Improving Machine Learning Observability at Etsy: Insights from Etsy Engineering
Introduction:
Etsy, the popular online marketplace, has recognized the importance of implementing ML observability to enhance its machine learning (ML) deployments. While the platform has comprehensive software observability, there is a need for centralized ML-specific observability. ML observability involves monitoring input features, predictions, and performance metrics in order to optimize ML models. Etsy aims to achieve comprehensive oversight by logging features, predictions, and ground truth labels. This data is processed, metrics are calculated, and visualizations are created to monitor and alert any issues. ML observability is challenging due to the massive amount of data and complex requirements. Etsy’s motivation behind implementing ML observability is to save costs, reduce production incidents, and continuously improve their models. By partnering with a third-party vendor, Etsy is able to integrate a scalable ML observability solution without disrupting their existing workflow. While there are still challenges in the process, Etsy looks forward to generating value and reducing risks for their ML models in the long term.
Full Article: Improving Machine Learning Observability at Etsy: Insights from Etsy Engineering
Etsy, the online marketplace, has announced the implementation of machine learning (ML)-specific observability to improve its ML deployments. While the company already has observability over its ML deployments from a software engineering perspective, it lacked a centralized implementation for ML-specific observability. This new implementation focuses on monitoring the distribution of input features, predictions, and performance metrics.
ML observability requires three main components: input features, a prediction, and a ground truth label. For example, in the search and recommendations space, Etsy would log user features and item features, the model’s prediction on whether the user would click on an item, and whether the user actually clicked on it. However, processing and analyzing this data, as well as effectively visualizing, monitoring, and alerting on it, is a complex task that requires active development and few best practices.
Etsy faced unique challenges in implementing ML observability due to the massive scale of data it processes (terabytes per day) and the complexity of requirements. The company aims to save on long-term costs by setting up the necessary infrastructure for future features like intelligent retraining. Lack of observability has caused production incidents in the past, leading to revenue loss, and manual debugging processes. Thus, the goal of building comprehensive model observability is to reduce the time to remediation of production incidents and improve model performance.
To establish ML observability, the Machine Learning Infrastructure, Platform and Systems (MIPS) team at Etsy engaged with ML customer teams across various areas. The solution needed to monitor performance, data quality, and drift issues, integrate easily with different teams’ metrics, and offer model explainability. The platform also had to handle Etsy’s massive data, ensure secure handling of personal identifying information (PII), and integrate with the company’s existing in-house tools.
While Etsy initially considered building an in-house solution, the complexity of ML observability and the potential disruption to the existing ML lifecycle made it a challenging task. Instead, the company decided to work with a third-party vendor to handle observability as a scalable software-as-a-service (SaaS) solution. Integrating prediction logs with the third-party tool was a non-trivial task, but it allowed Etsy to benefit from the tool’s features without disrupting its existing workflow.
Currently, Etsy is in the process of integrating and leveraging the features of the ML observability tool. It involves batch processes to generate data for loading and does not yet offer real-time monitoring. However, the company plans to set up monitoring and alerting for its 80+ models in production.
In conclusion, Etsy’s implementation of ML observability aims to improve the performance, reliability, and cost-effectiveness of its ML deployments. By monitoring input features, predictions, and performance metrics, the company can identify and troubleshoot issues, reduce downtime, and continuously improve its models.
Summary: Improving Machine Learning Observability at Etsy: Insights from Etsy Engineering
Etsy, a popular e-commerce platform, has recognized the importance of implementing observability in its machine learning (ML) deployments. While the platform has comprehensive observability from a software engineering standpoint, there has been a lack of ML-specific observability. This new field focuses on monitoring features, predictions, and performance metrics to ensure the accuracy and efficiency of ML models. Etsy aims to improve long-term cost-effectiveness, minimize production incidents, and continuously enhance its models through ML observability. After careful consideration, Etsy decided to partner with a third-party vendor to integrate their ML observability tool seamlessly into their existing ML lifecycle. The process of integrating data and adopting real-time monitoring is still ongoing, but Etsy is already reaping the benefits and mitigating risks in its ML deployments.
Frequently Asked Questions:
1. What is machine learning and how does it work?
Machine learning is a branch of artificial intelligence that focuses on computer systems learning from data without being explicitly programmed. It involves algorithms and statistical models that enable computers to make accurate predictions or decisions based on patterns and trends in the data. By analyzing vast amounts of data, machine learning algorithms learn and improve over time, enabling them to generalize and adapt to new situations.
2. What are the different types of machine learning?
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning involves training a model with labeled data, where the desired output is known. The algorithm learns patterns and relationships to make predictions on new data. In unsupervised learning, the data is unlabeled, so the algorithm identifies hidden patterns and structures within the data. Reinforcement learning is a trial-and-error approach, where an agent learns from interactions with an environment and receives feedback to maximize rewards and improve decision-making.
3. What are some real-life applications of machine learning?
Machine learning has various applications across industries. In healthcare, it can be used to diagnose diseases and predict patient outcomes. In finance, it helps identify fraudulent transactions and forecast market trends. In e-commerce, it powers recommendation systems to personalize product suggestions. It also finds applications in autonomous vehicles, natural language processing, image recognition, and cybersecurity, among others.
4. What are the main challenges in machine learning?
One of the challenges in machine learning is the availability of high-quality and labeled data, which is often a requirement for supervised learning. The selection and preprocessing of data can also impact the performance of the models. Overfitting and underfitting of models pose challenges, as they can lead to poor generalization. Another challenge is the interpretability of complex machine learning models, which can be difficult to understand and explain. Additionally, staying updated with the constantly evolving algorithms and techniques in the field can be a challenge.
5. What are the future prospects of machine learning?
Machine learning is constantly evolving and is poised to have a significant impact on various industries in the future. With advancements in deep learning, natural language processing, and reinforcement learning, we can anticipate improved accuracy and performance of models. The integration of machine learning with Internet of Things (IoT) devices will enable smart homes and cities, while healthcare will benefit from personalized treatment plans. Machine learning will also contribute to advancements in areas like robotics, virtual reality, and data analysis, driving innovation and efficiency in many sectors.
