Becoming a machine learning engineer

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled Hands-on Machine Learning with Scikit-Learn and TensorFlow. Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

As more companies adopt big data and data science technologies, there is an emerging cohort of individuals who have strong software engineering skills and are experienced using machine learning and statistical techniques. The need to build data products has given rise to what many are calling “machine learning engineers”: individuals who can work on both data science prototypes and production systems.

data science machine learning jobs
Chart by Ben Lorica.

Géron is finding strong demand for his services as a consulting machine learning engineer, and he hopes his new book will be an important resource for those who want to enter the field.

Here are some highlights from our conversation:

Continue reading “Becoming a machine learning engineer”

Natural language analysis using Hierarchical Temporal Memory

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.

In this episode of the Data Show, I spoke with Francisco Webber, founder of Cortical.io, a startup that is applying tools based on Hierarchical Temporal Memory (HTM) to natural language understanding. While HTM has been around for more than a decade, there aren’t many companies that have released products based on it (at least compared to other machine learning methods). Numenta, an organization developing open source machine intelligence based on the biology of the neocortex, maintains a community site featuring showcase applications. Webber’s company has been building tools based on HTM and applying them to big text data in a variety of industries; financial services has been a particularly strong vertical for Cortical.

Here are some highlights from our conversation:
Continue reading “Natural language analysis using Hierarchical Temporal Memory”

Time-turner: Strata San Jose 2017, day 2

There are so many good talks happening at the same time that it’s impossible to not miss out on good sessions. But imagine I had a time-turner necklace and could actually “attend” 3 (maybe 5) sessions happening at the same time. Taking into account my current personal interests and tastes, here’s how my day would look:

11 a.m.

11:50 a.m.

1:50 p.m.

2:40 p.m.

4:20 p.m.

Time-turner: Strata San Jose 2017, day 1

There are so many good talks happening at the same time that it’s impossible to not miss out on good sessions. But imagine I had a time-turner necklace and could actually “attend” 3 (maybe 5) sessions happening at the same time. Taking into account my current personal interests and tastes, here’s how my day would look:

11 a.m.

11:50 a.m.

1:50 p.m.

2:40 p.m.

4:20 p.m.

5:10 p.m.

Deep learning that’s easy to implement and easy to scale

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.

In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.

Here are some highlights from our conversation:
Continue reading “Deep learning that’s easy to implement and easy to scale”