Company: Aporia
Company Description: Aporia is a full-stack ML observability platform that enables data science and ML teams to monitor, explain, and improve their ML models. Used by Fortune 500 companies and data science teams in every industry across the world, Aporia empowers businesses to trust their machine learning and ensure responsible AI and fairness.
Nomination Category: Company / Organization Categories
Nomination Sub Category: Most Innovative Tech Company of the Year - Up to 100 Employees
 Nomination Title: Aporia
- Which will you submit for your nomination in this category, a video of up to five (5) minutes in length about the achievements of the nominated organization since 1 January 2020, OR written answers to the questions for this category? (Choose one):
Written answers to the questions
- If you are submitting a video of up to five (5) minutes in length, provide the URL of the nominated video here, OR attach it to your entry via the "Add Attachments, Videos, or Links to This Entry" link above, through which you may also upload a copy of your video.
- If you are providing written answers for your submission, you must provide an answer to this first question: Briefly describe the nominated organization: its history and past performance (up to 200 words):
Total 150 words used.
Aporia is a Tel Aviv-based company founded in 2019 by Liran Hason (CEO) and Alon Gubkin (CTO, Forbes 30 under 30 Europe 2022). It is a full-stack observability platform for machine learning that enables data science and ML teams to monitor, explain and improve their ML models.
Used by Fortune 500 companies and data science teams in every industry around the world, Aporia empowers businesses to trust their machine learning and ensure responsible AI and fairness. With a total of $30M in funding raised, Aporia is backed by Tiger Global, Samsung Next, TLV Partners, and Vertex Ventures.
The platform is self-serve and self-hosted, providing visibility, proactive monitoring and automation, advanced investigation tools, and explainability. With customization at its core, Aporia is easily tailored to fit any ML use case and allows data scientists to have full control over detecting issues including data drift, bias, data integrity issues, and performance degradation.
- If you are providing written answers for your submission, you must provide an answer to this second question: Outline the organization's achievements since the beginning of 2020 that you wish to bring to the judges' attention (up to 250 words):
Total 133 words used.
Aporia has experienced 600% growth in customers over the past 6 months, including Fortune 500 and well-known companies like Lemonade and Armis. Aporia launched a free self-serve community Edition of our ML Monitoring Solution. In Feb 2022, Aporia raised a $25M Series A funding round. Alon Gubkin (CTO of Aporia) is in Forbes 30 under 30 Europe 2022.
Aporia is currently expanding its US sales team. In April 2022, the former IBM sales leader Tim Tyrrell joined the company as Vice President of Sales. Aporia was recognized as a ModelOps Vendor in the 2021 Gartner Market Guide for AI Trust, Risk, and Security Management. Aporia was selected as an AWS AI Challenge Master in 2021Aporia was recognized in CRN’s 10 Cool Cloud AI And ML Services You Need To Know About in 2021.
- If you are providing written answers for your submission, you must provide an answer to this third question: Explain why the achievements you have highlighted are unique or significant. If possible compare the achievements to the performance of other players in your industry and/or to the organization's past performance (up to 250 words):
Total 172 words used.
AI fails on a regular basis — models predict results that can be distorted due to unexpected changes in the format of input data, model performance degradation over time, and more. With the reliance upon AI becoming more common, irresponsible use can have significant consequences like discrimination due to bias, or huge financial losses. Despite the complex nature of ML monitoring platforms, Aporia’s solution is self-serve, self-hosted and extremely easy to use compared to all other ML monitoring and observability solutions. Data scientists and ML engineers can easily get started within just a few minutes and create customized monitoring to match their specific models and use cases, while most other solutions require a long onboarding and support structure. The customizable nature of Aporia's monitoring means ML monitoring can be tailor-made uniquely to each customer's AI needs within minutes, whilst integrating seamlessly with existing ML infrastructure. This fusion of off-the-shelf adaptability and functionality has led hundreds of businesses, across all industries, such as Lemonade and Armis, to trust Aporia with their model observability.
- You have the option to answer this final question: Reference any attachments of supporting materials throughout this nomination and how they provide evidence of the claims you have made in this nomination (up to 250 words):
Total 206 words used.
- Link to Aporia’s website: https://www.aporia.com/
- Link to Aporia’s self-serve product: https://app.aporia.com/universal-login?mode=signup
- Link to Aporia’s documentation that shows the simplicity of the use: https://docs.aporia.com/
- How to Monitor Your ML Model with Aporia – demo video: https://www.youtube.com/watch?v=9oyZDXrmWMA
- The news about the funding on VentureBeat
- Aporia recognized as an ModelOps Vendor by Gartner in their Market Guide on AI Trust, Risk and Security Management
- Aporia's Introduction of a Self-Serve, Free Community Edition of our ML Monitoring Solution
- Aporia’s CTO workshop on How to Build an ML Platform from Scratch (the equivalent of a whitepaper but more hands-on and useful)
Open Source Tools: One of Aporia’s goals is to make sense of the MLOps space and to share tools that help the community build, manage and scale their AI. These are open-source tools that Aporia has created for the community:
- MLOps.toys - an open-source curated list of useful MLOps tools, projects and more.
- MLNotify - an open-source tool released this year that notifies data scientists when their model training finishes
- Train Invaders - a Space Invaders type game that data scientists can play in their Jupyter Notebook during training, which also notifies when training finishes
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