Generic filters
Exact matches only
Search in title
Search in content
Search in excerpt

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course 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 in Microsoft Azure. Audience Profile:- This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

  • Price: $2,595.00
  • Duration: 1 day
  • Delivery Methods: Virtual
Date Time Price Option
Please contact us at info@toptalentlearning.com or 469-721-6100 for this course schedule.
For questions call: (469) 721-6100

Why choose
TOPTALENT?

  • Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
  • Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
  • Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
  • Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.

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 – 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

5 – Explore developer tools for workspace interaction

  • Explore the studio
  • Explore the Python SDK
  • Explore the CLI

6 – Make data available in Azure Machine Learning

  • Video – Make data available in Azure Machine Learning
  • Understand URIs
  • Create a datastore
  • Create a data asset

7 – 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

8 – Work with environments in Azure Machine Learning

  • Understand environments
  • Explore and use curated environments
  • Create and use custom environments

9 – Find the best classification model with Automated Machine Learning

  • Video – Find the best classification model with Automated Machine Learning
  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models

10 – Track model training in Jupyter notebooks with MLflow

  • Configure MLflow for model tracking in notebooks
  • Train and track models in notebooks

11 – Run a training script as a command job in Azure Machine Learning

  • Video – 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

12 – Track model training with MLflow in jobs

  • Track metrics with MLflow
  • View metrics and evaluate models

13 – Run pipelines in Azure Machine Learning

  • Video – Run pipelines in Azure Machine Learning
  • Create components
  • Create a pipeline
  • Run a pipeline job

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 – 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

16 – 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

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course 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 in Microsoft Azure.

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

  • Creating cloud resources in Microsoft Azure.
  • Using Python to explore and visualize data.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
  • Working with containers
  • AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.
Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.
Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.
You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.
If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.
You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.