Our friends over a Comet produced the video presentation below, hosted by Harpreet Sahota, to help you learn when & how to deploy MLOps from experts who have done it! In discussions with leading organizations utilizing ML like The RealReal and Uber, Comet compiled real-world case studies and organizational best practices for MLOps in the enterprise.
Video Highlights: Lessons From the Field in Building Your MLOps Strategy
October 31, 2021 by Leave a Comment
Video Highlights: Improving ML Systems Beyond First A/B Test
October 17, 2021 by Leave a Comment
Presented by Vijay Pappu, Senior ML Engineering Manager, Personalization Lead at Peloton, this talk focuses on any ML systems that rely on a feedback loop for improvement. How do we measure the efficacy of an ML system?
Webinar: How to Use External Consumer Insights and Marketing Data to Build a Customer-centric Business
September 20, 2021 by Leave a Comment
[SPONSORED CONTENT] In this virtual session, AWS Data Exchange will host a discussion with thought leaders from companies such as Acxiom and BlastPoint. They will share how organizations from big box retailers to automotive brands are using consumer insights data to reach new customers, drive real business change, and increase longevity. MON, SEPTEMBER 27 at 11AM PT | 2PM ET
Video Highlights: Minimize Risk and Accelerate MLOps With ML Monitoring and Explainability
August 8, 2021 by Leave a Comment
In the presentation below, Amit Paka, Chief Product Officer and Co-founder, from our friends over at Fiddler AI, spoke at the Machine Learning in Finance Summit discussing the importance of monitoring and explainable AI (XAI).
Video Highlights: FeatureTerminatoR Package for R
August 1, 2021 by Leave a Comment
FeatureTerminatoR is an R package to remove unimportant variables from statistical and machine learning models automatically. The motivation for this package is simple, while there are many packages that do similar things, few of them perform automated removal of the features from your models. The author provides the video presentation below to help get you familiar with how the package works.
Video Highlights: A Path Into Data Science
July 18, 2021 by Leave a Comment
Are you interested in getting ahead in data science? On this TalkPython podcast episode, you’ll meet Sanyam Bhutani who studied computer science but found his education didn’t prepare him for getting a data science-focused job. That’s where he started his own path of self-education and advancement. Now he’s working at an AI startup and ranking high on Kaggle.
Video Highlights: Running Effective Machine Learning Teams
July 11, 2021 by Leave a Comment
In the video presentation below, Niko Laskaris, Data Scientist and Head of Strategic Projects at MLOps solutions provider Comet, hosts a compelling webinar showing you how to address many issues regarding AI/ML. He also shares case studies, and examines the shortfalls of traditional and agile practices applied to ML teams.
Video Highlights: Thinking Sparse and Dense
July 4, 2021 by Leave a Comment
The video below, “Thinking Sparse and Dense” is the presentation by Paco Nathan from live@Manning Developer Productivity Conference, June 15, 2021. In a Post-Moore’s Law world, how do data science and data engineering need to change? This talk presents design patterns for idiomatic programming in Python so that hardware can optimize machine learning workflows.
Video Highlights: Emil Eifrem on the Origins of Neo4j and the Ubiquity of Graphs
June 27, 2021 by Leave a Comment
The video below is from a webinar for Neo4j’s APAC Quarterly Customer Update. It includes a fascinating conversation between Emil Eifrem, Co-Founder and CEO, and Nik Vora, the Vice President of Neo4j APAC.
AI Under the Hood: Object Detection Model Capable of Identifying Floating Plastic Beneath the Surface of the Ocean
June 17, 2021 by Leave a Comment
A group of researchers, Gautam Tata, Sarah-Jeanne Royer, Olivier Poirion, and Jay Lowe, have written a new paper “DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models.” The workflow described in the paper includes creating and preprocessing a domain-specific data set, building an object detection model utilizing a deep neural network, and evaluating the model’s performance.
