DevXcon San Francisco

I just finished attending DevXcon in San Francisco at the beginning of the week before last. It’s the way the DataStax crew welcomed me into the family. It was a solidly awesome time, a great way to get started, and I’ve rated it “would do again!” Tamao (@mewzherder), Matthew (@matthewrevell), and fellow organizers did a great job putting things together!

The DevXcon is kidn of a sibling or parallel of sorts to the DevRelCon presented by Github. These events are organized by Hoopy, a consultancy of Matthew’s that specializes in helping companies around developer relations and marketing. Both of these conferences focus around this, the developer relations of software companies and how to improve that relationship companies have with their prospective developers.

UX Practices for Developers

This DevXcon a big focus of the event was on and around user experience (UX) practices. I found it interesting this was brought up in this context. User experience being a key practice of any good application, CLI or API end point, web interface, or physically the interaction in using devices, or even a weed wacker for that matter is really unquestionable. If a company designs poor interfaces, their products will suffer at market or in the case of an ongoing user experience fail, injure the person using the device.

Other Topics Around Relations

The conference had a range of topics covered, including; working with email newsletters that developers actually want to receive, DevRel’s role in product feedback, and others. I’m going to skip that and point you instead to check out Adam Duvander’s write up “DevXcon SF 2018: where UX, DX, and product came together with dev rel“.

DevRel, the practice of effectively coordinating, creating, and building content, coding, and communicating the benefits of products and services of a company, and open source project, or other organization has grown into something a bit more than merely marketing. Even though much of the work of DevRel could fall into the marketing category if by no means fits into that realm, but more closely – when effective – to engineering, support, and the technical side of the spectrum.

The biggest issues I see are the age old problem of maintaining integrity and reputation in the industry once moving into DevRel from engineering. It’s a difficult step when one has engineering or related work experience their reputation is built on and then delves into DevRel. The myth goes that we aren’t developing anymore, that we lose our coding chops. But seriously, good DevRel build products, services, and expands on what they’re advocating and showing to their respective community just as engineering is building that product or service. Good, emphasis on good, DevRel teams advocate, code, and build still, which is fundamental in my opinion to building a solid community base around any project, product, or service offering.

Advances for DevRel, Advocacy Not Evangelism

It does appear, as seen with many groups, at least we’re frequently starting to drop the misnomer title of Evangelist and pushing toward titles like Advocate, such as Microsoft has done with their Cloud Developer Advocates (or CDAs as they call them in short, because Microsoft gotta TLA like they’ve always TLA’ed). Their focus has always been on their ecosystem, etc, but they’ve refocused around a wide spectrum of tooling, many times tooling that isn’t even from Microsoft. This refocus around an advocacy approach versus and evangelism approach is a pretty big deal, especially for the end user. It’s a positive reflection that DevRel as a working group in companies is moving in a positive direction to benefit the community that uses the products and services of an organization.

Anyway, that’s just a quick summary, more on many of these topics in the future. For now, happy coding, conferencing, and cheers!

 

ML4ALL LiveStream, Talks & More

If you’re attending, or if you’re at the office or at home, you can check out the talks as they go live on the ML4ALL Youtube Channel! Right now during the conference we also have the live feed on the channel, so if you’re feeling a little FOMO this might help a little. Enjoy!

Here are a few gems that are live already!

Manuel Muro “Barriers To Accelerating The Training Of Artificial Neural Networks”

-> Introduction of Manuel

Jon Oropeza “ML Spends A Year In Burgundy”

-> Introduction of Jon

Igor Dziuba “Teach Machine To Teach: Personal Tutor For Language Learners”

-> Introduction to Igor

IDE Launcher via Amtrak Cascades to Portland for ML4ALL

Got fidgety on the train, and just wanted to write code, on the way down to Portland for ML4ALL so I wrote up some decision tree code on determining what IDE’s I want opened up. Ya know, if you do something more than twice it needs automated, so I’ve started the process of automating all startup and shutdown tasks for a day’s coding. Simplistic geeky train geek code fun code is fun geeky train code. Cheers!

package main

import (
	"time"
	"fmt"
)

var sessionMinimal, sessionMedium, sessionLong, sessionZone time.Duration
var language string

func main() {
	sessionMinimal = 15
	sessionMedium = 45
	sessionLong = 90
	sessionZone = 180

	language = "golang"

	openIde("golang", 200)
}

func openIde(languageStack string, expectedCodingTime time.Duration) {
	var ide string

	switch  {
	case expectedCodingTime  sessionZone:
		ide = stackSpecific(languageStack, false, true, true)
		fmt.Printf("Launching: %s", ide)

	}
}

func stackSpecific(language string, fastLaunch bool, featureRich bool, introspective bool) string {
	if fastLaunch == true && featureRich == true && introspective == true {
		return "\n\nCome on, you know better. You get at best two out of three.\n\n"
	}

	if fastLaunch == true && featureRich == true {
		return "Visual Studio Code"
	}

	if featureRich == true && introspective == true {
		switch language {
		case "SQL":
			return "DataGrip"
		case "C":
			return "CLion"
		case "Python":
			return "PyCharm"
		case "golang":
			return "Goland"
		case "java":
			return "IntelliJ"
		case "scala":
			return "IntelliJ"
		case "kotlin":
			return "IntelliJ"
		case "dotnet":
			return "Rider"
		case "csharp":
			return "Rider"
		case "fsharp":
			return "Rider"
		case "vbnet":
			return "Rider"
		case "javascript":
			return "Webstorm"
		case "hcl":
			return "IntelliJ"
		case "ruby":
			return "RubyMine"
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "IntelliJ"
		}
	}

	if featureRich == true {
		switch language {
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "Visual Studio Code"
		}
	}

	if introspective == true {
		switch language {
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "Visual Studio Code"
		}
	}

	if fastLaunch == true  {
		return "Sublime"
	}

	return "No IDE for you."
}

#ML4ALL Bike Ride Details

I previously posted the map when I introduced Igor and Carol, but for reference, here it is again with some additional details!

ML4ALL Ride

ML4ALL Ride

The ride will be what I’d call a “slow ride“, which is a super chill, easy going, roll through neighborhoods on the east side of Portland, down through the hip inner south east, and then back up around the waterfront. I’ll also provide a run down of a little Portland information about it’s wonky history, the neighborhood layout (check out point 2 on the map for instance, it’s called Ladd’s Addition), the awesome bridges the city has (two are unique in north America to Portland!), and more.

In addition we’ll also make a number of stops for photos, a coffee, and prospective a beer if the crew is up for it. We’ll leave at 2pm, and wrap up at 4pm in time to swing by our respective hotels and such before the evening reception. Our starting point is shown above, it’s a little hard to see but is denoted by a green dot! Basically we’re going to start at the Bossonova Ballroom Parking Lot and depart from there.

parking.png

BIKES @ Bike Town!

You may ask, but what if I don’t have a bike, I’m coming into town for this? Well, Portland has you covered! The easiest way is to pick up one of Portland’s many bike share bikes via Biketown. Which, to note, is FREE for May! To get a Biketown Bike just look for any of the orange bicycles, there’s a map on the Biketown Site too of all the stations where they’re parked, and when you download the mobile app you can see where any are nearby and easily reserve them and just go pick one up!

free

Barriers To Accelerating The Training Of Artificial Neural Networks – A Systemic Perspective – Meet Manuel Muro

manuel-muroThe real breakthrough for the modern Artificial Intelligence (AI) and Machine Learning (ML) technology explosions started back in 1943 when researchers McCulloch & Pitts came up with a mathematical model to represent that function of the biological neuron; nature’s gift that allows all life to operate and learn over time. Eventually this research would then give birth to the Artificial Neural Network (ANN).

Continue reading “Barriers To Accelerating The Training Of Artificial Neural Networks – A Systemic Perspective – Meet Manuel Muro”

Cassandra: Quick Installation & Download Notes & Details

A collection of notes and details. There’s plenty of details and docs out there, which I’ll reference a few below to get started with Cassandra. The goal with these notes is to provide a kind of summarized punch list of items to quickly get started around Cassandra/Datastax Enterprise 6 (DSE6). I’ll have a number of additional pieces coming real soon, as I’ve got some geeky experiments and related surprise implementations I’m putting together in the very near future. Let’s just say there will likely be Lego, trains, and many nodes among other fascinating elements coming together to make it happen! Enjoy the start…

The Big Details

  • Who – Avinash Lakshman (@hedvigeng) and Prashant Malik (@pmalik) originally developed Cassandra at Facebook for inbox search.
  • 2000px-Cassandra_logo.svgWhat – Apache Cassandra is a highly scalable, high-performance distributed database built with the ability to handle large amounts of structured data decentralized across many servers. In service of that goal Cassandra provides a highly available system without a single point of failure.
  • Where – Apache Cassandra can be found on the Apache Cassandra Site and the code in the Cassandra Github Repo.
  • When – Cassandra was released as open source by Facebook in 2008, and became an Apache top-level project in February of 2010.
  • Why – When you want no fixed schema, massive scale, huge storage capability options, eventually consistent, fault-tolerant, dynamic/elastic scalability, fast linear-scale performance, always on highly available, and related features.

Distributed Databases

Getting Started, Installing, Configuration, Setup, & Start

CQL – Cassandra Query Language

Intro & Architecture of Cassandra References

This is merely the beginning of the blogging, projects, and notes, if you’d like to bookmark where I’ll be linking all of my Datastax + Cassandra notes, check out my Cassandra root documentation & links page.

 

Conducting a Data Science Contest in Your Organization w/ Ashutosh Sanzgiri

ashutosh-sanzgiriAshutosh Sanzgiri (@sanzgiri) is a Data Scientist at AppNexus, the world’s largest independent Online Advertising (Ad Tech) company. I develop algorithms for machine learning products and services that help digital publishers optimize the monetization of their inventory on the AppNexus platform.

Ashutosh has a diverse educational and career background. He’s attained a Bachelor’s degree in Engineering Physics from the Indian Institute of Technology, Mumbai, a Ph.D. in Particle Physics from Texas A&M University and he’s conducted Post-Doctoral research in Nuclear Physics at Yale University. In addition to these achievements Ashutosh also has a certificate in Computational Finance and an MBA from the Oregon Health & Sciences University.

Prior to joining AppNexus, Ashutosh has held positions in Embedded Software Development, Agile Project Management, Program Management and Technical Leadership at Tektronix, Xerox, Grass Valley and Nike.

Scaling Machine Learning (ML) at your organization means increasing ML knowledge beyond the Data Science (DS) department. Several companies have Data / ML literacy strategies in place, usually through an internal data science university or a formal training program. At AppNexus, we’ve been experimenting with different ways to expand the use of ML in our products and services and share responsibility for its evaluation. An internal contest adds a competitive element, and makes the learning process more fun. It can engage people to work on a problem that’s important to the company instead of working on generic examples (e.g. “cat vs dog” classification), and gives contestants familiarity with the tools used by the DS team.

In this talk, Ashutosh will present the experience of conducting a “Kaggle-style” internal DS contest at AppNexus. he’ll discuss our motivations for doing it and how we went about it. Then he’ll share the tools we developed to host the contest. The hope being you too will find inspiration to try something in your organization!