AWS Machine Learning

DECEMBER 7, 2022

This post shows how Amazon SageMaker enables you to not only bring your own model algorithm using script mode, but also use the built-in HPO algorithm. You will learn how to easily output the evaluation metric of choice to Amazon CloudWatch , from which you can extract this metric to guide the automatic HPO algorithm.

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AWS Machine Learning

FEBRUARY 29, 2024

The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well. 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

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AWS Machine Learning

AUGUST 11, 2022

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

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NobelBiz

DECEMBER 8, 2020

The first step toward running a successful campaign starts with creating a good outbound call script. The purpose behind outbound call scripts No matter who your prospects really are, one thing is certain. Hence the need for an outbound call script that follows certain golden rules. They will always impose a time limit.

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AWS Machine Learning

JULY 2, 2024

Image 2: Hugging Face NLP model inference performance improvement with torch.compile on AWS Graviton3-based c7g instance using Hugging Face example scripts. This section shows how to run inference in eager and torch.compile modes using torch Python wheels and benchmarking scripts from Hugging Face and TorchBench repos.

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AWS Machine Learning

JUNE 24, 2022

We run this example on Amazon SageMaker Studio for you to try out for yourself. We use the following GitHub repo as an example, so let’s load this notebook. This example uses PyTorch, so we can choose the pre-built PyTorch 1.10 We use the Cambridge-driving Labeled Video Database (CamVid) for this example. program: train.py

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Calltools

FEBRUARY 16, 2022

It may sound complicated, but a fairly simple set of KPI metrics can help you measure your lead source ROI. The most important KPI metrics for monitoring your lead source ROI are: Cost per Acquisition. This metric assesses the quality of your lead lists. Sales script that needs improvement. Connection Rate.

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ShepHyken

NOVEMBER 4, 2022

This week, we feature an article by Baphira Wahlang Shylla, a digital marketer at Knowmax , a SaaS company that provides knowledge management solutions for various industries that are seeking to improve their customer service metrics. For example, it can take up to 5-6 weeks to provide training to new agents at a call center.

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AWS Machine Learning

SEPTEMBER 18, 2024

Reusable scaling scripts for rapid experimentation – HyperPod offers a set of scalable and reusable scripts that simplify the process of launching multiple training runs. The Observability section of this post goes into more detail on which metrics are exported and what the dashboards look like in Amazon Managaed Grafana.

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AWS Machine Learning

JUNE 17, 2024

The following code shows an example of how a query is configured within the config.yml file. If you are using the default VPC and security groups then you can leave these configuration parameters empty, see example in this configuration file. The latest model registered in the model registry from the training pipeline is approved.

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AWS Machine Learning

NOVEMBER 14, 2024

In AWS, these model lifecycle activities can be performed over multiple AWS accounts (for example, development, test, and production accounts) at the use case or business unit level. You can build a use case (or AI system) using existing models, newly built models, or combination of both.

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AWS Machine Learning

SEPTEMBER 16, 2024

For example, if you have the DNA sequence “ATCGATCG” and you choose k = 3, then the k-mers within this sequence would be “ATC,” “TCG,” “CGA,” “GAT,” and “ATC.” Run the script get_30mers_from_fa.py (from the CRISPRon GitHub repository ) to obtain all possible 23-mers and 30-mers from the sequences obtained from Step 1.

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Scripts Metrics Healthcare Education 116

REVE Chat Blog

FEBRUARY 5, 2020

An example of poor customer service not only worsens the existing customer relationships but also endangers the potential opportunities, and obviously erodes the bottom line of your business. Overuse of scripts to respond to customers. Top reasons for bad customer service examples (and how to fix them). How to fix?

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AWS Machine Learning

APRIL 1, 2024

Most of the details will be abstracted by the automation scripts that we use to run the Llama2 example. We use the following code references in this use case: End-to-end fsdp example Llama-recipes example What is Llama2? The example will also work with a pre-existing EKS cluster. Cluster with p4de.24xlarge

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Fonolo

MARCH 16, 2023

But without the contact center KPIs and metrics that managers use to measure the effectiveness of their operations, you’d never know for sure. We asked contact center industry influencers to share their insights into the changing role of KPIs and shine a light on new metrics to watch. KPIs matter. And they’re changing quickly.

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AWS Machine Learning

MAY 29, 2024

For example, instead of simply asking the model to describe the image, ask specific questions about the image and relating to its content. By creating synthetic examples of text descriptions, question-answer pairs, and corresponding charts, you can augment your dataset with multimodal examples tailored to your specific use case.

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Infinity

MAY 25, 2022

That’s where call centre tracking and metrics come in. Tracking the right metrics can unlock a wealth of insights that will make your call centre operations more effective than ever. There are tons of call centre metrics you can track , from abandonment rate to FAQs. How do call centres track performance? Average handle time.

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AWS Machine Learning

APRIL 12, 2023

All the training and evaluation metrics were inspected manually from Amazon Simple Storage Service (Amazon S3). The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline.

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AWS Machine Learning

MAY 31, 2024

This genomic data could be either public (for example, GenBank) or could be your own proprietary data. Training on SageMaker We use PyTorch and Amazon SageMaker script mode to train this model. Script mode’s compatibility with PyTorch was crucial, allowing us to use our existing scripts with minimal modifications.

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AWS Machine Learning

NOVEMBER 22, 2024

In our example, we use one ml.p4d.24xlarge The layers (for example, transformer layers, gate networks, and feed-forward networks) put together will form the entire model, as shown in the following diagram, which will be considered to be sharded by FSDP across our cluster (shown as small shards in blue). 24xlarge compute instance.

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ProProfs Blog

APRIL 17, 2020

But without numbers or metric data in hand, coming up with any new strategy would only consume your valuable time. For example, you need access to metrics like NPS, average response time and others like it to make sure you come up with relevant strategies that help you retain more customers. So, buckle up. 7: LTV/CAC Ratio. #8:

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AWS Machine Learning

NOVEMBER 10, 2023

For example, you might want to solve an image recognition task using a supervised learning algorithm. Additionally, you need to define which underlying metric fits best for your task and you want to optimize for (such as accuracy, F1 score, or ROC). We opted for providing our own Python script and using Scikit-learn as our framework.

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AWS Machine Learning

MARCH 6, 2024

For example, in 2023, a research team described training a 100 billion-parameter pLM on 768 A100 GPUs for 164 days! In the following sections, we go through the steps to prepare your training data, create a training script, and run a SageMaker training job. The following diagram illustrates this workflow. apply(lambda x: len(x)).between(100,

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GetFeedback

AUGUST 14, 2019

Measuring just a piece of this journey can seem short-sighted or not as powerful as other CX metrics, like Net Promoter Score (NPS). CX shouldn’t ever be measured by one metric alone. Customers and their experiences are complex and nuanced, so there’s no perfect metric. See the example below. FREE TOOL: CSAT CALCULATOR

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Metrics Journey mapping Surveys Feedback 109

AWS Machine Learning

SEPTEMBER 26, 2024

Furthermore, as clusters scale to larger sizes (for example, more than 32 nodes), they require built-in resiliency mechanisms such as automated faulty node detection and replacement to improve cluster goodput and maintain efficient operations. For example, when working with a smaller backbone model like Stable Diffusion 1.5,

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AWS Machine Learning

APRIL 19, 2024

Solution overview In this section, we present a generic architecture that is similar to the one we use for our own workloads, which allows elastic deployment of models using efficient auto scaling based on custom metrics. The reverse proxy collects metrics about calls to the service and exposes them via a standard metrics API to Prometheus.

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Infinity

MAY 9, 2022

Metrics are the most effective way of monitoring and measuring the performance of your contact centres. Tracking the right metrics can unlock a wealth of insights that will make your call centre operations more effective than ever. How can call centres improve metrics? What are the call centre metrics that need to be tracked?

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AWS Machine Learning

MAY 10, 2023

It provides a suite of tools for visualizing training metrics, examining model architectures, exploring embeddings, and more. TensorFlow and PyTorch projects both endorse and use TensorBoard in their official documentation and examples. is your training script, and simple_tensorboard.ipynb launches the SageMaker training job.

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AWS Machine Learning

AUGUST 10, 2023

SageMaker services, such as Processing, Training, and Hosting, collect metrics and logs from the running instances and push them to users’ Amazon CloudWatch accounts. An example dashboard is shown in the following screenshot. Convert event information into metrics in certain log formats as ingested as EMF logs. aws/config.

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AWS Machine Learning

NOVEMBER 15, 2023

For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering. For this example, you don’t use a specialized dataset; instead, you work with the California Housing dataset that you will import from Amazon Simple Storage Service (Amazon S3).

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AWS Machine Learning

NOVEMBER 15, 2024

For example, you can use Amazon Bedrock Guardrails to filter out harmful user inputs and toxic model outputs, redact by either blocking or masking sensitive information from user inputs and model outputs, or help prevent your application from responding to unsafe or undesired topics.

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AWS Machine Learning

OCTOBER 18, 2024

Use Qualcomm AI Hub to compile and profile the model, running it on cloud-hosted devices to deliver performance metrics ahead of downloading for deployment across edge devices. Let’s walk through this scenario with an implementation example. We’ve prepared a script called train_yolov8.py environment. An AWS account.

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AWS Machine Learning

OCTOBER 21, 2024

If you have a different format, you can potentially use Llama convert scripts or Mistral convert scripts to convert your model to a supported format. The fine-tuning scripts are based on the scripts provided by the Llama fine-tuning repository. from sagemaker.s3 3B model Now, we’ll fine-tune the Llama 3.2 from sagemaker.s3

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AWS Machine Learning

OCTOBER 24, 2024

Anatomy of RAG RAG is an efficient way to provide an FM with additional knowledge by using external data sources and is depicted in the following diagram: Retrieval : Based on a user’s question (1), relevant information is retrieved from a knowledge base (2) (for example, an OpenSearch index).

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AWS Machine Learning

JANUARY 10, 2023

From there, we dive into how you can track and understand the metrics and performance of the SageMaker endpoint utilizing Amazon CloudWatch metrics. The entire set of code for the example is available in the following GitHub repository. Metrics to track. Instance-level metrics. Invocation metrics.

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AWS Machine Learning

NOVEMBER 1, 2024

For example, “Show me my backup failures for the past 72 hours, grouped by server.” We curated a small list of examples in question-API route pairs, which helped improve the accuracy and make the output format more consistent. For example, “Create a pandas dataframe and group it by server column.”

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APIs Scripts Engineering Government 105

AWS Machine Learning

JANUARY 19, 2024

One example is an online retailer who deploys a large number of inference endpoints for text summarization, product catalog classification, and product feedback sentiment classification. The performance of the architecture is typically measured using metrics such as validation loss. Choose System terminal under Utilities and files.

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