Big data, Groups and Scripts - Customer Contact Central

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

OCTOBER 5, 2023

We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw. Compared to Llama 1, Llama 2 doubles context length from 2,000 to 4,000, and uses grouped-query attention (only for 70B). 4096 2 8 4 1 256 7.4. 4096 4 8 4 1 256 14.6.

Scripts

Scripts Big data Engineering SaaS

Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

FEBRUARY 29, 2024

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts

Scripts Engineering Metrics Best practices

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

AWS Machine Learning

DECEMBER 21, 2022

Depending on the design of your feature groups and their scale, you can experience training query performance improvements of 10x to 100x by using this new capability. The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. Creating feature groups using Iceberg table format.

Scripts

Scripts Engineering APIs Big data

Webinars

Manual Call Reviews? Theres a Better Way!

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

NOVEMBER 9, 2023

client("sagemaker") create_model_package_group_response = sm_client.create_model_package_group( ModelPackageGroupName=model_package_group_name, ModelPackageGroupDescription="Cross account model package group for mammo severity model", ) print('ModelPackageGroup Arn : {}'.format(create_model_package_group_response['ModelPackageGroupArn']))

Scripts

Scripts Accountability Engineering Best practices

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

AWS Machine Learning

FEBRUARY 13, 2024

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM). For a deep dive, refer to Cross account feature group discoverability and access.

Accountability

Accountability Government Engineering Scripts

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

MARCH 15, 2024

To create these packages, run the following script found in the root directory: /build_mlops_pkg.sh Enter a group name and a project name, then choose OK. He entered the big data space in 2013 and continues to explore that area. Choose Create a new project. He also holds an MBA from Colorado State University.

Healthcare

Healthcare Big data Engineering Accountability

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning

AUGUST 29, 2023

Data I/O design SageMaker interacts directly with Amazon S3 for reading inputs and storing outputs of individual steps in the training and inference pipelines. The pipeline will automatically upload Python scripts from the GitLab repository and store output files or model artifacts from each step in the appropriate S3 path.

Scripts

Scripts APIs Enterprise Accountability

6 Online Data Analyst Courses

JivoChat

JANUARY 31, 2023

The Data Analyst Course With the Data Analyst Course, you will be able to become a professional in this area, developing all the necessary skills to succeed in your career. The course also teaches beginner and advanced Python, basics and advanced NumPy and Pandas, and data visualization.

Analytics

Analytics APIs Scripts Big data

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

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

AWS Machine Learning

MAY 8, 2023

1', base_job_name='mlflow', environment=environment ) Visualize runs and experiments from the MLflow UI After the first deployment is complete, let’s populate the Amazon Cognito user pool with three users, each belonging to a different group, to test the permissions we have implemented. You can use this script add_users_and_groups.py

APIs

APIs Government Accountability Scripts

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

AWS Machine Learning

SEPTEMBER 9, 2022

athenahealth a leading provider of network-enabled software and services for medical groups and health systems nationwide. 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.

Engineering

Engineering Healthcare APIs Scripts

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

AWS Machine Learning

APRIL 11, 2023

Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. 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.

APIs

APIs Accountability Scripts Enterprise

How Sophos trains a powerful, lightweight PDF malware detector at ultra scale with Amazon SageMaker

AWS Machine Learning

SEPTEMBER 30, 2022

The Sophos Artificial Intelligence (AI) group (SophosAI) oversees the development and maintenance of Sophos’s major ML security technology. Security is a big-data problem. This translates into colossal threat datasets that the group must work with to best defend customers.

Scripts

Scripts Engineering Metrics Big data

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

SEPTEMBER 9, 2022

The pipeline allowed Amp data engineers to easily deploy Airflow DAGs or PySpark scripts across multiple environments. Amp used Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS) to configure and manage containers for their data processing and transformation jobs. Data Engineer for Amp on Amazon.

Analytics

Analytics Engineering APIs Metrics

The State of the Bot Going Into 2018

Aspect

DECEMBER 27, 2017

Accordingly, I expect to see a range of new solutions see the light of day in 2018; solutions that bring the old solutions like Interactive Voice Response (cue the robotic ‘press 1 for English’ script) into the 21 st century, on a channel people actually like to use.

Chatbots

Chatbots Contact Center Interactive Voice Response Enterprise

Prepare image data with Amazon SageMaker Data Wrangler

AWS Machine Learning

MAY 1, 2023

Add a custom transformation to detect and remove image outliers With image preparation in Data Wrangler, we can also invoke another endpoint for another model. You can find some sample scripts with boilerplate code in the Search example snippets section. Lu Huang is a Senior Product Manager on Data Wrangler.

Scripts

Scripts Big data Engineering Enterprise

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

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). Track experiments – Experiments allows data scientists to track experiments.

Metrics

Metrics Scripts Enterprise Telecommunications

The Most Effective Communication Channels for Personalized Customer Service

aircall

JUNE 2, 2021

It’s also the main type of communication channel that people use to get in touch with customer service representatives across all age groups and countries. Businesses now have the option to create a community or group to send out mass targeted messages. Do you want to gather and record customer data from this interaction?

Personalization

Personalization Customer Service B2B Self service

Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

AWS Machine Learning

APRIL 21, 2023

The code sets up the S3 paths for pipeline inputs, outputs, and model artifacts, and uploads scripts used within the pipeline steps. After the train and baseline pipeline run successfully, it registers the trained model as part of the model group in the model registry. Repeat the same for the second custom policy.

Metrics

Metrics Telecommunications Analytics Healthcare

SOASTA Ushers in New Era of Innovation in Digital Performance Management

Natalie Petouhof

APRIL 4, 2016

The new SOASTA mPulse enhancements make Big Data insights easy to visualize, access and share. Instead of siloing information within groups, SOASTA mPulse with embedded Data Science Workbench reports help enterprises isolate issues, triage performance problems, and make decisions based on a better understanding of the customer. .

Management

Management Analytics Consulting Consulting

Customer Contact Central

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium