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Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

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 119
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Customer Experience Automation: Transforming the Future of Customer Service

TechSee

Today, CXA encompasses various technologies such as AI, machine learning, and big data analytics to provide personalized and efficient customer experiences. The development of chatbots, automated email responses, and AI-driven customer support tools marked a new era in customer service automation.

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

AWS Machine Learning

Challenges in data management Traditionally, managing and governing data across multiple systems involved tedious manual processes, custom scripts, and disconnected tools. This approach was not only time-consuming but also prone to errors and difficult to scale.

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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

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")

Scripts 125
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Host the Spark UI on Amazon SageMaker Studio

AWS Machine Learning

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. install-scripts chmod +x install-history-server.sh./install-history-server.sh script and attach it to an existing SageMaker Studio domain.

Scripts 79
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MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning

Refer to Operating model for best practices regarding a multi-account strategy for ML. 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. Refer to the./env_files/dev_env.tfvars

Scripts 85
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker. default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket resource("s3").Bucket Bucket (bucket).Object

Scripts 121