DP-100 | Designing and Implementing a Data Science Solution on AzureSaturday 01 January
This course is designed to teach data scientists 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.
The outcomes of this course for delegates are:
- Understanding and working with machine learning
- Provisioning a machine learning workspace
- Working with tools and code for machine learning
- Training and code-based experiments
- Creating and using Datastores and Datasets
- Creating and using Environments and Compute Targets
- Creating and publishing Pipelines
- Understanding real time and batch inferencing
- Using hyperparameter tuning and automating machine learning
- Understanding differential privacy and fairness
- Monitoring models for insights and data drift
The course, along with MS Learn, is designed to help delegates get an understanding of the topics covered on the DP-100 certification exam.
Special discounted pricing for Vuzion Partners can be found on the price guide within the Vuzion Partner portal here or quoted by your BDM. You can also reach out to us at email@example.com
This course is constituted as follows
- Theoretical: 30%
- Practical - Demo: 20%
- Practical - Tasks, Activities or Labs: 50%
Percentages are approximate. Whilst we endeavor to include a blend of theory, demo and activities in all of our courses we recognise that some delegates prefer a particular style of course (I.e. wholly or predominantly lab based workshops or immersion experiences) and would ask them to consider their preferred learning style before registration. We would also ask delegates to check the site regularly for new courses as we expand to offer different blends of theory/practice
Who Should Attend?
Course Level: 300/400. 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.
Roles Aimed at: Data Scientist or any IT professional interested in the field of data science, looking to consolidate their deep knowledge, or looking to certify.
The course is designed for data scientists. It is important to point out that this course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that. Please see prerequisites for more information
This course is not intended for:
- Inexperienced users in the field of Data Science
- Inexperienced users of Azure
- Inexperienced users of Python
- Non-technical roles
Certifications & Competencies
This course is linked to exam DP-100 Designing and Implementing a Data Science Solution on Azure. For Microsoft Partners, this exam is linked to the Data Analytics competency
Exam is not included in the cost of the course and should be paid for separately.
This course has the following prerequisites:
- A modern laptop/desktop
- A modern browser (Edge/Chrome)
- A good internet connection (wired recommended)
Delegates must have:
- Experience in training, building, and evaluating machine learning models.
- A solid command of Python and data science libraries and frameworks including: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, PyTorch, and TensorFlow.
- Experience with statistical learning concepts such as: regression, correlation, normalization, feature importance, and AUC – ROC
- Experience of working with Containers.
- Azure Fundamentals
Should you have any question related to this course, please do not hesitate to contact us at firstname.lastname@example.org
Upon submission, we will reach out and work with you to find the next available course to fit your schedule
Register today!Limited spaces available
Modules covered include:
- Getting Started with Azure Machine Learning
- Visual Tools for Machine Learning
- Running Experiments and Training Models
- Working with Data
- Working with Compute
- Orchestrating Operations with Pipelines
- Deploying and Consuming Models
- Training Optimal Models
- Responsible Machine Learning
- Monitoring Models
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