Machine Learning Operations Professional Certificate

What you will learn:

  • Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
  • Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
  • Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
  • Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
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DevOps, DataOps, MLOps (edX) EdX
AI (Pragmatic AI Labs)

DevOps, DataOps, MLOps (edX)

Computer Science

Unlock the full potential of your machine learning projects by mastering DevOps, DataOps, and MLOps. This course will guide you through end-to-end operations, ensuring seamless integration from data collection to model deployment. Enhance your skills in automating processes, managing data pipelines, and deploying ML models efficiently.

Self Paced
Self-Paced
Machine Learning DevOps Docker
Details
MLOps Tools: MLflow and Hugging Face (edX) EdX
AI (Pragmatic AI Labs)

MLOps Tools: MLflow and Hugging Face (edX)

Computer Science

Embark on an in-depth exploration of MLOps with our course dedicated to MLflow and Hugging Face. This course is designed for data scientists and engineers looking to efficiently manage their machine learning projects from development to deployment. Discover how to leverage these powerful tools to automate the ML lifecycle, optimize model performance, and ensure seamless integration into production environments.

Self Paced
Self-Paced
Machine Learning MLOps Machine Learning Operations
Details
MLOps Platforms: Amazon SageMaker and Azure ML (edX) EdX
AI (Pragmatic AI Labs)

MLOps Platforms: Amazon SageMaker and Azure ML (edX)

Computer Science

Elevate Your MLOps Game: Master AWS SageMaker and Azure ML for Production-Ready AI Solutions. This course is designed for data scientists, developers, and IT professionals who want to learn how to effectively deploy, monitor, and manage machine learning models in production using Amazon's SageMaker and Microsoft's Azure Machine Learning platforms.

Self Paced
Self-Paced
Exploratory Data Analysis Azure ML Amazon SageMaker
Details
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