The Evaluator
Your go-to blog for insights on AI observability and evaluation.
Showing 401–410 of 458 posts (page 41 of 46)
Best Practices for ML Monitoring and Observability of Demand Forecasting Models
Learn more about how Arize helps clients observe demand forecasting models, dive into an interactive demo or request a trial of Arize. Demand forecasting is the time-tested discipline of using historical data, traditionally on purchases, to forecast customer demand over a given time period. Critical to operations and pricing strategy, nearly every category of business…
The Rise of the ML Engineer: Ilya Reznik, Twitter Cortex
Ilya Reznik has seen a lot in his career. With a background in physics and a stint at the Occupational Health and Safety Administration (OSHA), Reznik brings an interesting perspective to industry changes in the wake of the COVID-19 pandemic. “The world can change in an instant, and models are not static,” he reminds us….
Feast and Arize Supercharge Feature Management and Model Monitoring for MLOps
Arize AI and Feast partner to enhance the ML model lifecycle. Empower online/offline feature transformation and serving through Feast’s feature store and detect and resolve data inconsistencies through Arize’s ML observability platform. Check out our example Feast/Arize integration tutorial for an interactive demo! Arize and Feast are aimed at different parts of the machine learning…
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Five Takeaways From CDAO (Fall) On AI ROI
The last 18 months have introduced unforeseen challenges and opportunities for data science and machine learning organizations. Due to the global impacts of the COVID-19 pandemic, data science and ML engineering teams have been forced to navigate uncertainty, evolve their infrastructure investments and prioritize ML monitoring and model observability in an increasingly unpredictable world. In…
Continuous Monitoring, Continuous Improvements for ML Models Using Neptune AI and Arize AI
Delivering the best machine learning model to production should be as easy as training, testing, and deploying — right? Not quite! Models are far from perfect as they move from research to production, and maintaining model performance once in production is even more challenging. Once out of the offline research environment, the data a model…
Best Practices In ML Observability for Monitoring, Mitigating and Preventing Fraud
Every year, fraud costs the global economy over $5 trillion. In addition to taking a deeply personal toll on individual victims, fraud impacts businesses in the form of lost revenue and productivity as well as damaged reputation and customer relationships. AI practitioners are on the front lines of this battle, building and deploying sophisticated ML…
Rise of the ML Engineer: Chick-fil-A’s Korri Jones
Korri Jones has a call to action for senior leaders: take Data Science and Machine Learning seriously and build the right teams and systems to adapt to unforeseen and unexpected business and market changes. “Drive by any Chick-fil-A across the country during business hours and you’ll almost always see a fast-moving line. Maintaining that scale…
Introducing Amber Roberts, Arize’s Newest ML Sales Engineer
Shortly after Arize’s co-founders began talking to prospects and the company’s first customers, they also met Amber Roberts. An astrophysicist by training and a self-taught data scientist and ML engineer, Amber exuded a passion for connecting her work to a larger purpose and we could see right away that she shared our view that machine…
Two Essentials for ML Service-Level Performance Monitoring
Over the last decade, a wave of renewed interest in machine learning has encouraged countless researchers to attempt to solve problems with state of the art machine learning techniques. It feels like every month some paper is published with a novel use of machine learning to solve a task that was previously impossible; however, outside…
Arize AI Raises $19 Million Series A As Organizations Move To Address ML Observability, the Missing Foundational Piece of ML infrastructure
Today, almost every business is making massive investments in artificial intelligence to gain a competitive advantage. Yet, if you’re not Google, Facebook or Uber you most likely lack purpose-built, in-house systems to scale your MLOps and tools that can discover issues, diagnose problems, and improve the performance of your ML models. In response to the…
