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The content of this repository is available for you so you can reproduce any demo or learn how to present any session of the Learning Path presented at Microsoft Ignite and during Microsoft Ignite The Tour, in your local field office, a community user group, or even as a lunch-and-learn event for your company.
If you are here to reproduce a demo in the comfort of your home/office, go in in the section Sessions. In each session you will find deployment instructions, to create the environment you need, and a tutorial to do the demo step by step.
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Artificial Intelligence (AI) is driving innovative solutions across all industries but with machine learning (ML) applying a paradigm change to how we approach building products we are all exploring how to expand our skill-sets
Tailwind Traders is a retail company looking for support on how to benefit from applying AI across their business. In 'Developers Guide to AI’ we’ll show how Tailwind Traders has achieved this
There is something for every stage of the AI learning curve; whether you want to consume ML technologies, increase technical knowledge of ML theory, or build your own custom ML models. The model is not the end of the data science story, we will conclude with applying DevOps practices to ML projects to build an end-to-end pipeline
- There are x5 45 minute sessions (AIML10, AIML20, AIML30, AIML40 and AIML50)
- And x1 20 min theatre session (AIML21)
Here all the sessions available in the learning path Developers Guide to AI (aka: AIML)
Tailwind Traders has a lot of legacy data that they’d like their developers to leverage in their apps – from various sources, both structured and unstructured, and including images, forms, pdf files, and several others. In this session, you'll learn how the team used Cognitive Search to make sense of this data in a short amount of time and with amazing success. We'll discuss tons of AI concepts, like the ingest-enrich-explore pattern, skillsets, cognitive skills, natural language processing, computer vision, and beyond.
As a data-driven company, Tailwind Traders understands the importance of using Artificial Intelligence to improve business processes and delight customers. Before investing in an AI team, their existing developers were able to demonstrate some quick wins using pre-built AI technologies. In this session, we will show how you can use Azure Cognitive Services to extract insights from retail data and go into the neural networks behind computer vision. You’ll learn how it works and how to augment the pre-built AI with your own images for custom image recognition applications.
Tailwind Traders uses custom machine learning models to fix their inventory issues – without changing their Software Development Life Cycle! How? Azure Machine Learning Visual Interface. In this session, you’ll learn the data science process that Tailwind Traders’ uses and get an introduction to Azure Machine Learning Visual Interface. You’ll see how to find, import, and prepare data, select a machine learning algorithm, train and test the model, and deploy a complete model to an API. Get the tips, best practices, and resources you and your development team need to continue your machine learning journey, build your first model, and more.
Tailwind Traders’ data science team uses natural language processing (NLP), and recently discovered how to fine-tune and build a baseline models with Automated ML.
In this session, you’ll learn what Automated ML is and why it’s so powerful, then dive into how to improve upon baseline models, using examples from the NLP best practices repository. We’ll highlight Azure Machine Learning key features and how you can apply them to your organization, including low priority compute instances, distributed training with auto scale, hyperparameter optimization, collaboration, logging, and deployment.
While many companies have adopted DevOps practices to improve their software delivery, these same techniques are rarely applied to machine learning projects. Collaboration between developers and data scientists can be limited and deploying models to production in a consistent and trustworthy way is often a pipedream.
In this session, you’ll learn how to apply DevOps practices to your machine learning projects using Azure DevOps and Azure Machine Learning Service. We’ll set up automated training, scoring, and storage of versioned models and wrap the models in docker containers and deploy them to Azure Container Instances and Azure Kubernetes Service. We’ll even collect continuous feedback on model behavior so we know when to retrain.
In this theatre session we will show the data science process and how to apply it. From exploration of datasets to deployment of services - all applied to an interesting data story. This will also take you on a very brief tour of the Azure AI Platform.
To know more about about to contribute to this project please refer to the Code of Conduct and Contributing page.
You don't need anything to present this content, it's all there to be used. However, by becoming a Trained Presenter the scalable content team will recognize you as well. Trained Presenter see their contact information (name, picture, website) in the bottom of each session.
To become a Trained Presenter, contact [email protected]. In your email please include:
- Complete name:
- The code of this presentation: <Session Code (ex:APPS10)>
- Link to an unlisted YouTube video of you presenting around 10 minutes of the content for this specific session.
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