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

APRIL 1, 2024

However, training or fine-tuning these large models for a custom use case requires a large amount of data and compute power, which adds to the overall engineering complexity of the ML stack. Most of the details will be abstracted by the automation scripts that we use to run the Llama2 example. Cluster with p4de.24xlarge

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AWS Machine Learning

OCTOBER 23, 2023

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com.

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AWS Machine Learning

NOVEMBER 19, 2024

These codes, such as P0300 for a generic engine misfire or C1201 for an ABS system fault, are crucial for precise diagnosis. We compare a customized SLM with a general purpose LLM, using various metrics to assess vocabulary richness and overall accuracy. For this post, we use Meta Llama3.1 8B Instruct from Hugging Face as the SLM.

Automotive 82

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AWS Machine Learning

JULY 25, 2024

The node recovery agent is a separate component that periodically checks the Prometheus metrics exposed by the node problem detector. Additionally, the node recovery agent will publish Amazon CloudWatch metrics for users to monitor and alert on these events. You can see the CloudWatch NeuronHasError_DMA_ERROR metric has the value 1.

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AWS Machine Learning

NOVEMBER 15, 2024

For automatic model evaluation jobs, you can either use built-in datasets across three predefined metrics (accuracy, robustness, toxicity) or bring your own datasets. For early detection, implement custom testing scripts that run toxicity evaluations on new data and model outputs continuously.

APIs 85

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AWS Machine Learning

NOVEMBER 22, 2023

Training script Before starting with model training, we need to make changes to the training script to make it XLA compliant. We followed the instructions provided in the Neuron PyTorch MLP training tutorial to add XLA-specific constructs in our training scripts. These code changes are straightforward to implement.

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AWS Machine Learning

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. The PyTorch estimator from the sagemaker.pytorch package allows us to run our own training script in a managed PyTorch container.

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AWS Machine Learning

OCTOBER 18, 2024

AI Hub offers automatic conversion and optimization of PyTorch or ONNX models for efficient on-device deployment using TensorFlow Lite, ONNX Runtime, or Qualcomm AI Engine Direct SDK. Review the training script You’re now prepared to fine-tune the model using the model.train method from the Ultralytics YOLO library.

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AWS Machine Learning

OCTOBER 2, 2023

We can follow a simple three-step process to convert an experiment to a fully automated MLOps pipeline: Convert existing preprocessing, training, and evaluation code to command line scripts. Use the scripts created in step one as part of the processing and training steps. Integrate the pipeline into your CI/CD workflow.

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AWS Machine Learning

NOVEMBER 1, 2024

Each step became an independent stream, so our engineers could iteratively develop and evaluate the performance and speed until they worked well in isolation. Then it passes the sample data and the user question to an FM and responds with a Python script that transforms data to answer the question.

APIs 104

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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. degree in Electrical Engineering. from sagemaker.s3

APIs 135

APIs Scripts Finance Real estate 135

AWS Machine Learning

MAY 29, 2024

bin/bash # Set the prompt and model versions directly in the command deepspeed /root/LLaVA/llava/train/train_mem.py --deepspeed /root/LLaVA/scripts/zero2.json It sets up a SageMaker training job to run the custom training script from LLaVA. He has over a decade experience in the FinTech industry as software engineer.

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Scripts Healthcare Metrics Finance 121

AWS Machine Learning

JUNE 27, 2023

This post explains how Provectus and Earth.com were able to enhance the AI-powered image recognition capabilities of EarthSnap, reduce engineering heavy lifting, and minimize administrative costs by implementing end-to-end ML pipelines, delivered as part of a managed MLOps platform and managed AI services.

Engineering 113

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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. Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step.

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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. Amazon Athena is a serverless SQL query engine that natively supports Iceberg management procedures. AWS Glue Job setup.

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AWS Machine Learning

FEBRUARY 19, 2024

Before you can write scripts that use the Amazon Bedrock API, you need to install the appropriate version of the AWS SDK in your environment. For Python scripts, you can use the AWS SDK for Python (Boto3). For more information, refer to Prompt Engineering Guidelines. exclusive) to 10.0 Parse and decode the response.

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AWS Machine Learning

JANUARY 19, 2024

The concepts illustrated in this post can be applied to applications that use PLM features, such as recommendation systems, sentiment analysis, and search engines. The performance of the architecture is typically measured using metrics such as validation loss. training.py ).

Metrics 115

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AWS Machine Learning

APRIL 19, 2023

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. They use the DJL PyTorch engine to initialize the model predictor.

Engineering 82

Engineering APIs Scripts Education 82

Hodusoft

NOVEMBER 6, 2024

Service Level Agreements (SLAs): Ensure compliance with SLAs, which outline expected service levels and performance metrics. Identify Key Metrics and KPIs Key Performance Indicators (KPIs) are vital for measuring the success of contact center operations. This will enable you to create tailored strategies to enhance overall experience.

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Best practices Contact Center Contact center software Abandon rate 52

AWS Machine Learning

JULY 14, 2022

Data scientists often work towards understanding the effects of various data preprocessing and feature engineering strategies in combination with different model architectures and hyperparameters. In each individual experiment, we track input and output artifacts, code, and metrics using SageMaker Experiments. SageMaker Experiments.

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AWS Machine Learning

MARCH 2, 2023

Set up an EKS cluster with a scalable file system One way to get started with Amazon EKS is aws-do-eks , which is an open-source project offering easy-to-use and configurable scripts and tools to provision EKS clusters and run distributed training jobs. script exists in a Docker image that copies data from Amazon S3 to Amazon EFS.

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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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AWS Machine Learning

NOVEMBER 1, 2022

Data scientists need to only provide a tabular dataset and select the target column to predict, and Autopilot automatically infers the problem type, performs data preprocessing and feature engineering, selects the algorithms and training mode, and explores different configurations to find the best ML model. script creates an Autopilot job.

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AWS Machine Learning

FEBRUARY 12, 2024

For hyperparameter tuning, a hyperparameter optimization (HPO) job can be initiated, selecting the best model based on the objective metric. Evaluate – A PySpark processing job evaluates the model using a custom Spark script. This decision is based on a condition metric defined in the configuration file.

Engineering 128

Engineering Wait times Metrics Accountability 128

AWS Machine Learning

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. For Script Path , enter Jenkinsfile. upload_file("pipelines/train/scripts/raw_preprocess.py","mammography-severity-model/scripts/raw_preprocess.py") s3_client.Bucket(default_bucket).upload_file("pipelines/train/scripts/evaluate_model.py","mammography-severity-model/scripts/evaluate_model.py")

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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. One example is performing a metric query on the SageMaker job host’s utilization metrics when a job completion event is received.

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AWS Machine Learning

AUGUST 29, 2023

If the model quality metric (for example, RMSE for regression and F1 score for classification) doesn’t meet a pre-specified criterion, the model quality check step is marked as failed. The data engineer is responsible for the following: Uploading labeled training data to the appropriate path in Amazon S3.

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CSM Magazine

APRIL 17, 2024

Understand Inputs and Outputs People analytics and customer service systems provide countless metrics that can help you assess the balance of efforts vs. outcomes. Some important input metrics include how your team spends their time and on which tasks. This can help answer questions: Where does overwork exist?

Customer Service 59

Customer Service Scripts Banking Analytics 59

AWS Machine Learning

MAY 8, 2023

To serve models, Triton supports various backends as engines to support the running and serving of various ML models for inference. With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Import the ONNX model into TensorRT and generate the TensorRT engine.

Engineering 93

Engineering APIs Best practices Scripts 93

AWS Machine Learning

SEPTEMBER 30, 2024

To evaluate the question answering task, we use the metrics F1 Score, Exact Match Score, Quasi Exact Match Score, Precision Over Words, and Recall Over Words. The FMEval library supports out-of-the-box evaluation algorithms for metrics such as accuracy, QA Accuracy, and others detailed in the FMEval documentation.

APIs 83

APIs Real estate Accountability Metrics 83

AWS Machine Learning

FEBRUARY 13, 2024

Before moving to full-scale production, BigBasket tried a pilot on SageMaker to evaluate performance, cost, and convenience metrics. Log model training metrics. Each worker then proceeds with the forward and backward pass defined in your training script on each GPU. Copy the final model to an S3 bucket.

Engineering 119

Engineering Gamification Big data Scripts 119

AWS Machine Learning

MAY 30, 2023

Data Wrangler reduces the time it takes to aggregate and prepare data by simplifying the process of data source integration and feature engineering using a single visual interface and a fully distributed data processing environment. This is a great way to test your scripts before running them in a SageMaker managed environment.

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AWS Machine Learning

MAY 10, 2023

Examples of such use cases include scaling up a feature engineering job that was previously tested on a small sample dataset on a small notebook instance, running nightly reports to gain insights into business metrics, and retraining ML models on a schedule as new data becomes available.

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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'

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AWS Machine Learning

NOVEMBER 15, 2023

If you don’t want to change the quota, you can simply modify the value of the MAX_PARALLEL_JOBS variable in the script (for example, to 5). Training script template The AutoML workflow in this post is based on scikit-learn preprocessing pipelines and algorithms. Note that individual pipeline scripts are not created yet at this point.

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AWS Machine Learning

FEBRUARY 20, 2024

Trusted by the biggest names in entertainment, ZOO Digital delivers high-quality localization and media services at scale, including dubbing, subtitling, scripting, and compliance. git+[link] ffmpeg-python Create an inference script to load the models and run inference Next, we create a custom inference.py

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