InceptionWebDesign/main.html at master · jordanjones243/InceptionWebDesign · GitHub

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<!DOCTYPE html>
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    <title>About</title>
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    <h1>Greener AI</h1>
    <h2 class="main-title">Image Classification</h2>
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  </header>
    <div class="w-20 p-2" style="background-color:green;"></div>
    <h2>What is the Microsoft GreenerAI Project?</h2>
    <p> Microsoft has become a corporate leader in environmental sustainability by constantly striving to minimize the environmental footprint of its business operations while maximizing the positive environmental impacts of its products, policies and partnerships. As the environmental challenges facing the world grow, so too has Microsoft’s commitment to ensuring that the company’s activities are in-line with the best available science and the expectations of leading stakeholder communities. </p>
    <p>The Azure Machine Learning team is launching a project related to GreenAI . This project will establish a cost/carbon accounting capability within AzureML, and help inform a standard model for cost and carbon telemetry across Azure. As part of a cross-functional team, you’ll need to create & wrangle diverse data into production pipelines. You’ll use this to build telemetry, dashboards, and communicate your results across business groups. While you’ll be embedded within the Azure Machine Learning team, you’ll also be paired with mentors within the broader Microsoft sustainability community (Azure/MSR/Windows). You’ll also likely be contributing to open-source carbon measurement tooling on GitHub. </p>

    <h2>Problem Statement</h2>
    <p> <strong>Sense of urgency:</strong> As a corporate leader in environmental sustainability, Microsoft is
      seeking new ways of decreasing their carbon footprint through their commitment to environmental
      sustainability, and must seek more opportunities to make their tools more efficient. Along with
      this, they must continue to seek improvements around ML practices such as training datasets so
      their customers can better utilize Azure services. </p>

    <p>Currently, the way in which many tech companies analyze data is unsustainable when compared to
        the rate at which they are collecting the data. Data collection has rapidly increased over the
        years however, the processes that are used to analyze this data on are not advancing at a high
        enough rate to catch up. We find this to be a major drawback for Microsoft's sustainability
        goals as data collection and analysis require high computational hours. To reduce the overall
        ost of operations, the current Microsoft AzureML model needs to be optimized to accurately map
        key efficiencies, power metrics, cost, carbon and accuracy of the model. Our motivation is to
        research and develop a methodology that best fits the Image Classification vertical,
        specifically using the InceptionV3 model as a baseline to create GitHub samples that showcase
        our optimized approach. </p>

    <p>
    Goals of case study: From a general scope, Microsoft wants to have resources that show their new
    AzureML technologies are more cost-effective and use less resources so customers can make more
    informed decisions. Our group specifically is focusing on how we can provide this ability through
    image classification capabilities of AzureML for our stakeholders:
        <ul>
            <li>Create a starting point for customers</li>
            <li>Microsoft wants to integrate cost/power/efficiency metrics into their AzureML software
                to help customers choose the correct tools for what they need.</li>
            <li>Faster training = quicker experimental models can lead to faster turnaround times as
                well as being cheaper. </li>
        </ul>
    </p>
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