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Diploma in DevOps & Artificial Intelligence Advancement Level 5 RQF -…
Diploma in DevOps & Artificial Intelligence Advancement Level 5 RQF
Amazon SageMaker is used for building and training machine learning models, while AWS CodeCommit is used for version control and collaboration among developers.
AWS CodePipeline is used to automate the deployment of machine learning models created in Amazon SageMaker, making it easy to deploy and scale them.
Amazon SageMaker is used for training natural language processing models, while AWS Lambda is used for serverless computing to create chatbots and voice assistants.
Amazon SageMaker is used for building recommendation engines, while AWS CodePipeline is used for continuous integration and deployment of these models.
AWS Lambda is used for serverless computing to preprocess data for machine learning models built in Amazon SageMaker.
Amazon SageMaker is used for building computer vision models, while Amazon Rekognition is used for object detection and image recognition.
AWS CloudFormation is used for infrastructure as code, while Amazon SageMaker is used for creating and deploying machine learning models.
AWS CodePipeline is used for continuous integration and deployment of machine learning models created in Amazon SageMaker, while AWS Step Functions is used for workflow automation.
AWS Glue is used for data integration and ETL processes, while Amazon SageMaker is used for building and deploying machine learning models.
Amazon SageMaker is used for anomaly detection in log data, while AWS CloudTrail is used for auditing and monitoring AWS resource usage.
AWS CodeDeploy is used for deploying machine learning models created in Amazon SageMaker, while Amazon CloudWatch is used for monitoring and logging.
AWS CodeBuild is used for continuous integration of machine learning models built in Amazon SageMaker, while Amazon S3 is used for storing data and models.
AWS CodePipeline is used for continuous delivery of machine learning models built in Amazon SageMaker, while Amazon Kinesis is used for real-time data streaming.
Amazon SageMaker is used for building and deploying machine learning models for predictive maintenance, while AWS IoT Core is used for device connectivity and data ingestion.
Amazon SageMaker is used for fraud detection and prevention, while AWS Identity and Access Management (IAM) is used for access control and security.
AWS Lambda is used for serverless computing to create predictive models, while Amazon Simple Queue Service (SQS) is used for message queuing and processing.
Amazon SageMaker is used for building and deploying machine learning models for image and video analysis, while Amazon Elastic Transcoder is used for video transcoding.
AWS CodeStar is used for team collaboration and project management, while Amazon SageMaker is used for building and deploying machine learning models.
AWS Batch is used for batch processing of large datasets, while Amazon SageMaker is used for building and deploying machine learning models.
AWS Elastic Beanstalk is used for deploying web applications, while Amazon SageMaker is used for building and deploying machine learning models for personalized recommendations based on user behavior.
Azure DevOps for Continuous Integration and Continuous Deployment (CI/CD) pipeline, with Azure Machine Learning used to train and deploy machine learning models.
Azure DevOps used for version control, work item tracking, and project management for an AI-based application development project, with Azure Cognitive Services used for natural language processing and computer vision.
Azure Kubernetes Service used to deploy containerized AI applications developed in Azure DevOps, with Azure Cognitive Services used for image and speech recognition.
Azure DevOps used for code collaboration and version control, with Azure Databricks used for big data analytics and machine learning.
Azure DevOps used for automated testing of AI-based applications developed in Azure Machine Learning, with Azure Cosmos DB used to store and manage data.
Azure DevOps used for continuous integration and deployment of an AI-based chatbot developed with Azure Cognitive Services.
Azure DevOps used for CI/CD pipeline for an AI-based recommendation engine developed with Azure Machine Learning.
Azure DevOps used for version control and continuous deployment of an AI-based fraud detection system developed with Azure Cognitive Services.
Azure DevOps used for code management and project management of a large-scale AI-based analytics project, with Azure Synapse Analytics used for data management.
Azure DevOps used for CI/CD pipeline of an AI-based forecasting model developed with Azure Machine Learning, with Azure Data Factory used for data ingestion and transformation.
Azure DevOps used for continuous delivery of an AI-based autonomous vehicle system developed with Azure Cognitive Services.
Azure DevOps used for version control and project management of an AI-based chatbot developed with Azure Bot Service.
Azure DevOps used for CI/CD pipeline of an AI-based image recognition system developed with Azure Cognitive Services.
Azure DevOps used for automated testing of an AI-based recommendation engine developed with Azure Machine Learning.
Azure DevOps used for code collaboration and version control of an AI-based predictive maintenance system developed with Azure Machine Learning.
Azure DevOps used for CI/CD pipeline of an AI-based natural language processing system developed with Azure Cognitive Services.
Azure DevOps used for version control and project management of an AI-based fraud prevention system developed with Azure Cognitive Services.
Azure DevOps used for automated testing of an AI-based forecasting model developed with Azure Machine Learning.
Azure DevOps used for continuous delivery of an AI-based medical diagnosis system developed with Azure Cognitive Services.
Azure DevOps used for code management and project management of an AI-based anomaly detection system developed with Azure Machine Learning.