AWS Machine Learning

SEPTEMBER 21, 2022

This requires more accessible ML training, speaking to a larger number of people with diverse backgrounds. You can get started with RL quickly with hands-on tutorials that guide you through the basics of training RL models and testing them in an exciting, autonomous car racing experience. “We

Accountability 65

Accountability APIs Enterprise Big data 65

AWS Machine Learning

NOVEMBER 7, 2022

JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. In this post, we provide a step-by-step walkthrough on how to deploy pre-trained text generation models.

APIs 79

APIs Scripts Enterprise Big data 79

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 consists of an online and an offline mode for managing features.

Scripts 77

Scripts Engineering APIs Big data 77

AWS Machine Learning

OCTOBER 27, 2022

They faced two challenges: how to reduce food waste, and how to manage forecast models for over 10,000 SKUs and thousands of stores efficiently and at scale. Advanced inventory forecasting using machine learning (ML) allows retail stores to maximize sales and minimize waste through more effective inventory management and turnover.

APIs 98

APIs Best practices Metrics Government 98

AWS Machine Learning

MARCH 23, 2023

Although this example shows how to perform this for inference operations, you can extend the solution to training and other ML steps. Endpoints are deployed with a couple clicks or lines of code using SageMaker, which simplifies the process for developers and ML experts to build and train ML and deep learning models in the cloud.

Scripts 100

Scripts APIs Government Analytics 100

AWS Machine Learning

NOVEMBER 3, 2022

The phases we discuss in this post use the following key services: Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning (ML) models that have been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses.

Analytics 80

Analytics APIs Healthcare Big data 80

AWS Machine Learning

DECEMBER 1, 2022

In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console. They’re permitted to manage models, endpoints, and pipelines, and audit resources. ML activities.

Management 77

Management Government Enterprise APIs 77

AWS Machine Learning

AUGUST 26, 2022

JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. Fine-tune pre-trained models – JumpStart allows you to fine-tune pre-trained models with no need to write your own training algorithm.

APIs 82

APIs Scripts Enterprise Big data 82

AWS Machine Learning

JULY 19, 2022

You simply provide images with the appropriate labels, train the model, and deploy without having to build the model and fine-tune it. In this post, we show how to label, train, and build a computer vision model to detect rooftops and solar panels from satellite images. Use Amazon Rekognition to train the model with custom labels.

APIs 74

APIs Metrics Enterprise Government 74

AWS Machine Learning

SEPTEMBER 9, 2022

Collecting raw data for batch feature computing doesn’t have the sub-minute reflection time requirement PIT features have, which makes it feasible to buffer the events longer and transform metrics in batch. The underlying infrastructure for a Processing job is fully managed by SageMaker. Operational health.

Personalization 76

Personalization APIs Engineering Metrics 76

AWS Machine Learning

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio.

APIs 87

APIs Management Engineering Big data 87

AWS Machine Learning

JULY 29, 2022

Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data without the need of any prior ML experience.

Scripts 87

Scripts APIs Big data Engineering 87

AWS Machine Learning

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

Best practices 126

Best practices Government Engineering Accountability 126

AWS Machine Learning

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. This dichotomy can be effectively managed using a cross-account setup for the feature store.

Accountability 109

Accountability Government Engineering Scripts 109

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. Therefore, the typical first step in moving towards data-driven decision-making relies on finding that relevant (abnormal) data.

Metrics 96

Metrics Analytics APIs Big data 96

AWS Machine Learning

DECEMBER 1, 2022

A well-implemented model governance framework should minimize the number of interfaces required to view, track, and manage lifecycle tasks to make it easier to monitor the ML lifecycle at scale. Auto-populate Model cards for SageMaker trained models. Upload and share model and data evaluation results. SageMaker Model Cards.

Government 82

Government Metrics APIs Engineering 82

AWS Machine Learning

NOVEMBER 27, 2023

However, these models require massive amounts of clean, structured training data to reach their full potential. Most real-world data exists in unstructured formats like PDFs, which requires preprocessing before it can be used effectively. According to IDC , unstructured data accounts for over 80% of all business data today.

Engineering 77

Engineering APIs Big data Accountability 77

AWS Machine Learning

AUGUST 2, 2023

GenAI models are typically trained on vast amounts of data, which make them suitable for various tasks without additional training. Pre-training methods allow multi-modal applications to be easily trained using state-of-the-art language and image models.

Enterprise 75

Enterprise Healthcare APIs Big data 75

AWS Machine Learning

JULY 24, 2023

Patsnap provides a global one-stop platform for patent search, analysis, and management. They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. Patsnap had trained a customized GPT-2 model for such a purpose. implement the model and the inference API.

APIs 70

APIs Engineering Construction Benchmark 70

AWS Machine Learning

SEPTEMBER 9, 2022

Its electronic health records, revenue cycle management, and patient engagement tools allow anytime, anywhere access, driving better financial outcomes for its customers and enabling its provider customers to deliver better quality care. Amazon CloudWatch for persistent log management.

Engineering 80

Engineering Healthcare APIs Scripts 80

AWS Machine Learning

AUGUST 15, 2022

Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers whether it runs on AWS or not. Train an Amazon Comprehend custom entity recognition model. Then we train a custom entity recognition model.

Banking 78

Banking APIs Accountability Analytics 78

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. In this case, each time you train a new model and want to deploy it to the mobile phones, you need to redeploy the whole application. Edge computing.

Transportation 80

Transportation APIs Big data Wireless 80

AWS Machine Learning

JUNE 24, 2022

As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. Initial phase: During this phase, the data scientists are able to experiment and build, train, and deploy models on AWS using SageMaker services.

Enterprise 94

Enterprise Engineering Accountability APIs 94

AWS Machine Learning

JULY 25, 2022

To solve this challenge, Trumid and the ML Solutions Lab developed an end-to-end data preparation, model training, and inference process based on a deep neural network model built using the Deep Graph Library for Knowledge Embedding ( DGL-KE ). Training a knowledge graph embedding model. Predicting new trades.

Scripts 74

Scripts Engineering Analytics APIs 74

AWS Machine Learning

OCTOBER 4, 2022

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. After the pipeline has been fully tested locally, you can easily rerun it with Amazon SageMaker managed resources with just a few lines of code changes. Model training and tuning.

Scripts 76

Scripts Engineering Management APIs 76

AWS Machine Learning

JULY 21, 2022

As data scientists iterate through that process, they need a reliable method to easily track experiments to understand how each model version was built and how it performed. Amazon SageMaker allows teams to take advantage of a broad range of features to quickly prepare, build, train, deploy, and monitor ML models. xlarge" ).

Metrics 82

Metrics Scripts Enterprise Telecommunications 82

Hello Customer

JUNE 21, 2023

AI represents a complete flow that starts with the data, which gets put in the algorithm and eventually presented to the end-user. Representation of the data flow within Hello Customer For data to be valuable it’s crucial that you can manage, store and use data in an efficient way.

Customer Experience 52

Customer Experience Engineering Feedback Finance 52

AWS Machine Learning

MAY 3, 2023

Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it. However, generalizing feature engineering is challenging.

Engineering 74

Engineering Metrics APIs Big data 74

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 94

Management APIs Best practices Calibration 94

aircall

APRIL 16, 2021

AI and transcription – Automatically transcribe calls and uses AI to detect different speakers and produce transcripts for team training. SaaS works well for a variety of general use cases, including: Data backup. Big data analytics. But, regardless of how well-trained your employees are, miscommunications happen.

SaaS 62

SaaS Sales Coaching APIs 62

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

aircall

MAY 24, 2021

In today’s marketplace, it’s hard to survive without the cloud, big data, APIs, IoT, machine learning, artificial intelligence, automation, and mobile technologies. Overall, your digital transformation framework is the place to start whenever your data or new trends motivate you to change your tools or processes.

Best practices 62

Best practices Call Center Technology Analytics 62

Natalie Petouhof

MAY 27, 2016

How to Measure Customer Experience: Performance Management Maturity-Upcoming. Here’s a list of Dr. Natalie’s completed and published research and soon to be published content! The As-a-Service Economy: CX and IOT Mean You Have to Deliver Great Experiences- Upcoming.

CRM 40

CRM Automotive Analytics Big data 40

AWS Machine Learning

MAY 8, 2023

With Amazon SageMaker , you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. How to use MLflow as a centralized repository in a multi-account setup.

APIs 75

APIs Government Accountability Scripts 75

AWS Machine Learning

MARCH 22, 2023

Finally, we show how to export the data flow and train a model using SageMaker Autopilot. An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles. An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles. Configure Snowflake.

APIs 84

APIs Engineering Analytics Scripts 84

AWS Machine Learning

DECEMBER 18, 2023

Industries such as Finance, Retail, Supply Chain Management, and Logistics face the risk of missed opportunities, increased costs, inefficient resource allocation, and the inability to meet customer expectations. In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution.

Finance 108

Finance APIs Analytics Scripts 108

AWS Machine Learning

JANUARY 4, 2024

NLP SQL enables business users to analyze data and get answers by typing or speaking questions in natural language, such as the following: “Show total sales for each product last month” “Which products generated more revenue?” Fine-tuning directly trains the model on the end task but requires many text-SQL examples.

Best practices 124

Best practices Enterprise Engineering APIs 124