MLOps Machine Learning Operations Specialization

By theetay.com Categories: Coursera
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About Course

MLOps Machine Learning Operations Specialization, This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You’ll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You’ll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps. This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers.

You’ll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You’ll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps. Through this series, you will begin to learn skills for various career paths: 1. Data Science – Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making. 2. Machine Learning Engineering – Design, build, and deploy ML models and systems to solve real-world problems. 3. Cloud ML Solutions Architect – Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner. 4. Artificial Intelligence (AI) Product Management – Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

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What Will You 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.

Course Content

01. Python Essentials for MLOps

  • 004 02_meet-your-supporting-instructor-noah-gift_instructions.html
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  • 005 03_course-structure-and-discussion-etiquette_instructions.html
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  • 008 01_lesson-introduction-variables-and-types.mp4
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  • 011 02_variables-and-assignments.mp4
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  • 014 03_working-with-different-data-types.mp4
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  • 017 04_conditionals-and-evaluations.mp4
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  • 020 05_catching-and-handling-exceptions.mp4
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  • 023 06_lesson-recap-variables-and-types.mp4
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  • 026 01_lesson-introduction-python-data-structures.mp4
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  • 029 02_introduction-to-lists.mp4
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  • 032 03_creating-and-iterating-over-lists.mp4
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  • 035 04_introduction-to-dictionaries.mp4
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  • 038 05_creating-and-iterating-over-dictionaries.mp4
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  • 041 06_other-data-structures-tuples-and-sets.mp4
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  • 044 07_lesson-recap-python-data-structures.mp4
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  • 045 08_minimal-python-book-storing-data_instructions.html
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  • 048 01_lesson-introduction-adding-and-extracting-data.mp4
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  • 051 02_adding-data-to-lists.mp4
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  • 054 03_extracting-data-from-lists.mp4
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  • 057 04_extracting-data-from-dictionaries.mp4
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  • 060 05_lesson-recap-adding-and-extracting-data.mp4
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  • 061 06_python-basics_exam.html
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  • 064 01_lesson-introduction-working-with-functions.mp4
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  • 067 02_function-structure-and-values.mp4
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  • 070 03_function-arguments.mp4
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  • 073 04_variable-and-keyword-arguments.mp4
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  • 076 05_lesson-recap-working-with-functions.mp4
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  • 077 06_minimal-python-book-create-functions_instructions.html
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  • 080 01_lesson-introduction-building-classes-and-methods.mp4
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  • 083 02_introduction-to-classes.mp4
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  • 086 03_using-a-constructor.mp4
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  • 089 04_adding-methods.mp4
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  • 092 05_class-inheritance.mp4
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  • 095 06_lesson-recap-building-classes-and-methods.mp4
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  • 098 01_lesson-introduction-modules-and-advanced-usages.mp4
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  • 101 02_introduction-to-python-modules.mp4
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  • 104 03_working-with-imports.mp4
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  • 107 04_working-with-python-scripts.mp4
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  • 110 05_virtual-environments-and-dependencies.mp4
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  • 113 06_lesson-recap-modules-and-advanced-usages.mp4
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  • 114 07_python-functions-and-classes_exam.html
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  • 115 08_python-for-beginners-learning-path_instructions.html
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  • 118 01_lesson-introduction-writing-and-executing-tests.mp4
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  • 121 02_motivations-for-testing-in-python.mp4
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  • 124 03_testing-conventions.mp4
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  • 127 04_testing-with-pytest.mp4
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  • 130 05_lesson-recap-writing-and-executing-tests.mp4
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  • 133 01_lesson-introduction-writing-useful-tests.mp4
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  • 136 02_using-plan-asserts-in-pytest.mp4
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  • 139 03_writing-test-classes.mp4
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  • 142 04_test-classes-vs-test-functions.mp4
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  • 145 05_parameterizing-tests.mp4
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  • 148 06_lesson-recap-writing-useful-tests.mp4
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  • 151 01_lesson-introduction-testing-failures.mp4
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  • 154 02_test-failure-output.mp4
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  • 157 03_python-debugging-with-pdb.mp4
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  • 160 04_other-pytest-runner-options.mp4
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  • 163 05_pytest-fixtures.mp4
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  • 166 06_lesson-recap-testing-failures.mp4
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  • 167 07_python-testing_exam.html
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  • 170 01_lesson-introduction-basic-pandas-usage.mp4
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  • 173 02_introduction-to-pandas.mp4
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  • 176 03_loading-data-into-pandas.mp4
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  • 179 04_writing-data-from-pandas-dataframes.mp4
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  • 182 05_exploratory-analysis-with-pandas.mp4
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  • 185 06_lesson-recap-basic-pandas-usage.mp4
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  • 188 01_lesson-introduction-working-with-dataframes.mp4
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  • 191 02_common-dataframe-operations.mp4
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  • 194 03_manipulating-text-in-dataframes.mp4
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  • 197 04_applying-functions-with-pandas.mp4
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  • 200 05_visualizing-data-with-pandas.mp4
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  • 203 06_lesson-recap-working-with-dataframes.mp4
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  • 206 01_lesson-introduction-numpy-basics.mp4
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  • 209 02_introduction-to-numpy-arrays.mp4
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  • 212 03_common-numpy-array-operations.mp4
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  • 215 04_more-numpy-array-operations.mp4
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  • 218 05_lesson-recap-numpy-basics.mp4
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  • 219 06_pandas-and-numpy_exam.html
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  • 222 01_lesson-introduction-apis-and-sdks.mp4
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  • 225 02_installing-azure-command-line-interface-cli.mp4
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  • 228 03_azureml-studio-with-python.mp4
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  • 231 04_hugging-face-transformers.mp4
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  • 234 05_hugging-face-datasets.mp4
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  • 237 06_azure-open-datasets.mp4
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  • 240 07_lesson-recap-apis-and-sdks.mp4
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  • 243 01_lesson-introduction-automation-with-command-line-tools.mp4
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  • 246 02_creating-a-single-file-script.mp4
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  • 249 03_using-the-argparse-framework.mp4
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  • 252 04_declaring-dependencies.mp4
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  • 255 05_using-the-click-framework.mp4
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  • 258 06_packaging-your-project.mp4
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  • 261 07_solving-a-machine-learning-problem-with-a-cli-tool.mp4
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  • 264 08_lesson-recap-automation-with-command-line-tools.mp4
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  • 267 01_lesson-introduction-building-machine-learning-apis.mp4
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  • 270 02_introduction-to-flask-framework.mp4
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  • 273 03_building-an-api-with-flask.mp4
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  • 276 04_introduction-to-the-fastapi-framework.mp4
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  • 279 05_building-an-api-with-fastapi.mp4
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  • 282 06_python-api-best-practices.mp4
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  • 285 07_lesson-recap-building-machine-learning-apis.mp4
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  • 286 08_automation-with-python_exam.html
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02. DevOps, DataOps, MLOps

03. MLOps Platforms Amazon SageMaker and Azure ML

04. MLOps Tools MLflow and Hugging Face

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