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Soma Dhavala committed Sep 13, 2024
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<h3 class="anchored" data-anchor-id="what-to-collect">What to collect?</h3>
<p>We <a href="../lectures/w02-l02.html">discussed</a> ideation tools like <a href="https://web.stanford.edu/~mshanks/MichaelShanks/files/509554.pdf">Design Thinking</a>, <a href="https://online.hbs.edu/blog/post/what-is-human-centered-design">Human-centered Design</a> to engage with the problem (to define what to solve). In the <a href="../resources/project-card.html">Project Canvas</a>, we also discussed what is the ML Task, and what kind of data is needed to solve the business problem.</p>
<p>For example, suppose the problem is, say, assessing the quality of food grains. The business problem is to objectively assess the grain quality and decide a fair price linked to the quality. ML problem is to grade the grains, identify any foreign materia/contaminations, may be based on the morphology of the grains. What kind of data is needed here? We can treat this problem as a combination of segmentation, object detection, object classification, morphology analysis, and granulometry. Data must support these tasks. See the blogs <a href="https://www.v7labs.com/blog/image-annotation-guide">Image Annotation Definition, Usecases and Types</a>, <a href="https://labelbox.com/guides/image-annotation/">Best practices for successful image annotation</a>, <a href="https://opencv.org/blog/data-annotation/">Data Annotation - A beginner’s guide</a> to delve into annotating images further.</p>
<p>Arguably, annotating images for segmentation at grain level is very time consuming but can potentially be very useful. Treating this problem as a grading problem (accept/ reject) as opposed to provide quality assessment at grain and sample level is relatively easy in terms of annotations. But with <em>accept/reject the lot</em> binary labels at image level one can not pivot to a different ML problem formulation if the solution is proved not useful. This is an inherent design challenge. Getting as many details as possible is desirable but many not be practical. But thankfully, with foundation models like <a href="https://segment-anything.com/">SAM</a>, some of this leg work can be automated. See this <a href="https://arxiv.org/abs/2105.04678">paper</a> on sample selection for efficient image annotation. Most modern annotation studios like <a href="https://www.labellerr.com/">Labeller</a> offer some support to speed-up the process.</p>
<p>Arguably, annotating images for segmentation at grain level is very time consuming but can potentially be very useful. Treating this problem as a grading problem (accept/ reject) as opposed to provide quality assessment at grain and sample level is relatively easy in terms of annotations. But with <em>accept/reject the lot</em> binary labels at image level one can not pivot to a different ML problem formulation if the solution is proved not useful. This is an inherent design challenge. Getting as many details as possible is desirable but many not be practical. But thankfully, with foundation models like <a href="https://segment-anything.com/">SAM</a>, some of this leg work can be automated. See this <a href="https://arxiv.org/abs/2105.04678">paper</a> on sample selection for efficient image annotation. Most modern annotation studios like <a href="https://www.labellerr.com/">Labellerr</a> offer some support to speed-up the process.</p>
<p>While the above labels support the primary ML task, say the segmentation task, for example, they may not be sufficient to offer SLAs or Model monitoring (battling the unknown unknowns) that we discussed in <a href="../lectures/w05-l01.html">Model Monitoring</a>. Suppose, for the same problem, the model is deployed in two grain collection sites. Training data was collected from the the first site (for rice grains), and model is being is used in the second site. It so happens that the second site is using the model for “wheat” instead of “rice.” Obviously, model will perform poorly. This may not have been anticipated ahead of time. Only way to detect this is to first log the data where the model is being used, and observe the performance <em>by site</em>. That means, <em>where</em> the model is being in this case and any other contextual data is necessary to monitor, diagnose, and improve models over time. This is all but one of the dimensions to think about. Generally speaking, thinking about dimensions is the topic of <em>knowledge representation</em> and <em>knowledge engineering</em>. Dimensional Modeling](https://www.ibm.com/docs/en/ida/9.1.2?topic=modeling-dimensional) is a framework to adopt to collect data that can address the concerns of different stakeholders besides the ML and Data Scientists such as a Software Engineer, QA, Architect, Product Manager, Site Manager etc. IBM’s book on <a href="https://www.redbooks.ibm.com/redbooks/pdfs/sg247138.pdf">Dimensional Modeling: In a Business Intelligence Environment</a> is a definitive guide on this topic. <a href="https://arxiv.org/abs/2305.17196">A knowledge Engineering Primer</a> is perhaps a lighter compared to the book.</p>
<p>Now let us move to the other question. <strong>How many?</strong></p>
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