AWS Machine Learning

OCTOBER 27, 2022

We can then call a Forecast API to create a dataset group and import data from the processed S3 bucket. We use the AutoPredictor API, which is also accessible through the Forecast console. We use the AutoPredictor API, which is also accessible through the Forecast console. Ray Wang is a Solutions Architect at AWS.

APIs 98

APIs Best practices Metrics Government 98

AWS Machine Learning

FEBRUARY 1, 2024

Observability Besides the resource metrics you typically collect, like CPU and RAM utilization, you need to closely monitor GPU utilization if you host a model on Amazon SageMaker or Amazon Elastic Compute Cloud (Amazon EC2). He entered the big data space in 2013 and continues to explore that area.

Best practices 131

Best practices Engineering SaaS APIs 131

AWS Machine Learning

NOVEMBER 3, 2022

Before you get started, refer to Part 1 for a high-level overview of the insurance use case with IDP and details about the data capture and classification stages. In Part 1, we saw how to use Amazon Textract APIs to extract information like forms and tables from documents, and how to analyze invoices and identity documents.

Analytics 87

Analytics APIs Healthcare Big data 87

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 109

Management Best practices APIs Calibration 109

AWS Machine Learning

JULY 19, 2022

To test the model output, we use a Jupyter notebook to run Python code to detect custom labels in a supplied image by calling Amazon Rekognition APIs. The solution workflow is as follows: Store satellite imagery data in Amazon S3 as the input source. Store satellite imagery data in Amazon S3 as an input source.

APIs 81

APIs Metrics Enterprise Marketing 81

AWS Machine Learning

MAY 8, 2023

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

APIs 81

APIs Government Accountability Scripts 81

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

DECEMBER 18, 2023

The SageMaker Canvas UI lets you seamlessly integrate data sources from the cloud or on-premises, merge datasets effortlessly, train precise models, and make predictions with emerging data—all without coding. Solution overview Users persist their transactional time series data in MongoDB Atlas.

Finance 120

Finance APIs Analytics Big data 120

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. You can change the configuration later from the SageMaker Canvas UI or using SageMaker APIs.

Surveys 109

Surveys Metrics Analytics Engineering 109

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 91

Government Metrics APIs Engineering 91

AWS Machine Learning

DECEMBER 21, 2022

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

Scripts 84

Scripts Engineering APIs Big data 84

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 111

Metrics Analytics APIs Big data 111

AWS Machine Learning

APRIL 24, 2023

In this post, we explore how AWS customer Pro360 used the Amazon Comprehend custom classification API , which enables you to easily build custom text classification models using your business-specific labels without requiring you to learn machine learning (ML), to improve customer experience and reduce operational costs.

Customer Experience 70

Customer Experience APIs Feedback Chatbots 70

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

MAY 3, 2023

Furthermore, SageMaker Processing provides an API interface for running, monitoring, and evaluating the workload. The managed cluster, instances, and containers report metrics to Amazon CloudWatch , including usage of GPU, CPU, memory, GPU memory, disk metrics, and event logging.

Engineering 83

Engineering Metrics APIs Big data 83

AWS Machine Learning

JULY 24, 2023

They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. In this section, we show how to build your own container, deploy your own GPT-2 model, and test with the SageMaker endpoint API. implement the model and the inference API. gpt2 and predictor.py

APIs 76

APIs Engineering Construction Benchmark 76

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

Spearline

JANUARY 23, 2018

An API (Application Programming Interface) will enhance your utilisation of our platform. Our RESTful API provides your developers with the ability to create campaigns, add numbers, time groups, export data for every test run, every day, every hour, every minute if that’s what you need to put your arms around your business.

Benchmark 40

Benchmark Call flow APIs Big data 40

AWS Machine Learning

JUNE 24, 2022

The model training step could be either one training job, if the data scientist is aware of the best model configuration, or a hyperparameter optimization (HPO) job, in which AWS defines the best hyperparameters for the model (Bayesian method) and produces the corresponding model artifact. Data lake and MLOps integration.

Enterprise 109

Enterprise Engineering Accountability APIs 109

AWS Machine Learning

AUGUST 29, 2023

When a new version of the model is registered in the model registry, it triggers a notification to the responsible data scientist via Amazon SNS. If the model quality metric (for example, RMSE for regression and F1 score for classification) doesn’t meet a pre-specified criterion, the model quality check step is marked as failed.

Scripts 84

Scripts APIs Enterprise Accountability 84

AWS Machine Learning

JULY 25, 2022

For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing. With other standard metrics, the improvement ranged from 50–130%.

Scripts 82

Scripts Engineering Analytics APIs 82

aircall

APRIL 16, 2021

Establish performance metrics (response time, retention, engagement, etc.). SaaS works well for a variety of general use cases, including: Data backup. Big data analytics. Flexibility – SaaS uses an open API (application programming interface) technology. 8) Failing to track metrics. troubleshooting.

SaaS 62

SaaS Sales Coaching APIs 62

AWS Machine Learning

JUNE 15, 2022

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. How do I eliminate the need of installing a big framework like TensorFlow or PyTorch on my restricted device? Edge computing.

Transportation 87

Transportation APIs Big data Wireless 87

AWS Machine Learning

SEPTEMBER 9, 2022

Prior to our adoption of Kubeflow on AWS, our data scientists used a standardized set of tools and a process that allowed flexibility in the technology and workflow used to train a given model. This means that user access can be controlled on the Kubeflow UI but not over the Kubernetes API via Kubectl. Logging and monitoring.

Engineering 87

Engineering Healthcare APIs Scripts 87

AWS Machine Learning

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.

Benchmark 93

Benchmark Banking Analytics Big data 93

Xaqt

FEBRUARY 6, 2020

Back then, Artificial Intelligence, APIs, Robotic Process Automation (RPA), and even "Big Data" weren't things yet. There are also drill-down reports that promise to let your managers slice and dice their data anyway they choose. Rewind it Back Let's take a look back to 2005 when "Web 2.0"

Contact Center 52

Contact Center Enterprise Call Center APIs 52

Xaqt

JUNE 22, 2020

Despite significant advancements in big data and open source tools, niche Contact Center Business Intelligence providers are still wed to their own proprietary tools leaving them saddled with technical debt and an inability to innovate from within. or "Does Service Level include Calls abandoned?").

Call Center 52

Call Center Contact Center Analytics Engineering 52

aircall

MARCH 19, 2021

With all the available customer data companies have at their disposal to enhance the performance of customer service, sales, and marketing efforts, a remarkable 73% of companies still do not use it effectively. And out of those who do practise data collection, only 12% analyze it. Best Practices of Customer Data Management.

Sales 62

Sales CRM Marketing Best practices 62

Enghouse Interactive

JULY 10, 2018

Contact Babel 2017 DMG says, “It can be stated with some confidence that first-contact resolution is seen as the key to a successful contact center: 76% of the report’s respondents place first-contact resolution as being one of the top 3 metrics that are most influential on customer satisfaction, with 31% stating it as being no.1.”.

Self service 40

Self service CRM Contact Center Analytics 40

SmartKarrot

JUNE 23, 2020

Without analytics, collation of behavioural data is a waste. Without analytics, CS teams can only rely on insufficient demographic data, or what’s called ‘vanity metrics. So, as Streaming, Sharing, Stealing: Big Data and the Future of Entertainment co-author Michael D. Remember: Data never lies .

Analytics 10

Analytics SaaS Upselling Entertainment 10

AWS Machine Learning

JANUARY 26, 2024

Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.

Best practices 137

Best practices Government Engineering Accountability 137

AWS Machine Learning

SEPTEMBER 18, 2024

It provides a unified interface for logging parameters, code versions, metrics, and artifacts, making it easier to compare experiments and manage the model lifecycle. From our experience, artifact server has some limitations, such as limits on artifact size (because of sending it using REST API).

APIs 112

APIs Engineering Analytics Marketing 112

AWS Machine Learning

AUGUST 12, 2024

We also demonstrate how to use the generative AI capabilities of SageMaker Canvas to speed up your data exploration and help you build better ML models. Use case overview In this example, a health-tech company offering remote patient monitoring is collecting operational data from wearables using Splunk.

Engineering 110

Engineering APIs Healthcare Analytics 110

AWS Machine Learning

AUGUST 2, 2024

Queries are sent to the backend using a REST API defined in Amazon API Gateway , a fully managed service that makes it straightforward for developers to create, publish, maintain, monitor, and secure APIs at any scale, and implemented through an API Gateway private integration.

APIs 111

APIs Analytics Chatbots Engineering 111

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 111

Engineering Metrics APIs Big data 111

AWS Machine Learning

NOVEMBER 13, 2024

Because SageMaker Model Cards and SageMaker Model Registry were built on separate APIs, it was challenging to associate the model information and gain a comprehensive view of the model development lifecycle. SageMaker Model Registry catalogs your models along with their versions and associated metadata and metrics for training and evaluation.

Government 102

Government Engineering Metrics APIs 102

AWS Machine Learning

DECEMBER 17, 2024

Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Connect with Hin Yee on LinkedIn.

APIs 90

APIs Engineering Feedback Coaching 90