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 120

Government APIs Enterprise Best practices 120

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 90

Government Management APIs Accountability 90

AWS Machine Learning

NOVEMBER 7, 2024

A high-level description of the markdown pricing algorithm solution can be broken down into four steps: Discount-dependent forecast – Using past data, forecast future discount-dependent quantities that are relevant for determining the future profit of an item. Return rate – What share of sold items will be returned by the customer?

Engineering 108

Engineering Automotive Finance Government 108

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

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 117

Scripts Engineering Metrics Best practices 117

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 118

Government Banking Engineering Enterprise 118

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 120

Healthcare Big data Engineering Accountability 120

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 121

Metrics APIs Government Big data 121

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 122

Scripts Engineering Metrics Big data 122

Callminer

NOVEMBER 6, 2019

Big data is getting bigger with each passing year, but making sense of trends hidden deep in the heap of 1s and 0s is more confounding than ever. As metrics pile up, you may find yourself wondering which data points matter and in what ways they relate to your business’s interests.

Big data 140

Big data Analytics Metrics Sales 140

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 133

Best practices Engineering SaaS Accountability 133

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 124

Engineering Gamification Scripts Big data 124

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 84

Personalization APIs Metrics Engineering 84

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 115

Surveys Metrics Analytics Engineering 115

Branch Mesenger

MAY 2, 2018

Using Big Data to Make Leadership Advances in the Workplace. While surveys that lead to these results are historically what we’ve had to understand engagement metrics, analytics are far more important. According to a recent Gallup poll, 13 percent of employees are actively engaged at work.

Engineering 74

Engineering Morale Employee engagement Big data 74

Beyond Philosophy

JULY 5, 2016

Create experiences that are proactively human-engineered. Within customer-related processes, experiences need to be designed, engineered, or re-engineered, so that authentic humanity is built in. It is employees who are the real, flexible experience engineers.

Customer Experience 210

Customer Experience Engineering Customer centricity B2C 210

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 80

Analytics Engineering Metrics APIs 80

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 86

Calibration Engineering Big data Automotive 86

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 93

Engineering Analytics Self service Government 93

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 85

Scripts Engineering APIs Big data 85

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 93

Government Metrics APIs Engineering 93

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 117

Metrics Analytics APIs Big data 117

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

CustomerSuccessBox

MAY 30, 2022

To identify a watermelon customer, the metric that would help you the most is instead the Customer Intent Score. The Customer Intent Score is a metric that measures a visitor’s willingness to accomplish a conversion goal, for example- a request for further information. Just imagine the enormity of untracked data.

Upselling 52

Upselling Calibration Engineering Big data 52

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 126

Finance APIs Analytics Scripts 126

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 115

Management Best practices APIs Calibration 115

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 88

Enterprise Engineering Analytics Big data 88

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 89

Metrics Scripts Enterprise Telecommunications 89

AWS Machine Learning

SEPTEMBER 11, 2024

SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. SIMT describes processors that are able to operate on data vectors and arrays (as opposed to just scalars), and therefore handle big data workloads efficiently.

Benchmark 100

Benchmark Banking Analytics Big data 100

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

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

JULY 24, 2023

This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. Zilong Bai is a senior natural language processing engineer at Patsnap.

APIs 77

APIs Engineering Construction Benchmark 77

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

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 116

Enterprise Engineering Accountability APIs 116

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 92

Scripts Engineering Metrics Big data 92

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 87

Engineering Healthcare APIs Scripts 87

AWS Machine Learning

JULY 25, 2022

With other standard metrics, the improvement ranged from 50–130%. This performance enabled us to better match traders and bonds, indicating an enhanced trader experience within the model, with machine learning delivering a big step forward from hard-coded rules, which can be difficult to scale.

Scripts 84

Scripts Engineering Analytics APIs 84