September 2021 Summaries

The AI industry is growing rapidly, with businesses investing heavily in artificial intelligence to gain a competitive advantage. However, most organizations lack purpose-built systems to scale their MLOps and tools for model performance, leading to the need for observability solutions. Arize AI has raised $19 million in Series A funding from Battery Ventures, which will help strengthen its commitment to providing ML practitioners with deeper understanding of model performance across all stages of the model development cycle. The company's vision is to overcome current ethical deficits in AI systems by developing tools that monitor, troubleshoot, explain, and provide guardrails on AI, ultimately benefiting businesses and society. Arize's toolset allows teams to observe, manage, and improve machine learning models through a single pane of glass, connecting points across training, validation, and production, and providing automated monitoring of key model performance metrics.

Arize AI has been listed as a Representative Explainability Vendor in Gartner's 2021 Market Guide for AI Trust, Risk and Security Management (AI TRiSM). The company's Machine Learning Observability platform helps teams take models from research to production with ease. Arize's automated model monitoring and analytics platform is used by top enterprises to detect issues, troubleshoot problems, and improve overall model performance. Gartner views AI TriSM as a set of tools that ensure AI model governance, trustworthiness, fairness, reliability, efficacy, security, and data protection. Arize's recognition highlights the growing need for solutions that provide transparency and introspection into historically black box systems to ensure more effective and responsible AI deployment.

The text discusses the importance of model explainability in machine learning (ML) as models grow increasingly complex and impactful on various aspects of life. It highlights the need for understanding why a model makes certain predictions, especially when they power significant experiences.

The transparency problem in AI is a significant issue, with 51% of business executives reporting its importance and 41% suspending deployment due to potential ethical issues. Technical complexities contribute to black box AI, as the sheer volume of data fed into ML models makes their inner workings less comprehensible. Misconceptions regarding transparency include losing customer trust, believing self-regulation is sufficient, thinking that not using protected class data eliminates bias, and fearing disclosure of intellectual property. However, adopting responsible AI practices helps establish trust with customers, enabling predictable and consistent regulation, allowing access to protected class data for mitigating biases, and ensuring transparency doesn't mean disclosing intellectual property. ML observability tools can help organizations build more transparent AI systems by transforming black box models into glass box models that are more comprehensible to human beings.

Lyft relies on Machine Learning (ML) Engineers to bridge the gap between data scientists who develop models and teams that operationalize them. The company's ML infrastructure is used for various solutions, including mapping, fraud detection, pricing optimization, and ETA estimates. Alex Zamoshchin, an engineering manager at Lyft, explains how ML engineers help get models from research into the real world while ensuring they achieve business objectives. They are involved in framing ML problems within the business context, converting models into working pipelines, and analyzing experimental and observational data to ensure model quality and performance once deployed. In a hypothetical world without ML engineers, issues with models could arise before or after implementation, leading to potential failure in production environments.