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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

Government

Government Banking Accountability Engineering

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

NOVEMBER 14, 2024

This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization. Before we dive into the details of the architecture for sharing models, let’s review what use case and model governance is and why it’s needed.

Government

Government Management APIs Accountability

Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

FEBRUARY 29, 2024

Model governance – The Amazon SageMaker Model Registry integration allows for tracking model versions, and therefore promoting them to production with confidence. You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. script is used by pipeline_service.py The model_unit.py

Scripts

Scripts Engineering Metrics Best practices

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

FEBRUARY 13, 2024

Who needs a cross-account feature store Organizations need to securely share features across teams to build accurate ML models, while preventing unauthorized access to sensitive data. SageMaker Feature Store now allows granular sharing of features across accounts via AWS RAM, enabling collaborative model development with governance.

Accountability

Accountability Government Engineering Scripts

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

MARCH 23, 2023

default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. resource("s3").Bucket Bucket (bucket).Object Object (upload path).upload

Scripts

Scripts APIs Government Analytics

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

DECEMBER 21, 2022

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. Next, you need to create a Python script to run the Iceberg procedures. AWS Glue Job setup.

Scripts

Scripts Engineering APIs Big data

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

MARCH 7, 2023

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images.

Management

Management Scripts Government Engineering

The State of the Bot Going Into 2018

Aspect

DECEMBER 27, 2017

RFPs for chatbots have arisen in verticals as diverse as banking, government, healthcare, and retail. In 2018, we should see much better integration with customer data and analytics, bringing customer history, behavioral patterns, and big data into chatbot interactions.

Chatbots

Chatbots Contact Center Interactive Voice Response Enterprise

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We also introduce a logical construct of a shared services account that plays a key role in governance, administration, and orchestration.

APIs

APIs Accountability Scripts Enterprise

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

FEBRUARY 29, 2024

Before you can write scripts that use the Amazon Bedrock API, you’ll need to install the appropriate version of the AWS SDK in your environment. Information that identifies you may be shared with doctors responsible for your care or for audits and evaluations by government agencies, but talks and papers about the study will not identify you.

APIs

APIs Healthcare Scripts Enterprise

Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

MAY 8, 2023

It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This can be a challenge for enterprises in regulated industries that need to keep strong model governance for audit purposes. You can use this script add_users_and_groups.py

APIs

APIs Government Accountability Scripts

Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

AUGUST 22, 2023

The notebook instance client starts a SageMaker training job that runs a custom script to trigger the instantiation of the Flower client, which deserializes and reads the server configuration, triggers the training job, and sends the parameters response. script and a utils.py The client.py We use utility functions in the utils.py

Scripts

Scripts Accountability Transportation Benchmark

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

SEPTEMBER 9, 2022

Each project maintained detailed documentation that outlined how each script was used to build the final model. In many cases, this was an elaborate process involving 5 to 10 scripts with several outputs each. These had to be manually tracked with detailed instructions on how each output would be used in subsequent processes.

Engineering

Engineering Healthcare APIs Scripts

Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

SmartKarrot

APRIL 17, 2023

Access Controls: Limit access to sensitive data to only those who need it, and implement appropriate access controls to prevent unauthorized access. Data Retention Policies: Establish clear data retention policies to govern how long data is stored, and how it is securely disposed of once it is no longer needed.

Chatbots

Chatbots Scripts Enterprise Personalization

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

JANUARY 26, 2024

Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies.

Best practices

Best practices Government Engineering Accountability

Top 12 Customer Support Outsourcing Companies

ROI CX Solutions

DECEMBER 3, 2024

Its incorporating more artificial intelligence solutions for companies interested in benefiting from big data and AI insights. With its live answering option, you set up a customized script and forward your calls to Answerforce. Telus Internationals expanding services are reflected in its upcoming name change Telus Digital.

outsourcing

outsourcing Customer Support Technical Support Telemarketing

Customer Contact Central

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Trending Sources

Automate Amazon SageMaker Pipelines DAG creation

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

The State of the Bot Going Into 2018

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Securing MLflow in AWS: Fine-grained access control with AWS native services

Machine learning with decentralized training data using federated learning on Amazon SageMaker

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

Top 12 Customer Support Outsourcing Companies

Stay Connected

Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Trending Sources

Automate Amazon SageMaker Pipelines DAG creation

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

The State of the Bot Going Into 2018

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Securing MLflow in AWS: Fine-grained access control with AWS native services

Machine learning with decentralized training data using federated learning on Amazon SageMaker

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

Top 12 Customer Support Outsourcing Companies

Stay Connected

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction