Category Archives: Uncategorized

Gradient Flow #36: Model Monitoring, Hydrofoils, Data Portability

Subscribe • Previous Issues This edition has 428 words which will take you about 2 minutes to read. “Preferences are optional and subject to constraints, whereas constraints are neither optional nor subject to preferences.” – Marko Papic Data Exchange podcast Making Boats Fly with Reinforcement Learning and Ray   Nic Hohn (Chief Data Scientist, McKinsey/QuantumBlack Australia) describesContinue reading “Gradient Flow #36: Model Monitoring, Hydrofoils, Data Portability”

Model Monitoring Enables Robust Machine Learning Applications

Key features of ML monitoring solutions, why companies need a holistic MLOps platform that includes model monitoring, and challenges companies face in making that happen. By Ben Lorica and Paco Nathan. According to the 2020 Gartner Hype Cycle for Artificial Intelligence, machine learning (ML) is entering the Trough of Disillusionment phase. This is the phaseContinue reading “Model Monitoring Enables Robust Machine Learning Applications”

Gradient Flow #35: Optimizing Inference, Workflow Tools, RL in Large Enterprises

Subscribe • Previous Issues This edition has 510 words which will take you about 3 minutes to read. “The thing about machine learning scientists is that they never admit defeat because all of their problems can be solved with more data.” – William Tunstall-Pedoe Data Exchange podcast Why You Should Optimize Your Deep Learning Inference Platform  Continue reading “Gradient Flow #35: Optimizing Inference, Workflow Tools, RL in Large Enterprises”

Applications of Reinforcement Learning: Recent examples from large US companies

When I wrote about enterprise applications of reinforcement learning (RL) a little over a year ago, I cited a few examples of applications for recommenders and personalization systems. At the time, the examples I listed came from large technology companies, specifically Netflix, JD, Facebook, and YouTube, as only large companies tended to have the resourcesContinue reading “Applications of Reinforcement Learning: Recent examples from large US companies”

Why you should build your AI Applications with Ray

Compute-intensive applications that incorporate machine learning should be built on top of Ray. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] Introduction As machine learning and AI become prevalent in software services and applications, most backend platforms now consist of business logic and machine learning (inference).  Business logic andContinue reading “Why you should build your AI Applications with Ray”

Gradient Flow #34: Modernizing Data Governance, DataOps for ML, Declarative Interfaces

Subscribe • Previous Issues This edition has 510 words which will take you about 3 minutes to read. “If something cannot go on forever it will stop.” – Herbert Stein Data Exchange podcast Injecting Software Engineering Practices and Rigor into Data Governance  As the amount and importance of data grows within organizations, there is growing interest inContinue reading “Gradient Flow #34: Modernizing Data Governance, DataOps for ML, Declarative Interfaces”

Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML

Subscribe • Previous Issues This edition has 548 words which will take you about 3 minutes to read. “While you are looking, you might as well also listen, linger and think about what you see.”  – Jane Jacobs Data Exchange podcast How Technology Companies Are Using Ray  Zhe Zhang is an Engineering Manager at Anyscale where heContinue reading “Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML”