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Overview

<p aria-level="1" paraeid="{b77da3ef-5114-444b-8b32-17dc466666b7}{205}" paraid="334772353" role="heading"><span style="font-size:14px;"><span style="font-family:segoe ui;">Welcome to the <strong>Designing and</strong> <strong>Implementing a Data Science Solution on Azure</strong> course. </span></span></p>

<p paraeid="{b77da3ef-5114-444b-8b32-17dc466666b7}{225}" paraid="1974288660"><span style="font-size:14px;"><span style="font-family:segoe ui;">In this three-day course learners will learn how to operate <strong>machine learning solutions at cloud scale</strong> using <strong>Azure Machine Learning</strong>. This course teaches you to leverage your existing knowledge of Python and machine learning to <strong>manage data</strong> <strong>ingestion</strong> and preparation, model training and deployment, and machine learning solution monitoring in <strong>Microsoft Azure.</strong> </span></span></p>

<p paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{8}" paraid="724548523"><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Job role:</strong> Data Scientist </span></span></p>

<p><span style="font-size:18px;"><span style="font-family:segoe ui;"><strong>Features & Benefits</strong></span></span></p>

<ul>
	<li><span style="font-size:14px;"><span style="font-family:segoe ui;"><span style="font-size:14px;"><span style="font-family:segoe ui;"><span style="font-size:14px;"><span style="font-family:segoe ui;"></span></span><span style="font-size:14px;"><span style="font-family:segoe ui;">This is an authorised <strong>Microsoft Official Course (MOC)</strong>, a preparation course for <a href="https://docs.microsoft.com/en-us/learn/certifications/exams/dp-100" rel="noreferrer noopener" target="_blank">Exam: DP-100: Designing and Implementing a Data Science Solution on Azure certification.</a> </span></span> </span></span></span></span></li>
</ul>

<ul>
	<li><span style="font-size:14px;"><span style="font-family:segoe ui;">Learners will gain confidence to take other <strong>role-based courses</strong> and <strong>certifications. </strong></span></span></li>
	<li><span style="font-size:14px;"><span style="font-family:segoe ui;"><span style="font-size:14px;"><span style="font-family:segoe ui;">An <strong>additional fee</strong> is payable for exam vouchers. </span></span></span></span></li>
	<li><span style="font-size:14px;"><span style="font-family:segoe ui;">Take full advantage of our new <strong>Hybrid Learning</strong> by attending <strong>on campus</strong> or <strong>virtually.</strong> Have all your classes ready to be <strong>downloaded</strong> and <strong>watched</strong>, anytime, anywhere. <a href="https://learnfast.co.za/pgs-153-hybrid-learning.html">(Read More)</a></span></span></li>
</ul>

<ul>
	<li><span style="font-size:14px;"><span style="font-family:segoe ui;"><span style="font-size:14px;"><span style="font-family:segoe ui;">Attendees will learn practical skills which can be applied in the work environment.  </span></span></span></span></li>
</ul>

Duration

Career Options

Financing Options

Admission Requirements

<p paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{196}" paraid="1705708922"><span style="font-size:14px;"><span style="font-family:segoe ui;">Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. </span></span></p>

<p paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{202}" paraid="1848705432"><span style="font-size:14px;"><span style="font-family:segoe ui;">Specifically: </span></span></p>

<ul>
	<li paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{208}" paraid="1763124024">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Creating cloud resources in Microsoft Azure. </span></span></div>
	</li>
	<li paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{215}" paraid="934221581">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Using Python to explore and visualize data. </span></span></div>
	</li>
	<li paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{222}" paraid="1393828682">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. </span></span></div>
	</li>
	<li paraeid="{4bf3def0-b952-4480-916f-551300db3ea3}{229}" paraid="1184544859">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with containers </span></span></div>
	</li>
</ul>

Important Notes

<ul>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{151}" paraid="804321541">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Learners to arrive at the training venue from 08:00 in preparation for 08:30 starting time </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{174}" paraid="1182152953">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Bookings are only confirmed upon receipt of the proof of payment or an official company purchase order. </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{181}" paraid="1081372771">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">For full day, on campus courses, Learnfast will supply you with a computer to use for training (if applicable),& tea/coffee and a full lunch. Catering is not included for On-Site training and laptops are available for hire at an additional cost if required. </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{212}" paraid="1309175345">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Cancellation or rescheduling requests must be in writing and reach us via email at least 5-10 working days prior to the course commencement date. Full course fees will be retained for no shows. </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{219}" paraid="880286382">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Virtual learners are required to have a stable internet connection & a working headset available for sound purposes.  </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{226}" paraid="2124695199">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Learners who use their own laptops are fully responsible to ensure that administration rights, software installations, etc. are working sufficiently prior to training. </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{241}" paraid="2134258061">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Learnfast reserves the right to cancel or postpone dates if we require to do so and undertake to inform clients in writing and telephonically of these changes. </span></span></div>
	</li>
	<li paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{250}" paraid="1561849529">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Learnfast is not responsible for costs associated with cancellation of classes such as flight and accommodation for clients. </span></span></div>
	</li>
</ul>

CourseOutline

<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 1: Getting Started with Azure Machine Learning</strong> </span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="203" role="listitem">
	<div paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{232}" paraid="1606066122"><span style="font-size:14px;"><span style="font-family:segoe ui;">Introduction to Azure Machine Learning </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="203" role="listitem">
	<div paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{239}" paraid="361822100"><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with Azure Machine Learning</span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 2: No-Code Machine Learning</strong> </span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="204" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{17}" paraid="125554865"><span style="font-size:14px;"><span style="font-family:segoe ui;">Automated Machine Learning </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="204" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{24}" paraid="872531522"><span style="font-size:14px;"><span style="font-family:segoe ui;">Azure Machine Learning Designer </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 3: Running Experiments and Training Models </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.</span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="205" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{55}" paraid="1099692259"><span style="font-size:14px;"><span style="font-family:segoe ui;">Introduction to Experiments </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="205" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{62}" paraid="836237012"><span style="font-size:14px;"><span style="font-family:segoe ui;">Training and Registering Models </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 4: Working with Data </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage data stores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.</span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="206" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{93}" paraid="2116347172"><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with Datastores </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="206" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{100}" paraid="1752663979"><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with Datasets </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 5: Working with Compute </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="207" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{133}" paraid="1672292226"><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with Environments </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="207" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{140}" paraid="80838616"><span style="font-size:14px;"><span style="font-family:segoe ui;">Working with Compute Targets </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 6: Orchestrating Operations with Pipelines </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="208" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{173}" paraid="2036030938"><span style="font-size:14px;"><span style="font-family:segoe ui;">Introduction to Pipelines </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="208" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{180}" paraid="1093431002"><span style="font-size:14px;"><span style="font-family:segoe ui;">Publishing and Running Pipelines </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 7: Deploying and Consuming Models </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="209" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{215}" paraid="367789819"><span style="font-size:14px;"><span style="font-family:segoe ui;">Real-time Inferencing </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="209" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{222}" paraid="450131679"><span style="font-size:14px;"><span style="font-family:segoe ui;">Batch Inferencing </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="3" data-font="Symbol" data-leveltext="" data-listid="209" role="listitem">
	<div paraeid="{03c427cf-614b-4b32-985f-ee5eb2f66bb4}{229}" paraid="1675688068"><span style="font-size:14px;"><span style="font-family:segoe ui;">Continuous Integration and Delivery </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 8: Training Optimal Models </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="210" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{7}" paraid="889361017"><span style="font-size:14px;"><span style="font-family:segoe ui;">Hyperparameter Tuning </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="210" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{14}" paraid="1052378265"><span style="font-size:14px;"><span style="font-family:segoe ui;">Automated Machine Learning </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 9: Responsible Machine Learning </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="211" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{47}" paraid="1967142564"><span style="font-size:14px;"><span style="font-family:segoe ui;">Differential Privacy </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="211" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{54}" paraid="998061524"><span style="font-size:14px;"><span style="font-family:segoe ui;">Model Interpretability </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="3" data-font="Symbol" data-leveltext="" data-listid="211" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{61}" paraid="1300680811"><span style="font-size:14px;"><span style="font-family:segoe ui;">Fairness </span></span></div>
	</li>
</ul>

<hr />
<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Module 10:  Monitoring Models </strong></span></span></div>

<div><span style="font-size:14px;"><span style="font-family:segoe ui;">After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. </span></span></div>

<ul role="list">
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="1" data-font="Symbol" data-leveltext="" data-listid="212" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{94}" paraid="1931761339"><span style="font-size:14px;"><span style="font-family:segoe ui;">Monitoring Models with Application Insights </span></span></div>
	</li>
	<li aria-setsize="-1" data-aria-level="1" data-aria-posinset="2" data-font="Symbol" data-leveltext="" data-listid="212" role="listitem">
	<div paraeid="{1934a420-4fd1-44f4-9184-71b72526d6fa}{101}" paraid="1411393162"><span style="font-size:14px;"><span style="font-family:segoe ui;">Monitoring Data Drift </span></span></div>
	</li>
</ul>

Available Exams and Certifications

<p paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{160}" paraid="413941652"><span style="font-size:14px;"><span style="font-family:segoe ui;">On completion the learner will earn:   </span></span></p>

<ul>
	<li paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{176}" paraid="725911956">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong>Certificate of Completion</strong> from <strong>Microsoft </strong></span></span></div>
	</li>
	<li paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{187}" paraid="253507786">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;"><strong><a href="https://docs.microsoft.com/en-us/learn/certifications/exams/dp-100" rel="noreferrer noopener" target="_blank">Microsoft Certified: Designing and Implementing a Data Science Solution on Azure</a></strong> upon successful pass of the certification exam. </span></span><span style="font-size:14px;"><span style="font-family:segoe ui;"></span></span></div>
	</li>
</ul>

<p paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{207}" paraid="1283062853"><em><span style="font-size:14px;"><span style="font-family:segoe ui;">Note: All certificates are electronically issued.  </span></span></em></p>

Articulation Options

Outcomes

<p paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{233}" paraid="506398719"><span style="font-size:14px;"><span style="font-family:segoe ui;">Upon completion of this course, learners will have acquired these skills </span></span></p>

<ul>
	<li paraeid="{6c6ae948-fb09-479e-aca9-3d474d824dfe}{250}" paraid="2001917732">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Provision an Azure Machine Learning workspace </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{2}" paraid="1075087993">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use tools and code to work with Azure Machine Learning </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{9}" paraid="1521760878">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use automated machine learning to train a machine learning model </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{16}" paraid="1328705048">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use Azure Machine Learning designer to train a model </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{23}" paraid="786114025">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Run code-based experiments in an Azure Machine Learning workspace </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{30}" paraid="675945148">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Train and register machine learning models </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{37}" paraid="614346737">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Create and use datastores </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{44}" paraid="1671557641">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Create and use datasets </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{51}" paraid="1852670975">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Create and use environments </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{58}" paraid="2054017736">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Create and use compute targets </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{65}" paraid="7320794">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Create pipelines to automate machine learning workflows </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{72}" paraid="1484370940">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Publish and run pipeline services </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{79}" paraid="477470181">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Publish a model as a real-time inference service </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{86}" paraid="1731669455">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Publish a model as a batch inference service </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{93}" paraid="1256543056">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Describe techniques to implement continuous integration and delivery </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{100}" paraid="4368988">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Optimize hyperparameters for model training </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{107}" paraid="411978257">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use automated machine learning to find the optimal model for your data </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{114}" paraid="1461980248">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Apply differential provacy to data analysis </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{121}" paraid="486174298">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use explainers to interpret machine learning models </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{128}" paraid="1315453217">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Evaluate models for fairness </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{135}" paraid="1775749422">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Use Application Insights to monitor a published model </span></span></div>
	</li>
	<li paraeid="{b9a7ab0f-c7fe-4d02-bd92-a67fa4394fb5}{142}" paraid="374200188">
	<div><span style="font-size:14px;"><span style="font-family:segoe ui;">Monitor data drift </span></span></div>
	</li>
</ul>

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