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The Practical Quant's Blog

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You'll want Nexar's newly released Live Map for your city

July 17, 2019 Comments (0)

Extracting and exposing valuable insights to enable smart cities and many other applications.I recently had the privilege of getting a preview of Nexar's Live Map, from my friend, Nexar's CTO and co-founder Bruno Fernandez-Ruiz. Nexar uses off-the-shelf smartphones and dash-cams, sophisticated data ingestion, data processing, sensor fusion, and machine learning software to realize their vision of creating the largest safe driving network. To date the company has recorded many miles of driving...

Managing machine learning in the enterprise: Lessons from banking and health care

July 15, 2019 Comments (0)

A look at how guidelines from regulated industries can help shape your ML strategy.By Ben Lorica, Harish Doddi, David Talby.As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model...

Tools for machine learning development

July 3, 2019 Comments (0)

The O'Reilly Data Show: Ben Lorica chats with Jeff Meyerson of Software Engineering Daily about data engineering, data architecture and infrastructure, and machine learning.By Jenn Webb.In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine...

RISELab’s AutoPandas hints at automation tech that will change the nature of software development

July 1, 2019 Comments (0)

Neural-backed generators are a promising step toward practical program synthesis.There's a lot of hype surrounding AI, but are companies actually beginning to use AI technologies? In a survey we released earlier this year, we found that more than 60% of respondents worked in organizations that planned to invest some of their IT budgets into AI. We also found that the level of investment depended on how much experience a company already had with AI technologies, with companies further along the...

One simple chart: Who is interested in Spark NLP?

June 27, 2019 Comments (0)

As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.In July 2016, I broached the idea for an NLP library aimed at Apache Spark users to my friend David Talby. A little over a year later, Talby and his collaborators announced the release of Spark NLP. They described the motivation behind the project in their announcement post and in this accompanying podcast that Talby and I wrote, as well as in this recent post comparing popular open source...

AI and machine learning will require retraining your entire organization

June 26, 2019 Comments (0)

To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.In our recent surveys AI Adoption in the Enterprise and Machine Learning Adoption in the Enterprise, we found growing interest in AI technologies among companies across a variety of industries and geographic locations. Our findings align with other surveys and studies—in fact, a recent study by the World Intellectual Patent Office (WIPO) found that the...

What are model governance and model operations?

June 24, 2019 Comments (0)

A look at the landscape of tools for building and deploying robust, production-ready machine learning models.Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. A few factors are contributing to this strong interest in implementing ML in products and services. First, the machine learning community has conducted groundbreaking research in many areas of interest to companies, and much of this research has been...

Enabling end-to-end machine learning pipelines in real-world applications

June 20, 2019 Comments (0)

The O'Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models.In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end...

The quest for high-quality data

June 18, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.By Ihab Ilyas and Ben Lorica.“AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly...

AI adoption is being fueled by an improved tool ecosystem

June 11, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]We now are in the implementation phase for AI technologies.In this post, I share slides and notes from a keynote that Roger Chen and I gave at the 2019 Artificial Intelligence conference in New York City. In this short summary, I highlight results from a — survey (AI Adoption in the Enterprise) and describe recent trends in AI. Over the past decade, AI and machine learning (ML) have become extremely active research areas: the web site...