Week #7: Developing my website

Yayyy, I’m so happy to finally announce that I have started developing the dashboard for my machine learning program! This dashboard will be especially beneficial for readers who are unfamiliar with SSDs and data retention (I hope 🤞). In this blog, I’ll be going over the planned layout of my dashboard and how I will create it. This week, I have also been working further on gathering data and analyzing them, but there are no current updates. 

As you all know, I am developing a machine learning algorithm that predicts the threshold voltage of a cell. This prediction relies on the cell’s location and age data. Because this algorithm is hard to display to a large audience range, I decided to create a visual dashboard that allows users to simulate this prediction algorithm. I created this dashboard with Streamlit, which enables programmers to create dynamic data apps (2025, Streamlit).

Check out this article I referenced to introduce Streamlit: https://docs.streamlit.io

Now, let’s move onto the progress I’ve made with my dashboard thus far. In the last couple of blogs, I showed you guys a lot of the clusters that I’ve created with machine learning. My goal was to show the dashboard user how machine learning groups thousands of lines of data and how those clusters change over time. So, I created a new csv file that added the clusters as the last column. 

The number in the fifth column describes which cluster that data point is at. 

After creating the cluster column for all my data files, I imported them into my new dashboard program. I created a dropdown menu that asks the user to choose the day that they would like to display. Viewing different days allows the user to see how the clusters and threshold voltages change over time. After choosing, the program will display the cluster graph that I showed in previous blogs. 

Neat, right? This is all that I have on my dashboard currently, but I’m so excited to add more features to this dashboard! 

Other potential additions to this dashboard include allowing users to input their own die number, wordline, and day variables. Then, the machine learning algorithm will take in the user’s given features and output a predicted threshold voltage. 

That’s all I have for this week! I hope you enjoyed reading my blog! Next week, I’ll continue building onto this dashboard and I’ll have further updates for you on my gathered data and machine learning algorithm. See you next time!