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

FEBRUARY 1, 2024

There are unique considerations when engineering generative AI workloads through a resilience lens. Make sure to validate prompt input data and prompt input size for allocated character limits that are defined by your model. If you’re performing prompt engineering, you should persist your prompts to a reliable data store.

Best practices 120

Best practices Engineering SaaS APIs 120

AWS Machine Learning

MARCH 15, 2024

Randy has held a variety of positions in the technology space, ranging from software engineering to product management. He entered the big data space in 2013 and continues to explore that area. Prior to joining AWS, Arnab was a technology leader and previously held architect and engineering leadership roles.

Healthcare 110

Healthcare Big data Engineering Accountability 110

AWS Machine Learning

FEBRUARY 29, 2024

This enables data scientists to quickly build and iterate on ML models, and empowers ML engineers to run through continuous integration and continuous delivery (CI/CD) ML pipelines faster, decreasing time to production for models. Solution overview The proposed framework code starts by reading the configuration files. The model_unit.py

Scripts 101

Scripts Engineering Metrics Best practices 101

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 109

Engineering Gamification Big data Scripts 109

AWS Machine Learning

OCTOBER 2, 2024

This is often referred to as platform engineering and can be neatly summarized by the mantra “You (the developer) build and test, and we (the platform engineering team) do all the rest!” Amazon Bedrock is compatible with robust observability features to monitor and manage ML models and applications.

Government 105

Government APIs Enterprise Best practices 105

AWS Machine Learning

JUNE 17, 2024

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. The evaluation step uses the evaluation script as a code entry.

Scripts 106

Scripts Engineering Metrics Big data 106

AWS Machine Learning

JUNE 25, 2024

A new automatic dashboard for Amazon Bedrock was added to provide insights into key metrics for Amazon Bedrock models. From here you can gain centralized visibility and insights to key metrics such as latency and invocation metrics. Optionally, you can select a specific model to isolate the metrics to one model.

Metrics 104

Metrics APIs Government Big data 104

ClearAction

APRIL 18, 2015

Use group sharing engines to share documents with strategies and knowledge across departments. Focus employee metrics more on CX enabling behaviors, less on survey ratings. Data can be insightful to all of the roles HR takes on in facilitating the company’s CX goals. Here are ways HR can help: Knowledge Management.

Customer Experience 96

Customer Experience Employee engagement Big data Customer centricity 96

AWS Machine Learning

AUGUST 21, 2024

Amazon DataZone allows you to create and manage data zones , which are virtual data lakes that store and process your data, without the need for extensive coding or infrastructure management. The data publisher is responsible for publishing and governing access for the bespoke data in the Amazon DataZone business data catalog.

Government 101

Government Banking Engineering Enterprise 101

AWS Machine Learning

NOVEMBER 14, 2024

The DS uses SageMaker Training jobs to generate metrics captured by , selects a candidate model, and registers the model version inside the shared model group in their local model registry. After you have completed the data preparation step, it’s time to train the classification model.

Government 78

Government Management APIs Accountability 78

AWS Machine Learning

JUNE 3, 2024

Prepare your data As expected in the ML process, your dataset may require transformations to address issues such as missing values, outliers, or perform feature engineering prior to model building. SageMaker Canvas provides ML data transforms to clean, transform, and prepare your data for model building without having to write code.

Surveys 99

Surveys Metrics Analytics Engineering 99

Lumoa

MAY 16, 2024

How to Improve the Data Science and LLM Development Process Anyone who has worked in data science will recognize this familiar scene: a clever data scientist copies code from a Git repository to a Jupyter notebook , makes some tweaks, and then pastes it back to push it towards production. and “It cannot be happening!”

Engineering 78

Engineering Banking Metrics Big data 78

AWS Machine Learning

SEPTEMBER 9, 2022

This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform. The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022.

Personalization 72

Personalization APIs Metrics Engineering 72

AWS Machine Learning

NOVEMBER 13, 2024

With the unification of SageMaker Model Cards and SageMaker Model Registry, architects, data scientists, ML engineers, or platform engineers (depending on the organization’s hierarchy) can now seamlessly register ML model versions early in the development lifecycle, including essential business details and technical metadata.

Government 90

Government Engineering Metrics APIs 90

BlueOcean

DECEMBER 18, 2018

In their answers to the following questions, they should be addressing chatbots, self-service, machine learning, big data, and more. AI can be a powerful tool, but it is just one cog in the customer care engine. 5 What KPIs/metrics do you measure in tracking the effectiveness of your escalations from AI to live agent?

Contact Center 78

Contact Center Chatbots outsourcing Metrics 78

AWS Machine Learning

OCTOBER 16, 2024

To evaluate the transcription accuracy quality, the team compared the results against ground truth subtitles on a large test set, using the following metrics: Word error rate (WER) – This metric measures the percentage of words that are incorrectly transcribed compared to the ground truth. A lower MER signifies better accuracy.

Engineering 101

Engineering Metrics APIs Big data 101

AWS Machine Learning

SEPTEMBER 9, 2022

Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics. Solution overview.

Analytics 69

Analytics Engineering Metrics APIs 69

AWS Machine Learning

FEBRUARY 2, 2023

The data distribution for punt and kickoff are different. Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. As a baseline, we used the model that won our NFL Big Data Bowl competition on Kaggle.

Calibration 74

Calibration Engineering Big data Automotive 74

Calabrio

APRIL 25, 2017

One of the most confounding challenges for modern contact center leaders is reporting on any performance metric that requires information from more than one system or application, each of which is a self-contained silo of data. Traditionally contact centers have been dealing with two main data challenges. Real-time data.

Multichannel 56

Multichannel Contact Center Big data Metrics 56

AWS Machine Learning

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. The predicted value indicates the expected value for our target metric based on the training data.

Metrics 101

Metrics Analytics APIs Big data 101

AWS Machine Learning

DECEMBER 18, 2023

When the model is ready, select the model and click on the latest version Review the model metrics and column impact and if you are satisfied with the model performance, click Predict. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

Finance 113

Finance APIs Analytics Big data 113

AWS Machine Learning

DECEMBER 21, 2022

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation.

Scripts 73

Scripts Engineering APIs Big data 73

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.

Government 79

Government Metrics APIs Engineering 79

Tenfold - Contact Center Blog

JUNE 19, 2018

Big Data and CX. The Myth: In order to improve customer experience, you have to invest in big data processing. You’ve probably already heard quite a lot about big data. That is why most companies hire specialized big data engineers who are able to go through it and obtain useful information.

Big data 56

Big data Chatbots Metrics Marketing 56

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 99

Enterprise Engineering Accountability APIs 99

AWS Machine Learning

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

Engineering 79

Engineering Analytics Self service Government 79

AWS Machine Learning

NOVEMBER 14, 2023

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data.

Management 98

Management Best practices APIs Calibration 98

Natalie Petouhof

OCTOBER 12, 2016

DPM vendors help both the business and engineering teams to not only define conversion and revenue goals but also make sure they are reached. Big data monitoring and data visualization  . When DPM is optimized, companies can deliver an engaging digital experience, maximize revenue and improve brand loyalty.

Management 55

Management Big data Analytics Engineering 55

AWS Machine Learning

JULY 14, 2022

The notebook also provides a sample dataset to run Fiddler’s explainability algorithms and as a baseline for monitoring metrics. Danny Brock is a Sr Solutions Engineer at Fiddler AI. He founded his own big data analytics consulting company, Branchbird, in 2012. Rajeev Govindan is a Sr Solutions Engineer at Fiddler AI.

Enterprise 75

Enterprise Engineering Analytics Big data 75

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 77

Metrics Scripts Enterprise Telecommunications 77

SharpenCX

OCTOBER 7, 2022

In an AI exercise, if the vendor keeps asking for more and more data, that is a red flag. It basically means that the lab-version of their model, with your data, is not seeing results. Start asking for model validation graphs on contact center performance metrics. Be wary of “statistics-speak”. Keep perspective.

Contact Center 67

Contact Center Contact center software Big data Engineering 67

AWS Machine Learning

SEPTEMBER 30, 2022

Security is a big-data problem. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric for the given ML task. 24xlarge and x1.32xlarge) to train the model on as large of a sample of the data as possible. The following diagram illustrates the solution architecture.

Scripts 80

Scripts Engineering Metrics Big data 80

AWS Machine Learning

JULY 29, 2022

Customer satisfaction is a potent metric that directly influences the profitability of an organization. Node.js > 16 – Open-source JavaScript backend engine for application development and deployment. Vamshi is focused on Language AI and innovates in building world-class recommender engines. on an Amazon EC2 Instance.

Contact Center 105

Contact Center Interactive Voice Response Self service Accountability 105

Horizon CX

JULY 20, 2018

Big Data and CX. The Myth: In order to improve customer experience, you have to invest in big data processing. You’ve probably already heard quite a lot about big data. That is why most companies hire specialized big data engineers who are able to go through it and obtain useful information.

Big data 8

Big data Chatbots Metrics Marketing 8

AWS Machine Learning

SEPTEMBER 9, 2022

Consequently, maintaining and augmenting older projects required more engineering time and effort. In addition, as new data scientists joined the team, knowledge transfers and onboarding took more time, because synchronizing local environments included many undocumented dependencies. Logging and monitoring. Kubeflow Logging.

Engineering 75

Engineering Healthcare APIs Scripts 75

Calabrio

APRIL 25, 2017

One of the most confounding challenges for modern contact center leaders is reporting on any performance metric that requires information from more than one system or application, each of which is a self-contained silo of data. Traditionally contact centers have been dealing with two main data challenges. Real-time data.

Multichannel 40

Multichannel Contact Center Big data Metrics 40