- REFACTORING! : Martin Fowler speaking in Austin
https://an-evening-with-martin-fowler.eventbrite.com When we first found out that Martin Fowler (yes, the Refactoring Martin Fowler) was coming to Austin for Data Day Texas, we reached out and asked if he could speak. We're happy to announce that, in partnership with our friends at ThoughtWorks, Martin Fowler will be speaking at the AT&T Conference Center on the eve of Data Day Texas. Don't miss this free talk! The event is free, but you MUST RSVP at Eventbrite: https://an-evening-with-martin-fowler.eventbrite.com See you there!
- The Texas AI Summit
For full details and registration, visit: http://texasaisummit.com Each successive year at the Data Day Texas conference, we have gotten an increasing number of requests for more AI presentations and workshops - not high level pitches, but real-world use cases and deep dives. Given that the last Data Day Texas had over 80 presentations, and the next will likely have the same number, it didn't seem practical to try to cram more content into a single day. Although many have suggested it, we've resisted calls to make Data Day a multi-day event. Tickets for two-day event would cost almost twice as much -- and we want to keep Data Day affordable. Consequently, we decided create a new event dedicated solely to Artificial Intelligence - the Texas AI Summit - to be held on the day before Data Day Texas. This allows folks the opportunity to attend Data Day, or the Texas AI Summit -- or both. By hosting the two events back to back, we make it possible for all of our out of town visitors to attend both. The focus at the Texas AI Summit is real-world use cases, deep dives, models and methods. These are some of the topics to be covered: AI in the Enterprise, AI-based Analytics, Deep Learning, Human/Customer-Centered AI, Intelligent Assistants / Chatbots, Intelligent Applications, Machine Learning, Neural Networks, PyTorch, TensorFlow, Human in the Loop. For full details and registration, visit: http://texasaisummit.com
- Hands-on Introduction to PyTorch for Machine Learning
Details and registration at: https://pytorch-for-machine-learning.eventbrite.com This is a paid class. Overview This 4 hour workshop will introduce students to using PyTorch for Machine Learning. The class will be taught by Graham Ganssle of Expero. The course will use the example of an introductory customer journey. The problem presented will be as such: If Sally purchases a mattress,two trash cans, and a couch, then she may fit into a “new homeowner” customer classification. Perhaps a new home owner also needs a dish set! But more than just cross-selling needed home goods, can we start to establish a holistic view of Sally? A view that will guide us on how to be a better company that serves our customers better? The technique covered in this four hour mini-course is the first building block for creating a comprehensive view of a company’s customers. This stepping stone is fundamental in predicting customers’ next actions. In this course we will learn the basics of PyTorch, including: ● Tensors ● Variables ● Automatic gradient calculation ● Back - Propagation ● Building and training simple neural networks with hidden layers. Students are advised to bring a laptop and code along, although it should be noted that PyTorch is currently only supported in Linux and Mac OS (not Windows). The course assumes no previous knowledge of PyTorch, and though some understanding of neural networks is encouraged, the instructor will diagram the network architecture and discuss its use on the whiteboard. Details and registration at: https://pytorch-for-machine-learning.eventbrite.com This is a paid class. About the Instructor Graham Ganssle loves data. His favorite part of work at Expero is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction. Graham Ganssle’s physics Ph.D. focused on digital signal processing, specifically on a (then) new optimization method which used naturally coupled wavefields to stabilize convergence. He also holds a masters degree in applied physics and a professional geoscientist license. Graham worked in the oil and gas vertical for ten years, performing data science and quantitative geophysics for clients around the world. He has numerous publications on a variety of scientific topics and has been awarded both scientific and business achievement awards. Details and registration at: https://pytorch-for-machine-learning.eventbrite.com This is a paid class.
- Drinks and Data - Tonight - send off for Steve Guzman
If you've been to any of our events or conferences, you've no doubt run into Steve Guzman (https://www.linkedin.com/in/theguz/), of Guzmedia (http://guzmedia.com). Steve has not only managed the AV for most of our conferences over the last few years, he's also interviewed many members of the local data community, and recorded many of our meetups (http://youtube.globaldatageeks.org). Steve is heading off to Denver this month. We're hosting a special edition of Drinks and Data to say farewell. Please join us tonight, and help give Steve a proper sendoff.
- Jared Lander: Making R Go Bigger and Faster
This is a joint meetup (https://www.meetup.com/Austin-R-User-Group/events/237871495/) with the fine folks at the Austin R User Group (https://www.meetup.com/Austin-R-User-Group/). If you RSVPed there, you do not need to RSVP here as well. Louise Black of Global Data Geeks will onhand at the meetup to raffle off a free ticket to R User Day (http://ruserday.com). Jared Lander (https://www.jaredlander.com/) (LinkedIn (https://www.linkedin.com/in/jaredlander/)) is the Chief Data Scientist of Lander Analytics (https://www.landeranalytics.com/) a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup (https://www.meetup.com/nyhackr/) and the New York R Conference (http://www.rstats.nyc/) and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts. Jared specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics (https://www.amazon.com/Everyone-Advanced-Analytics-Graphics-Addison-Wesley/dp/0321888030), a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
- Funny stories about logs, and observability for the future of complex systems.
Special thanks to the folks at Kasasa (https://kasasa.com/) for hosting this joint meetup with Austin DevOps (https://www.meetup.com/austin-devops/events/238250504/). Our dear friend Charity Majors (https://www.google.com/search?q=Charity+Majors&ie=utf-8&oe=utf-8), Co-Founder and CTO of honeycomb.io (https://honeycomb.io/) is returning to Austin for Serverless Conf (https://austin.serverlessconf.io/). She graciously agreed to come a day early and share the latest with us. Details forthcoming.... Abstract Logs are easy, right? Logs will never bite you in the ass, right? Hahahaha, you probably don't even know how many times logs were the cause and/or solution to problems in production. But we'll talk about them! (Bring your own stories to share.) We'll also talk about ways that the futures of metrics, logs, APM are diverging, and what we're doing at honeycomb.io (http://honeycomb.io/) to bring the future to observability for software engineers. If you've ever been frustrated by the yawning gap between time series aggregated metrics (rigid, sparse) and log files (server-side string parsing in 2017, really?) maybe this is for you. Will give a demo of honeycomb and talk about ways it solves and doesn't solve some of these problems. P.S. All your dashboards can go die in a fire. About the Speaker Charity Majors is a co-founder and engineer at Honeycomb.io, a startup that blends the speed of time series with the raw power of rich events to give you interactive, iterative debugging of complex systems. She has worked at companies like Facebook, Parse, and Linden Lab, as a systems engineer and engineering manager, but always seems to end up responsible for the databases too. She loves free speech, free software and a nice peaty single malt. Agenda 6:30 - Meet and Greet / Networking 7:00 - Announcements and featured talk 8:30 - Adjourn to whiskey bar
- Designing Product Recommendation Engines for the New Age of Digital Commerce
This meetup will also be a launch for Garrett Eastham's new blog - Data Exhaust (http://dataexhaust.io/). Abstract In today’s world of rampant digital commerce growth, the topic on every executive’s mind is personalization – or rather how can the ecommerce experiences of tomorrow provide customers with product guidance tailored specifically to their unique needs and tastes. While this is not a new topic in the world of ecommerce innovation, it is has certainly been an evolving topic over the years as the techniques and technology – which some claim is responsible for up to 35% of Amazon.com’s total online revenue (https://www.martechadvisor.com/articles/customer-experience/recommendation-engines-how-amazon-and-netflix-are-winning-the-personalization-battle/) – has moved from a proprietary form of intellectual property to an entire studied field of machine learning, complete with open source frameworks and datasets. In this talk, Austin's own Garrett Eastham (http://dataexhaust.io/) will introduce the Machine Learning Canvas (http://www.louisdorard.com/machine-learning-canvas) - a template for developing new or documenting existing predictive systems based on machine learning. For additional details, visit Garrett's blog post on DataExhaust: http://dataexhaust.io/designing-product-recommendation-engines-for-the-new-age-of-digital-commerce/ About the speaker Garrett Eastham is a practicing data scientist and serial entrepreneur working at the intersection of Artificial Intelligence and Digital Commerce. He has been working within enterprise ecommerce for the past 6 years - working exclusively with leading retail technology innovators such as Edgecase (which he founded), Bazaarvoice, and RetailMeNot. Agenda 6:30 PM - Networking / meet and greet 7:00 PM - Announcements and featured presentation 8:30 PM - Adjourn to pub.
- Holden Karau on Debugging Apache Spark - Making Sense of Stack Traces & More
This is a joint event with the Austin Spark Meetup (https://www.meetup.com/austin-spark-meetup/events/237914759/). We will be raffling off two tickets to Data Day Texas 2018 at the meetup. You must RSVP and be present to enter. Our dear friend Holden Karau (https://www.google.com/search?q=%22Holden%20Karau&rct=j) will be returning to Austin to share the latest Spark goodness. She'll be previewing her Strata SJ talk for us in Austin. You'll see it first! Abstract Debugging Apache Spark - Making Sense of Stack Traces & More Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. Holden Karau explores how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, and some common errors and how to detect them. Spark’s own internal logging can often be quite verbose. Holden and Rachel demonstrate how to effectively search logs from Apache Spark to spot common problems and discuss options for logging from within your program itself. Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but Holden and Joey look at how to effectively use Spark’s current accumulators for debugging before gazing into the future to see the data property type accumulators that may be coming to Spark in future versions. And in addition to reading logs and instrumenting your program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems. Holden covers how to quickly use the UI to figure out if certain types of issues are occurring in our job. About the Speaker Holden Karau (https://www.linkedin.com/in/holdenkarau) @holdenkarau (http://twitter.com/holdenkarau) is a software development engineer and is active in open source. She a co-author of Learning Spark & Fast Data Processing with Spark and has taught intro Spark workshops. Prior to IBM she worked on a variety of big data, search, and classification problems at Alpine, DataBricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelors of Mathematics in Computer Science. Outside of computers she enjoys dancing & playing with fire. Agenda 6:30: Meet and Greet / Networking 7:00: Announcements and Featured Talk 8:30: Adjourn to pub
- Truth is Dead: an encore presentation from Jonathon Morgan
One of the most popular talks at the recent Data Day Texas (http://datadaytexas.com) was by Jonathon Morgan (http://goodattheinternet.com/) of Partially Derivative (http://partiallyderivative.com/about/) / New Knowledge (http://newknowledge.io/team/) / et al. The talk was entitled Truth is Dead. Provocative? Yes. Would you expect anything less from Jonathon? We had planned to record this talk for the Global Data Geeks channel (http://youtube.globaldatageeks.org), but AV issues prevented us from getting the quality we wanted. So, we asked Jonathon to give an encore presentation. Don't miss it! Abstract If the 2016 elections made one thing clear, it's that Americans are divided. Hyper-partisan rhetoric, social media filter bubbles, and fake news didn't just shape the outcome, but are warping how Americans experience reality -- even inspiring real-world acts of violence. We can quantify these different realities and their consequences with neural networks trained to examine language from social media. Using word vectors from models trained on partisan corpora embedded in a neutral vector space, we measured the increasing radicalization of the alt-right, the distance between the left and right on contentious campaign issues, and how racist rhetoric has infected mainstream political discourse. The results are alarming, but while voters seem to inhabit different realities when it comes to politics and policy issues like immigration, guns, taxes, we'll show that, when it comes to everyday issues like work, family, and even government, we may be more alike than we think. About the Speaker Jonathon Morgan (http://goodattheinternet.com/) (Linkedin (https://www.linkedin.com/in/jonathonmorgan)) is Founder and CEO at New Knowledge (http://newknowledge.io/) - a company building technologies to understand and predict human behavior. As part of his ongoing work applying quantitative methods to combating violent extremism, he served as an advisor to the White House and State Department, co-authored the ISIS Twitter Census for the Brookings Institution, and develops new technology with DARPA. Jonathon is also the co-host of Partially Derivative (http://partiallyderivative.com/), an unrealistically popular podcast about data science and drinking. Agenda 6:30pm Networking 7:00pm Announcements and Featured talk 8:30pm Adjourn to pub