AWS Machine Learning

JUNE 17, 2024

PrestoDB is an open source SQL query engine that is designed for fast analytic queries against data of any size from multiple sources. 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.

Scripts 106

Scripts Engineering Metrics Big data 106

AWS Machine Learning

JULY 11, 2024

Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. This solution uses Retrieval Augmented Generation (RAG) to ensure the generated scripts adhere to organizational needs and industry standards. A GitHub account with a repository to store the generated Terraform scripts.

Scripts 117

Scripts APIs Engineering industry standards 117

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 109

Scripts Accountability Best practices Engineering 109

AWS Machine Learning

JULY 2, 2024

An Amazon OpenSearch Serverless vector engine to store enterprise data as vectors to perform semantic search. Amazon Bedrock retrieves relevant data from the vector store (using the vector engine for OpenSearch Serverless ) using hybrid search. Create an S3 bucket in your account. The following diagram illustrates this workflow.

APIs 116

APIs Scripts Enterprise Engineering 116

AWS Machine Learning

SEPTEMBER 20, 2023

SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. SageMaker is a comprehensive ML service enabling business analysts, data scientists, and MLOps engineers to build, train, and deploy ML models for any use case, regardless of ML expertise.

Scripts 109

Scripts Engineering Accountability Enterprise 109

AWS Machine Learning

MARCH 8, 2023

After the data is downloaded into the training instance, the custom training script performs data preparation tasks and then trains the ML model using the XGBoost Estimator. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account. amazonaws.com/sagemaker-xgboost:1.5-1

Scripts 114

Scripts Accountability Enterprise Management 114

AWS Machine Learning

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. Amazon Bedrock service starts an import job in an AWS operated deployment account.

APIs 107

APIs Scripts Real estate Accountability 107

AWS Machine Learning

JUNE 4, 2024

The workflow includes the following steps: The user runs the terraform apply The Terraform local-exec provisioner is used to run a Python script that downloads the public dataset DialogSum from the Hugging Face Hub. Prerequisites This solution requires the following prerequisites: An AWS account.

Scripts 120

Scripts Accountability Engineering Best practices 120

AWS Machine Learning

NOVEMBER 30, 2023

Solution overview To deploy your SageMaker HyperPod, you first prepare your environment by configuring your Amazon Virtual Private Cloud (Amazon VPC) network and security groups, deploying supporting services such as FSx for Lustre in your VPC, and publishing your Slurm lifecycle scripts to an S3 bucket. Choose Create role. Choose Save.

Scripts 120

Scripts Engineering Government Management 120

AWS Machine Learning

NOVEMBER 22, 2024

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. The diagram shows several accounts and personas as part of the overall infrastructure. The following diagram gives a high-level illustration of the use case.

Government 80

Government Banking Accountability Engineering 80

AWS Machine Learning

APRIL 11, 2023

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs 70

APIs Accountability Scripts Enterprise 70

AWS Machine Learning

JUNE 16, 2023

They had several skilled engineers and scientists building insightful models that improved the quality of risk analysis on their platform. SambaSafety’s data science team maintained several script-like artifacts as part of their development workflow. SambaSafety connected with AWS account teams with their problem.

Scripts 82

Scripts Engineering Accountability Consulting 82

AWS Machine Learning

FEBRUARY 14, 2024

Prerequisites For this walkthrough, you should have the following prerequisites: Familiarity with SageMaker Ground Truth labeling jobs and the workforce portal Familiarity with the AWS Cloud Development Kit (AWS CDK) An AWS account with the permissions to deploy the AWS CDK stack A SageMaker Ground Truth private workforce Python 3.9+

Scripts 112

Scripts Engineering Entertainment Healthcare 112

AWS Machine Learning

DECEMBER 22, 2023

The agent can assist users with finding their account information, completing a loan application, or answering natural language questions while also citing sources for the provided answers. This memory allows the agent to provide responses that take into account the context of the ongoing conversation.

Scripts 108

Scripts Chatbots Accountability Multi-channel support 108

AWS Machine Learning

OCTOBER 28, 2024

Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. A preprocessor script is a capability of SageMaker Model Monitor to preprocess SageMaker endpoint data capture before creating metrics for model quality.

Scripts 89

Scripts Metrics Engineering Analytics 89

AWS Machine Learning

JANUARY 6, 2025

To build an anomaly detection system using such techniques, you need to write custom scripts for processing as well for training. SageMaker provides support for developing scripts by extending in-built algorithm containers, or by building your own custom containers. Combine all the steps in a SageMaker pipeline and run it.

Scripts 89

Scripts Metrics Construction Accountability 89

AWS Machine Learning

JULY 11, 2024

Use prompt engineering to provide this additional context to the LLM along with the original question. Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account set up. If you have administrator access to the account, no additional action is required. expand(token_embeddings.size()).float()

Scripts 110

Scripts Accountability APIs Engineering 110

AWS Machine Learning

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

Government 78

Government Management APIs Accountability 78

AWS Machine Learning

JUNE 21, 2023

Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting a new Studio notebook. This enables you to apply DevOps best practices and meet safety, compliance, and configuration standards across all AWS accounts and Regions. For Windows, use.cdk-venv/Scripts/activate.bat.

Construction 83

Construction Scripts Engineering Accountability 83

Amity

MARCH 24, 2017

If you’re in business today with a recurring revenue model, of the biggest questions you must ask is “where are the gaps in my revenue engine?” Most companies have revenue engines that run on delicately intertwined gears of new logos, retention and upsell/cross-sell. Learning from Our Process.

Accountability 56

Accountability Engineering Metrics Upselling 56

AWS Machine Learning

MAY 22, 2024

Customers can more easily locate products that have correct descriptions, because it allows the search engine to identify products that match not just the general category but also the specific attributes mentioned in the product description. For details, see Creating an AWS account. We use Amazon SageMaker Studio with the ml.t3.medium

Scripts 118

Scripts Engineering Accountability APIs 118

ChurnZero

MARCH 7, 2019

Job Opening: Sales Engineer. Our fast-growing startup is looking for a skilled, self-motivated problem solver to join our team as a Sales Engineer. As a Sales Engineer , you are the technical expert during the sales process. Create and update product demonstration scripts. SaaS/Cloud experience strongly preferred.

Engineering 8

Engineering Sales SaaS APIs 8

AWS Machine Learning

JANUARY 5, 2024

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight.

Management 119

Management APIs Engineering Government 119

AWS Machine Learning

APRIL 26, 2024

Also make sure you have the account-level service limit for using ml.p4d.24xlarge user Write a Python script to read a CSV file containing stock prices and plot the closing prices over time using Matplotlib. The file should have columns named 'Date' and 'Close' for this script to work correctly. 24xlarge or ml.pde.24xlarge

Transportation 109

Transportation Scripts Accountability Benchmark 109

AWS Machine Learning

NOVEMBER 13, 2024

script to automatically copy the cdk configuration parameters to a configuration file by running the following command, still in the /cdk folder: /scripts/postdeploy.sh After the deployment is complete, you have two options. The preferred option is to use the provided postdeploy.sh

APIs 110

APIs Scripts Accountability Entertainment 110

AWS Machine Learning

JUNE 19, 2024

Prerequisites You need an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?

APIs 87

APIs Engineering Accountability Big data 87

AWS Machine Learning

NOVEMBER 20, 2023

Data scientists or ML engineers who want to run model training can do so without the burden of configuring training infrastructure or managing Docker and the compatibility of different libraries. We reviewed the training script code to see if anything was causing the CPU bottleneck. 24xlarge instances. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'

Scripts 100

Scripts Finance Telecommunications Enterprise 100

AWS Machine Learning

MARCH 15, 2024

Healthcare organizations must navigate strict compliance regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, while implementing FL solutions. FedML Octopus is the industrial-grade platform of cross-silo FL for cross-organization and cross-account training.

Healthcare 110

Healthcare Big data Engineering Accountability 110

AWS Machine Learning

FEBRUARY 12, 2024

Evaluate – A PySpark processing job evaluates the model using a custom Spark script. This dual-repository approach allows for greater modularity, and enables science and engineering teams to iterate independently on ML code and ML pipeline components. The evaluation report is then stored in Amazon S3.

Engineering 119

Engineering Wait times Metrics Accountability 119

AWS Machine Learning

NOVEMBER 30, 2023

As with all IDE applications in SageMaker Studio, ML developers and engineers can select the underlying compute on demand, and swap it based on their needs without losing data. A lifecycle configuration script to run in case you want customize your environment at app creation. Choose Open CodeEditor to launch the IDE.

Scripts 111

Scripts Engineering Management Accountability 111

AWS Machine Learning

JULY 25, 2024

Overview of solution This post presents the steps to create a certificate and private key, configure Azure AD (either using the Azure AD console or a PowerShell script), and configure Amazon Q Business. You need a Microsoft Windows instance to run PowerShell scripts and commands with PowerShell 7.4.1+. Choose New registration.

Scripts 91

Scripts APIs Engineering Enterprise 91

AWS Machine Learning

APRIL 23, 2024

By performing operations (applications, infrastructure) as code, you can provide consistent and reliable deployments in multiple AWS accounts and AWS Regions, and maintain versioned and auditable infrastructure configurations. Enterprise Solutions Architect at AWS, experienced in Software Engineering, Enterprise Architecture, and AI/ML.

APIs 114

APIs Enterprise Scripts Accountability 114

AWS Machine Learning

JUNE 27, 2023

By enabling effective management of the ML lifecycle, MLOps can help account for various alterations in data, models, and concepts that the development of real-time image recognition applications is associated with. At-scale, real-time image recognition is a complex technical problem that also requires the implementation of MLOps.

Engineering 102

Engineering APIs Scripts Entertainment 102

AWS Machine Learning

NOVEMBER 15, 2023

Prerequisites The following are prerequisites for completing the walkthrough in this post: An AWS account Familiarity with SageMaker concepts, such as an Estimator, training job, and HPO job Familiarity with the Amazon SageMaker Python SDK Python programming knowledge Implement the solution The full code is available in the GitHub repo.

Scripts 95

Scripts Best practices Accountability Engineering 95

AWS Machine Learning

DECEMBER 21, 2022

Amazon SageMaker Feature Store is a purpose-built feature management solution that helps data scientists and ML engineers securely store, discover, and share curated data used in training and prediction workflows. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account.

Scripts 73

Scripts Engineering APIs Big data 73

AWS Machine Learning

FEBRUARY 29, 2024

The AI platform team’s key objective is to ensure seamless access to Workbench services and SageMaker Studio for all Deutsche Bahn teams and projects, with a primary focus on data scientists and ML engineers. The AI team does not have AWS Management Console access to the AI platform team’s account.

APIs 108

APIs Construction Transportation Analytics 108

AWS Machine Learning

NOVEMBER 30, 2023

The function then searches the OpenSearch Service image index for images matching the celebrity name and the k-nearest neighbors for the vector using cosine similarity using Exact k-NN with scoring script. Go to the CloudFormation console, choose the stack that you deployed through the deploy script mentioned previously, and delete the stack.

Scripts 103

Scripts APIs Entertainment Accountability 103