DP-100: Designing and Implementing a Data Science Solution on Azure

Course Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. The DP-100 class teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Audience profile This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models. Prerequisites Before attending this DP-100 course, students must have: • Azure Fundamentals Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one. • Python programming ability and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn. Interested in Getting Certification? Check out our Exam Pak option! Prepare for Microsoft Certification by getting our Exam Pak -- a $500 value you can get for free! Optional Exam Pak includes: • 24/7 Online Support Need assistance while you are learning? Chat with our 24/7 online support specialists. And, with your permission, the expert can even take over your computer to provide assistance. (90-day access.) • Practice Exam Software You may study at your own pace with this web-based practice exam. Exam-like questions are designed to help you prepare for your certification exam by validating your knowledge and reinforcing key concepts. • Exam Voucher with Exam-Pass Guarantee Prepare for your exam using the practice software. Once you have achieved an 85% or above score, contact us and we will provide you with an exam voucher. Didn't pass the first time? Not a problem--you will get a second voucher with our Exam-Pass Guarantee - For details visit ` Exam Pass Guarantee
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**Call our office to order the Exam Pak when registering for this class. Exam Paks are only available with this promotion when you register for your class. This promotion is valid on new registrations only and cannot be combined with other offers.** COURSE OUTLINE 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints