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Video Highlights: MLDataR – a Data Package for Supervised Machine Learning in R

The video presentation below is courtesy of Gary Hutson, regarding a new R package he launched recently on CRAN to provide example ML data sets for supervised machine learning problems. The data sets have examples in healthcare, but he plans to widen to include other types of data.

Video Highlights: Expert.ai – Executive Interview

In this interview, expert.ai’s CEO Walt Mayo gives an update on the company’s recent FY20 results. He discusses the technology that is being developed as part of the company’s Path to Lead five-year strategy and outlines how the company expects to commercialise it. He discusses the wider natural language understanding/processing (NLU/NLP) market, highlighting recent M&A activity. Finally, he outlines the key milestones the company is targeting over the next 12 months.

Video Highlights: Time Series Analysis with Pandas

The topic “Time Series Analysis with Pandas” was presented by Joshua Malina, former Data Scientist at American Express, held at the 2019 Data Science Salon event in Miami. In his talk, Joshua explains how American Express utilizes Pandas for the analysis and manipulation of big amounts of time series data in an easy, flexible and powerful way.

Video Highlights: ML System Design for Continuous Experimentation

While ML model development is a challenging process, the management of these models becomes even more complex once they’re in production. Shifting data distributions, upstream pipeline failures, and model predictions impacting the very data set they’re trained on can create thorny feedback loops between development and production.

Video Highlights: Andrew Ng on Career Advice / Reading Research Papers

Stanford University, CS230 is a widely revered course to learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Students learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. In the video lecture below, Andrew Ng Adjunct Professor, Computer Science, presents Lecture 8 which touches on career advice and also tips for reading research papers.

Video Highlights: GTC November 2021 Keynote Highlights with NVIDIA CEO Jensen Huang

In this video presentation, NVIDIA CEO Jensen Huang covers the highlights of his GTC21​ November keynote that presents the latest breakthroughs in AI​, data science, high performance computing, graphics, edge computing, networking, and autonomous machines.

Video Highlights: Lessons From the Field in Building Your MLOps Strategy

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: Improving ML Systems Beyond First A/B Test

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

[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

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).