Tag Archives: conferences

TRIP REPORT: O’Reilly Velocity & Software Architecture Conf 2019

This past week the O’Reilly Velocity and Software Architecture Conferences took place. I’ve attended both before, the 2nd time for Velocity in San Jose and the 2nd time for Software Architecture, however this time in San Jose and the last I attended was in London. The locations for these conferences dictate much about what is presented and how conversations, meeting and interacting, learning, and explorations take place during the conference, but more on those specifics in a moment.

The overarching theme from keynotes and many of the conversations I had met on a few key topics:

  1. When you’re building software, and you want to do it well you first and foremost must, absolutely must, invest in the people building your software.
  2. Focus on simplicity, remove complexity at every opportunity.
  3. Organizational structure can have direct impact in the complexity or simplicity of software, structure your organization efficiently and make every effort to keep it simple.

TLDR; Keep your people happy, focus on simplicity, minimize organizational noise from bureaucracy.

Topically Elaborating on Edge and Serverless

Ok, so a number of conversations came up around edge computing and serverless. Both interesting, but it also seems like there isn’t a strong play for the Enterprise is either space just yet. At this point however, a lot of enterprises are struggling with their Kubernetes, Cloud, and Hybrid solutions enough as it is that they haven’t even broached the edge compute and serverless realm. But that hasn’t stopped a lot of forward thinking individuals to start tackling how to cut out a useful spectrum of application with both edge and serverless.

Serverless Oh My!

Serverless, like so many other names, is kind of a garbage name at first. It effectively tells us nothing useful. It’s a word that requires more words just to give it meaning. It’s kind of like if I said “I like food!” What does this even mean? So, everybody eats food and most people like it, so what does “I like food!” actually mean? Same with serverless, because the first thing it doesn’t mean is a system that has no servers. What it generally means is something about your applications and you not doing anything with servers. It’s the mythic NoOps by simply removing the computers somehow.

Serverless, or simply code that executes via compute, and you get a result, has actually been around for some time. There were a number of startups that were well ahead of AWS Lambda and the other respective implementations that Azure and GCP have. These startups had been attempting to usher in what it took AWS’s massiveness and clout to actually get people to pay attention to. Serverless however has gone far beyond merely Lambda at AWS and now we’ve got to contend not only with the option in the existing cloud providers but where, how, and when can we get it into our data centers! The TLDR is that enterprise wants serverless and they’re interested in throwing it onto Kubernetes or whatever they’ve got. But often the infrastructure and systems to really make use of this simply isn’t there.

Most of the conversations I had evolved around the who, what, where, when, and how do we make use of these options for what we do? This is where most companies, at least enterprise and large companies, currently seem to be in the market. Then there are the companies that have already made the leap and are doing all sorts of stuff with Lambda and related serverless offerings. The gulf, that middle ground, doesn’t seem to have been broached by many others. Everybody, anecdotally of course, seems to be either trying to figure out how to start or already made the leap!

Edge Compute

This kept coming up, regardless of how or what people defined it as, it came up as something a number of people were very interested in. This notion, loosely based around using edge devices; smart phones, IoT devices, your car, or your washer for example, it could be almost anything. These devices do compute on the edge and thus the term. However it’s interesting because it isn’t like, for example, cloud computing that has core features like compute, storage, and related elements. Edge computing can run the gamut of any device doing any kind of work and the related capabilities of that device. It kind of leaves the space wide open. However, there were a few focal points that kept coming up.

The most common topic that came up around edge computing was doing tasks at point of presence. Such as having a phone do facial recognition, computing path finding (i.e. traffic directions), and related compute on the device versus round tripping it back to the cloud. It almost seems like after all these years of pushing things to the server we’re really starting in earnest to bring smart processes and tasks back to the devices we have in hand – no pun intended. It’s an interesting space, interesting paradigms, and I’m still not ready to call a specific thing within the world of edge compute and say, “that’s the next billion dollar idea”. Largely because, there are a lot of billion dollar ideas out there these days.

Speaking of edge compute and serverless, my fellow DataStaxian also had a few of these conversations on said topics. Patrick wrote up a post on a few observations over on the DataStax blog “Velocity Conference Shows What’s Gaining Velocity in Data Management“.

Geographic Location

As I mentioned, this set of conferences is in San Jose, the home of Silicon Valley, but the southern segment of the area. It’s a walk-able area with a number of places to break out from the conference and really dig into the hallway tracks (i.e. impromptu conversations!) that come up. For those willing to jump on the light rail, or scooter around, San Jose opens up even more to the local area providing a wide variety of coffee, food, and other operations to share conversations over.

All in all, the geographic location for the event is solid, being in the center of the city where it is. However one issue did arise, the Marriott lost power as an electrical fire in the control room of the multi-story hotel blew out the power. At last I checked upon leaving, it still didn’t have power! With the temperature at 105f going on multiple days at this point, the hotel because extremely hot inside, and being a kind of sealed airspace the air calculators also weren’t refreshing the air. That left a number of guests in less than stellar condition to attend, let alone attain value, from the conference events. Myself I ended up checking out in short order, getting sick the last day of the conference anyway, and being unable to provide the presentation that I had paired up with Lena (@lenadroid) for! I’ve been thinking, that maybe she and I can provide an online version of it for those that had wanted to hear us present on “Flexible Cloud Architectures: Decision Making Best Practices“.

Next year’s Velocity looks like it’ll be in Santa Clara, which doesn’t really excite me as it’s kind of a nebula of sprawling suburbia of boredom. This is were location becomes fundamental to what will or what can be the potential of secondary and tertiary conversations at a conference like this. Don’t get me wrong, the hallway track is excellent, but having options to step out and walk across the street from the event to converse further adds a tremendous value.

Santa Clara simply doesn’t do that unfortunately.

The fortunate thing between now and then, albeit the conference is moving to Santa Clara, they’re having subsequent conferences in the Velocity series in Berlin, and Software Architecture Conf series in the amazing cities of New York and Berlin. Those locations are worth traveling to for far more than a conference, increasing my interest in attending both of those future events. I’m looking forward to these!

Twitter Talk @VelocityConf

From @DataStaxDevs a thread! Click through for all ~17 parts.

 

Some Build Engineer Work – Click through for the whole construction thread.

 

 

Some of the Keynote Threads

Alena Hall – @lenadroid

 

 

 

 

 

 

Jessica Kerr – @jessitron

 

 

 

 

 

…and there were a bunch of others too, solid, check out the hash tag of #velocityconf to read up on more.

The Lagniappe

After the conference I finally managed to pick up a pocket Constitution.

 

 

 

If you’re ever in search of good coffee in San Jose, one place I found that’s tops is Academic Coffee, both the coffee and service are great. Good jovial crew and lots of cyclists in and out.

 

 

 

 

 

 

 

Making progress on the CaSMa, tweeted a bit on the topic while en route to the conference. If you’d like to get involved, please do let me know!

 

 

 

 

Other arbitrary statistics:

  • Stickers collected: 11 unique, ~7 of each. Total: 77 stickers.
  • T-shirt Swag: 2.
  • Conversations @ DataStax Booth: 11
  • Hallway Track Conversations: 7
  • Coffee Consumed: 9 over 3 days.
  • Twitter Filters Discussed: 123.
  • Fuel burned to compensate for electrical fire damage for the time of the conference: Approximately 5k gallons of fuel for the Marriott Hotel and no idea how much more fuel was or is still being burned to power the hotel.
  • Times the power still went off even with the diesel engine power trailer attached: 4.

Lots of Events & Topical Tech Discussions

This week we just had Ryan Zhang present at the Seattle Scalability Meetup. I did a little short presentation just showing some tools that I’ve been using as of late; DataGrip, and related schema migrations and Docker containers as I work through the schema migrations. It was a solid meetup and excellent conversation after meetup, big thanks to everybody who came out to the meetup and joined us for a round of drinks, amazing cheese curds and hummus at Collin’s afterwards! It was a great meetup and looking forward to getting together again on May 28th with Guinevere (@guincodes) presenting “The PR That Wouldn’t Merge“!

In other upcoming events that I’ll be at either presenting or attending. The events I’m attending let’s get talk, I’m always interested in meeting new people and learning about you’re working on, what you’re learning, and where and what efforts are of interest to you. For the events I’m presenting at the same applies, plus I’ll be standing among all the persons and presenting whatever tidbit of knowledge I’ve come to present. Hopefully it’ll be useful and informative for you and we can continue the conversation after the presentation and we all gain more insight, ideas, and ways to move forward more productively with our respective efforts. Here’s a list of the next big meetups and conferences I’m either speaking at or attending, and hope to see and meet many of you dear \m/ readers there!

Zhi Yang Presenting “Hierarchical Topic Modeling in Cancer Patients’ Mutational Profiles”

UPDATED: Video Added from the Conference!

zhi-yang.png

Introducing Zhi Yang > @zhiiiyang < presenting “Hierarchical Topic Modeling in Cancer Research”.

Topic models have been widely applied to extract topics from various range of documents or collections of texts, i.e., online customers reviews, medical records, scientific
journals, legal documents, books and etc. Its application facilitates the process for us to quickly understand the most featured and commonly shared information embedded texts without actually reading through the entire collection. In addition, topic models also allow us to access the contribution of each topic and its representations across different documents. Human genomes have been exposed to an assortment of mutational processes by contributing to unique patterns of somatic mutations. What would happen if we apply the same concept to the somatic mutations obtained from the cancer patients and look for “topics” of mutations? What would these “topics” tell us about the most important information for our health, genetic, risk factors for cancer and
something more that slip under the radar?

Shiraishi et al’s have proposed a topic model targeted for somatic mutations to capture the characteristics and burdens contributed by mutational processes. By closely examining the burdens, we’d like to compare them across different categories, say, for example, time, cancer subtype, ethnicity, smoking history, etc. Then, we’d like to develop the statistical machinery to infer the difference between the mutational profiles across different categories and associate the variations with the know exposures. This tool is potentially useful for identifying novel and existing mutational processes and correlating them with risk factors in which later can be used to monitor any treatment effects in personalized medicine and targeted therapy.

Read the publication here at biorxiv and come check out Zhi Yang’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Sachi Parikh Presenting “My Journey Learning ML and AI through Self Study as a High School Student”

UPDATED: Video Added from the Conference!

sachi-parikhIntroducing Sachi Parikh > @parikhsachi < presenting “My Journey Learning ML and AI through Self Study as a High School Student”.

Sachi is a high school student in the Bay Area who is interested in AI and Machine Learning and loves to code, read and learn. In the talk she’s put together for us she’s delved into the path she’s taken to get into this topic. I’ve seen an outline of this path and I’ll admit, I’m impressed, but you’ll have to come and attend to talk to see the outline!

Come check out Sachi Parikh’s talk and learn about this learning path at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Karl Weinmeister Presenting “Build, train, and serve your ML models on Kubernetes with Kubeflow”

UPDATED: Video Added from the Conference!

karl-weinmeisterIntroducing Karl Weinmeister > @kweinmeister < presenting “Build, train, and serve your ML models on Kubernetes with Kubeflow”.

Karl is a Developer Advocacy Manager from Google’s Developer Relations Artificial Intelligence and Machine Learning team.  Karl has worked extensively in cloud and mobile, and was a contributor to one of the first AI-based crossword puzzle solvers that is still referenced today.

Distributing ML workloads across multiple nodes has become common. To achieve higher and higher levels of accuracy, data scientists are using more data and more complex models than ever before.

Kubeflow is an open-source platform for model building, serving, and training. It is built on industry standard Kubernetes infrastructure and runs in multiple clouds and on-premises.

In this session, we’ll discuss the problems that Kubeflow solves, and how you can use it to create reproducible ML workflows.

Come check out Karl Weinmeister’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Aeva van der Veen Presenting “Gaming Rigs and ML Pipelines: how to get started with the tools you already have”

UPDATED: Video Added from the Conference!

That isn’t all though, Aeva gave a lightning talk too, included below!

aeva-vanIntroducing Aeva van der Veen > @aevavoom < presenting “Gaming Rigs and ML Pipelines: how to get started with the tools you already have”.

Aeva is an outspoken open source advocate with over a decade experience contributing to F/OSS software and communities. They have been building distributed systems on Linux-based systems since ’99, and are most well known for their work in the OpenStack community wherein they founded Ironic, the Bare-Metal-as-a-Service project. Aeva lives in rainy Seattle and enjoys staying home when not travelling for work.

If you think that only big tech companies or PhD scientists can use ML & AI, I’d like to show you that an individual open-source enthusiast can build and train a model on commodity hardware using Open Data – and then scale that up on a public cloud.

And if you’re a PC gamer, you probably already have all the tools you need!

  • fast.ai, an easy-to-learn Python ML framework
  • nvidia-docker on an Ubuntu Gaming PC
  • public-domain GIS imagery
  • a couple terabytes of storage space and a fast internet connection

This talk grew out of a startup competition last year: we tried to use public-domain satellite imagery to help predict and prevent forest fires. Even though we chose not to pursue this as a business, it’s an excellent example of how combine open source software, public data, and a gaming PC to build an ML pipeline.

Come check out Aeva van der Veen’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Jules S. Damji Presenting “MLflow: Infrastructure for a Complete Machine Learning Life Cycle”

jules-damjiIntroducing Jules S. Damji > @2twitme < presenting “MLflow: Infrastructure for a Complete Machine Learning Life Cycle”.

Jules S. Damji is an Apache Spark Community and Developer Advocate at Databricks. He is a hands-on developer with over 20 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science and MA in Political Advocacy and Communication from Oregon State University, Cal State, and Johns Hopkins University respectively.

He’s also the Program Chair for Spark + AI Summit

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.

In this session, we introduce MLflow (mlflow.org), a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics

Come check out Jules S. Damji’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!