrics. Use a custom PyTorch Docker container including other open source libraries.

Engineering 130

Engineering Gamification Big data Scripts 130

AWS Machine Learning

NOVEMBER 9, 2023

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 119

Scripts Accountability Best practices Engineering 119

AWS Machine Learning

MAY 3, 2023

The managed cluster, instances, and containers report metrics to Amazon CloudWatch , including usage of GPU, CPU, memory, GPU memory, disk metrics, and event logging. It was designed to restart and scale up the SageMaker Processing cluster based on performance metrics observed using Lambda functions monitoring the jobs.

Engineering 102

Engineering Metrics APIs Big data 102

ClearAction

MAY 2, 2016

Other marketing maturity models are holistic, it seems, yet the approach taken is stymied because of moving targets in emerging marketing practices, such as the advent of big data or digital marketing, which weren’t on the horizon of yesteryear. Accountability = maximize resources. Marketing Maturity Model Litmus Test.

Marketing 62

Marketing Accountability Enterprise Metrics 62

AWS Machine Learning

FEBRUARY 21, 2023

MLOps includes practices that integrate ML workloads into release management, CI/CD, and operations, accounting for the unique aspects of ML projects, including considerations for deploying and monitoring models. A/B testing is used in scenarios where closed loop feedback can directly tie model outputs to downstream business metrics.

Best practices 97

Best practices Accountability Metrics Engineering 97

AWS Machine Learning

JULY 21, 2022

As a result, this experimentation phase can produce multiple models, each created from their own inputs (datasets, training scripts, and hyperparameters) and producing their own outputs (model artifacts and evaluation metrics). We also illustrate how you can track your pipeline workflow and generate metrics and comparison charts.

Metrics 98

Metrics Scripts Enterprise Telecommunications 98

AWS Machine Learning

DECEMBER 1, 2022

With the SageMaker Python SDK, you can seamlessly update the Model card with evaluation metrics. Model cards provide model risk managers, data scientists, and ML engineers the ability to perform the following tasks: Document model requirements such as risk rating, intended usage, limitations, and expected performance. Conclusion.

Government 100

Government Metrics APIs Engineering 100

AWS Machine Learning

JUNE 24, 2022

After the data scientists have proven that ML can solve the business problem and are familiarized with SageMaker experimentation, training, and deployment of models, the next step is to start productionizing the ML solution. In the same account, Amazon SageMaker Feature Store can be hosted, but we don’t cover it this post.

Enterprise 119

Enterprise Engineering Accountability APIs 119

AWS Machine Learning

JULY 19, 2023

This enables you to establish a single source of truth for your registered model versions, with comprehensive and standardized documentation across all stages of the model’s journey on SageMaker, facilitating discoverability and promoting governance, compliance, and accountability throughout the model lifecycle.

Government 98

Government Best practices Metrics Enterprise 98

ChurnZero

JULY 22, 2022

The webinar’s Q&A session covered popular onboarding questions in SaaS like how long it should take a customer to reach first value, what should you do when a customer disengages, and how to hold customers accountable at scale. For example, customers aren’t accountable. Now, you’re not accountable.

Gamification 98

Gamification Accountability B2B Enterprise 98

TechSee

DECEMBER 5, 2017

Fully customizable, Enchant includes features such as unlimited Help Desk Inboxes, smart folders that update in real time, multiple knowledge base sites with their own set of articles, multiple messengers in a single account with each pointing to a different team or configured for a different website.

Contact Center 116

Contact Center Technology Self service Call Center 116

AWS Machine Learning

AUGUST 21, 2024

Prerequisites Before you can start using the SageMaker and Amazon DataZone integration, you must have the following: An AWS account with appropriate permissions to create and manage resources in SageMaker and Amazon DataZone. An Amazon DataZone domain and an associated Amazon DataZone project configured in your AWS account.

Government 119

Government Banking Engineering Enterprise 119

AWS Machine Learning

AUGUST 22, 2023

However, sometimes due to security and privacy regulations within or across organizations, the data is decentralized across multiple accounts or in different Regions and it can’t be centralized into one account or across Regions. Each account or Region has its own training instances.

Scripts 98

Scripts Accountability Transportation Benchmark 98

AWS Machine Learning

SEPTEMBER 21, 2022

To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. When looking at these metrics, business analysts often identify patterns in customer behavior, in order to determine whether the company risks losing the customer. Choose Visualize.

Metrics 98

Metrics Marketing Big data Banking 98

AWS Machine Learning

MAY 10, 2023

Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the prediction of future events. Choose Next: Tags.

Accountability 100

Accountability Enterprise Big data Management 100

Lumoa

SEPTEMBER 30, 2022

According to Samsung, 77% of customers still seek in-person assistance when facing an unusual or complex account issue. . Improving Products and Services Through Big Data. Big data, which is the vast amount of information collected from different customer touchpoints, has already fueled the growth of the financial industry. .

Banking 88

Banking Customer Experience Big data Chatbots 88

TechSee

MARCH 7, 2019

Companies that measure and hold reps accountable for the overall outcome of an episode, considering the emotional impact on customers, the frequency of the issue and overall company costs, find that they earn the loyalty of both their customers and agents. Gamification.

Gamification 104

Gamification Contact Center Agent Empowerment Self service 104

AWS Machine Learning

NOVEMBER 24, 2023

In contrast, the data science and analytics teams already using AWS directly for experimentation needed to also take care of building and operating their AWS infrastructure while ensuring compliance with BMW Group’s internal policies, local laws, and regulations. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

Engineering 105

Engineering Analytics Self service Government 105

AWS Machine Learning

OCTOBER 27, 2022

After the predictors have been created, we evaluated their quality metrics in the predictors dashboard. For customized evaluation and analysis, you can also export the forecasted values to evaluate predictor quality metrics. We use the AutoPredictor API, which is also accessible through the Forecast console.

APIs 98

APIs Best practices Metrics Government 98

AWS Machine Learning

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account).

Scripts 98

Scripts APIs Enterprise Accountability 98

AWS Machine Learning

DECEMBER 21, 2022

The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Table formats provide a way to abstract data files as a table. Conclusion.

Scripts 98

Scripts Engineering APIs Big data 98

Balto

JANUARY 14, 2025

But modern analytics goes beyond basic metricsit leverages technologies like call center data science, machine learning models, and big data to provide deeper insights. Predictive Analytics: Uses historical data to forecast future events like call volumes or customer churn.

Call Center 52

Call Center Analytics Average Handle Time Big data 52

ClearAction

MAY 16, 2016

Other marketing maturity models cover the whole enchilada, so to speak, yet the approach taken is stymied because of moving targets in emerging marketing practices, such as the advent of big data or digital marketing, which weren’t on the horizon of yesteryear. Accountability = maximize resources. No one is exempt.

Marketing 54

Marketing Customer Experience Enterprise Metrics 54

AWS Machine Learning

MAY 8, 2023

How to use MLflow as a centralized repository in a multi-account setup. Prerequisites Before deploying the solution, make sure you have access to an AWS account with admin permissions. Multi-account considerations Data science workflows have to pass multiple stages as they progress from experimentation to production.

APIs 97

APIs Government Accountability Scripts 97

NobelBiz

APRIL 30, 2024

This evolution has been driven by advancements in machine learning, natural language processing, and big data analytics. Predictive Analytics This technology leverages data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Call Center 105

Call Center Chatbots Virtual Agent Contact Center 105

NobelBiz

APRIL 30, 2024

This evolution has been driven by advancements in machine learning, natural language processing, and big data analytics. Predictive Analytics This technology leverages data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Call Center 104

Call Center Chatbots Virtual Agent Contact Center 104