2012, APIs and Metrics - Customer Contact Central

Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

JANUARY 24, 2025

Analyze results through metrics and evaluation. Model customization in Amazon Bedrock involves the following actions: Create training and validation datasets. Set up IAM permissions for data access. Configure a KMS key and VPC. Create a fine-tuning or pre-training job with hyperparameter tuning. Use the custom model for tasks like inference.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning

NOVEMBER 13, 2024

The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managed API.

Engineering

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Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

AWS Machine Learning

JULY 10, 2024

This process enhances task-specific model performance, allowing the model to handle custom use cases with task-specific performance metrics that meet or surpass more powerful models like Anthropic Claude 3 Sonnet or Anthropic Claude 3 Opus. Under Output data , for S3 location , enter the S3 path for the bucket storing fine-tuning metrics.

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

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

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Cost efficient ML inference with multi-framework models on Amazon SageMaker

AWS Machine Learning

OCTOBER 31, 2022

You can call the specific container directly in the API call and get the prediction from that model. Now we can call the endpoint for inference and define the TargetContainerHostname as either englishModel or germanModel depending on the client making the API call: sm.invoke_endpoint(. response = runtime.invoke_endpoint(.

APIs

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Deep demand forecasting with Amazon SageMaker

AWS Machine Learning

JUNE 28, 2022

The input data is a multi-variate time series that includes hourly electricity consumption of 321 users from 2012–2014. Amazon Forecast is a time-series forecasting service based on machine learning (ML) and built for business metrics analysis. For HPO, we use the RRSE as the evaluation metric for all the three algorithms.

Metrics

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

MAY 5, 2023

By uploading a small set of training images, Amazon Rekognition automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. For more details on the different metrics such as precision, recall, and F1, refer to Metrics for evaluation your model.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

NOVEMBER 16, 2022

Upon a new model version registration, someone with the authority to approve the model based on the metrics should approve or reject the model. You can use Boto3 APIs as shown the following example, or you can use the AWS Management Console to create the model package. We combine both policies to get the following final policy.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning

MAY 12, 2023

You can also detect many common issues that affect the readability, reproducibility, and correctness of computational notebooks, such as misuse of ML library APIs, invalid run order, and nondeterminism. To use the CodeWhisperer extension, ensure that you have the necessary permissions.

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Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

AWS Machine Learning

SEPTEMBER 7, 2023

if there are multiple, you can fetch available pipelines using boto3 api #and trigger the appropriate one based on your logic. The following piece of code illustrates the scenario where two different SageMaker pipeline runs are triggered: import boto3 def lambda_handler(event, context): items = [1, 2] #sagemaker client sm_client = boto3.client("sagemaker")

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Is Your Organization Ready for the Top Contact Center Trends of 2022?

Playvox

JANUARY 13, 2022

Members of Generation Z, people born between 1995 and 2012, are entering the workforce and bringing their own ideas about how the workplace should function. Cloud workforce management systems that provide real-time visibility to key metrics enable supervisors to see what agents are doing and if they’re adhering to schedules.

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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning

JUNE 6, 2023

When selecting the AMI, follow the release notes to run this command using the AWS Command Line Interface (AWS CLI) to find the AMI ID to use in us-west-2 : #STEP 1.2 - This requires AWS CLI credentials to call ec2 describe-images api (ec2:DescribeImages). We added the following argument to the trainer API in train_sentiment.py

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Contact Center Technologies 2017: find out what 23 experts say

RichCall

SEPTEMBER 13, 2017

And, as its mode of operation broadens and becomes more proactive, its success will have to be measured by new metrics, reflecting its critical role in overall business performance. In fact this number increased from 67% in 2012 to 84% in 2015. In 2011 Gartner predicted that. However, in 2016 Forrester has already reported that.

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A review of purpose-built accelerators for financial services

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. in 2012 is now widely referred to as ML’s “Cambrian Explosion.” The CUDA API and SDK were first released by NVIDIA in 2007.

Benchmark

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Selection, Implementation and Use: The Journey of Contact Center Technology with Joe Rice

Vistio

SEPTEMBER 20, 2021

And in 2012, I decided to make the jump to the indirect or referral partner channels, where a lot of these big Gartner ranked vendors across contact center and unified communications and networking have these really robust programs that enable partners, or referral brokers, or agents to basically add value around the purchasing process.

Contact Center

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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

OCTOBER 11, 2024

Amazon Bedrock makes it straightforward to adopt any of these choices by providing a common set of APIs, industry-leading embedding models, security, governance, and observability. In this post, we discuss use cases, features, and steps to set up and retrieve information using these vector databases.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning

MARCH 10, 2025

The Amazon Bedrock Knowledge Bases API also simplified our operations by combining embedding and retrieval functionality into a single API call. This optimization reduced our token usage and minimized unnecessary API calls, resulting in lower latency for each query.

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Customer Contact Central

Security best practices to consider while fine-tuning models in Amazon Bedrock

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

Trending Sources

Fine-tune Anthropic’s Claude 3 Haiku in Amazon Bedrock to boost model accuracy and quality

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

Cost efficient ML inference with multi-framework models on Amazon SageMaker

Deep demand forecasting with Amazon SageMaker

Build an image search engine with Amazon Kendra and Amazon Rekognition

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

Is Your Organization Ready for the Top Contact Center Trends of 2022?

Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

Contact Center Technologies 2017: find out what 23 experts say

A review of purpose-built accelerators for financial services

Selection, Implementation and Use: The Journey of Contact Center Technology with Joe Rice

Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

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