Introducing SHAPshot: Now Featured on Product Hunt!

At SOTAI, we understand the pivotal role that clear, interpretable model insights play in the successful application and communication of machine learning. Whether you are a seasoned data scientist, an aspiring ML engineer, or a project stakeholder, our platform is designed to make explaining and understanding machine learning models seamless, enlightening, and enjoyable.

We’re thrilled to announce that we are launching on Product Hunt! It’s an exciting moment for us, and we are eager to share our enthusiasm and passion for machine learning explainability with a broader community. Our product allows users to upload their SHAP package outputs effortlessly, enabling easy analysis and sharing of visualizations and explanations.

Our new offering empowers users and their team to glean actionable insights more easily, communicate more effectively, and ultimately drive more impactful machine learning applications.

The Problem We're Solving

SHAP (SHapley Additive exPlanations) has emerged as a pivotal tool for explaining machine learning model decisions, enabling practitioners to compute feature contributions to provide clearer insights and foster better communication around model predictions. However, utilizing SHAP effectively presents its own set of challenges.

The crux of the problem lies in extracting insights from various SHAP charts efficiently and communicating them effectively to teammates without a technical background.

For instance, imagine a data scientist who has just run SHAP on their model and wants to share their findings with project stakeholders. The stakeholders, eager for insights, are met with a barrage of charts and, often, technical language, making it difficult to pinpoint the actionable insights amidst the technical details. This complexity not only hampers understanding but also impedes the collaboration essential for the successful launch of machine learning models.

Moreover, the cumbersome process of sharing SHAP results often means spending valuable time focused on what language to use or on creating presentations or documents—time that could have been better spent on refining models or exploring new ideas. The need for a solution that streamlines the analysis and sharing of SHAP values is evident. We are hoping to alleviate pain points associated with interpreting and sharing SHAP results in a way that makes sense to non-technical stakeholders.

The Solution: SHAPshot

Our SHAPshot platform enables you to upload your SHAP results with ease to analyze models both globally and at the individual example level with the charts you already know and love.

What sets SOTAI's SHAPshot apart is its emphasis on seamless analysis, sharing, and collaboration. It is simple to annotate charts with descriptions, which you can generate automatically using AI. This makes the insights gathered from SHAP results accessible to both technical and non-technical team members. This feature ensures that the richness of SHAP insights is not lost in translation, allowing teams to converge on actionable insights rapidly to make effective data-driven decisions.

With just a click of a button, you can share annotated results through a public link, eliminating the need for lengthy presentations, documents, and meetings. This feature not only streamlines the sharing process but also makes collaboration more dynamic and real-time, allowing teams to iterate quickly and focus on what truly matters—leveraging insights to drive impactful applications of machine learning.

User-Friendly Upload

You can use our SDK to upload SHAP results to the SOTAI web client. This saves time on managing results so you can focus on analysis and interpretation.

sotai.external.shap(    inference_data=df,    shapley_values=shap_values.values,    base_values=shap_values.base_values,    name="My SHAP (Heart.csv)",    target="target",    dataset_name="heart.csv",)

Caption: The hub for your uploaded SHAP results for easy management and access.

Detailed Analysis with Annotated Charts

Delve deep into models by analyzing various SHAP charts. Annotate charts with descriptions to facilitate a deeper understanding of SHAP results across the whole team, making insights accessible to non-technical stakeholders.

Caption: Global Analysis of a model showing the description for the feature importance chart.

AI-Generated Explanations

With just a click of a button, use AI to generate explanations for your SHAP charts that are easy to consume for non-technical stakeholders.

Caption: Force chart explaining the prediction of an individual example with an AI-generated description.

Easy Sharing with Interactive Visualizations

With just a few clicks, you can share your results, streamlining your communication of insights to stakeholders with real-time collaboration and discussions. Shared pages provide the various interactive visualizations for your SHAP results with your annotations to help engage your team.

Caption: Easily share your results with your team with a click of a button.

How To: Use SOTAI + SHAP (SHAPshot)

  • If you haven't heart of SHAP, check out our recent comprehensive guide.
  • For a full run through of how to use SHAPshot, explore our docs.

Product Hunt

We chose to launch SHAPshot on Product Hunt because of its vibrant community of innovators, creators, and early adopters who are as passionate about new and exciting technology as we are. It’s the ideal platform to introduce our solution, gain valuable feedback, and connect with like-minded individuals who understand the importance of making machine learning more accessible and interpretable.

We launched on Thursday October 12th at Midnight PT. We are excited to share SHAPshot with the Product Hunt community and are eager to hear your thoughts, feedback, and experiences with our product.

  1. Visit Our Product Hunt Page: On the day of the launch, visit our Product Hunt page to view our post.
  2. Interact: If you find SHAPshot valuable, please support us by interacting with our post.
  3. Comment: Share your thoughts, experiences, or ask questions in the comment section. We’ll be there to answer any queries and engage in discussions.
  4. Share: Spread the word by sharing our Product Hunt post with your network, colleagues, and anyone who might find SHAPshot beneficial.

Your support means the world to us! By interacting, commenting, and sharing, you help us in reaching a wider audience, fostering more discussions around SHAP and model explainability, and ultimately driving the adoption of fair and transparent machine learning models.

SHAPshot on Product Hunt

We are thrilled about this launch and look forward to engaging with you on Product Hunt! Whether you are a data scientist, an ML engineer, or someone interested in machine learning, we believe SHAPshot has something to offer, and we can’t wait to hear your insights!

Future Plans

Our journey doesn’t end with the launch of SHAPshot; in fact, it marks the beginning of a new chapter in our endeavor to make machine learning insights more accessible and actionable. We have a vision of continual evolution, aimed at meeting the growing needs of our users and addressing the dynamic challenges in the world of machine learning explainability.

Commitment to Enhancement

We are deeply committed to refining and enhancing SHAPshot, ensuring it remains valuable to its users. We are listening attentively to your feedback and are eager to implement changes that elevate your experience and address your needs more effectively.

Upcoming Features We're Excited About

  • Hosted SHAP: Run SHAP on your model on our cloud and have results automatically ready for analysis in the web client. No longer worry about kernel crashes or how to run SHAP.
  • Collaboration Through Commenting: We are working to allow users to comment directly on pages and charts, facilitating discussion and collaboration all in one place within the platform, making the process of explaining and understanding your model's decisions even more seamless and dynamic.

We envision SHAPshot as not just a tool but a companion in your machine learning journey, helping you uncover insights, communicate more effectively, and drive impactful applications of machine learning. Our upcoming features are steps toward creating an environment where insights are not just shared but are also collaboratively built upon, fostering a sense of community and collective growth.

We are excited about the road ahead and are dedicated to pushing the boundaries in the quest for more transparent, understandable, and fair machine learning models. Our future plans are shaped by our desire to empower you to make the most out of your SHAP analyses and to bring clarity and actionable insights to every member of your team.

A Tip of the Hat

Our journey in developing SHAPshot has been incredibly rewarding.

A heartfelt thank you goes out to our dedicated team whose relentless pursuit of excellence has shaped SHAPshot into a reflection of our collective vision and passion.

Our early supporters, your belief and invaluable feedback have been the bedrock of our development process, constantly inspiring us to refine and enhance our offering.

To our readers, future users, and the broader community, your interest and enthusiasm are our driving forces. We are deeply thankful for your support and eagerness to join us on this exciting journey. Every insight, experience, and perspective you share will play a crucial role in shaping the evolution of SHAPshot.

We look forward to exploring uncharted territories, overcoming challenges, and reaching new milestones together. Here’s to more clarity, understanding, and impactful applications of machine learning in our shared future!