April 2022 Summaries

Arize AI has launched Bias Tracing, a tool designed to help enterprises identify and address algorithmic bias within machine learning models. The solution enables multidimensional comparisons, allowing teams to quickly uncover the features and cohorts contributing to potential biases without time-consuming SQL querying or troubleshooting workflows. Arize Bias Tracing helps data science and machine learning teams monitor and take action on model fairness metrics, ensuring that models do not perpetuate discrimination against marginalized groups. The tool is unique in its ability to provide multidimensional comparisons by default, enabling users to identify the feature-value combinations where parity across sensitive and base groups is most negatively impacting a model's overall fairness metrics.

In this article, Tsion Behailu shares her experience as a Founding Engineer at Arize after leaving Google. She discusses her journey from exhaustion during her undergraduate years to finding stability and growth at Google. However, she felt the need for a new experience and began soul-searching about what she truly wanted in her career and life. She considered various opportunities such as building an engineering stack from scratch, specializing in a specific technological area, or starting her own startup. Ultimately, she joined Arize after evaluating its potential for growth and the opportunity to work with a trusted colleague. Two years later, she is grateful for the decision that has made her a better engineer and leader.

Thomas Huang, a software engineer at LinkedIn, joined the company to work on machine learning (ML) infrastructure, which he believes is a crucial aspect of ML engineering teams' operations. He chose this role after realizing that his previous experience as a machine learning scientist was more aligned with data engineering and software engineering than actual machine learning work. Huang's new role involves working on LinkedIn's feature store, Feathr, which was recently open-sourced. The feature store allows for offline, online, and nearline operations, making it a comprehensive project. At LinkedIn, the company is using Feathr to improve its machine learning models, including those used in detecting abuse, ads, and people you may know features. Huang views the ML engineering role evolving over time, with the role blurring between data science, software engineering, and research. He believes that startups use the machine learning engineer title loosely and that roles can be outside of specific domains. In his previous role at Alectio, Huang worked on active learning as a service, which he found to be challenging due to its reliance on flawed premises. He advises students or others hoping to get into an ML engineering or ML platform type role to be patient, take alternative positions, and stay intellectually stimulated during the job search process.

The industry's largest event on machine learning observability, Arize:Observe, recently took place, featuring multiple tracks and talks from prominent companies such as Etsy, Kaggle, Opendoor, Spotify, and Uber, among others. The event highlighted key takeaways including the announcement of Arize's ML observability platform now available on a self-serve basis, including a free version. Several speakers emphasized the importance of scaling an ML practice by focusing on customer problems rather than just building a platform for its own sake. Machine learning infrastructure is complex and requires consideration beyond algorithms, with dependences on data layers and surrounding systems. Diversity is crucial in ML teams to improve accuracy, development, and retention, while AI ethics needs to be woven into the fabric of organizations from top to bottom. The industry is maturing, with tooling like Arize helping deploy models with confidence. The future holds promise for multimodal machine learning, including aligning different UMAP models and exploring new techniques such as Grumov Wasserstein distance. Overall, it's an exciting time for the AI industry, with global investment expected to reach $200 billion by 2023, and ML teams needing to invest in best practices and foundational investments in ML platforms to navigate the challenges of model issues impacting business results.