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Ship It! Ship It! #6

Follow the money

This week on Ship It! Gerhard talks with Ian Miell, author of Docker in Practice as well as Learn Git, Bash, and Terraform the Hard Way. They talk about being comfortable with the uncomfortable, focusing on the tech while keeping a holistic view of the business. Following the money is key. Ian explains this concept really well, and Gerhard feels fairly confident you will be better off if you pay attention. Let us know in the comments!

Ship It! Ship It! #5

The foundations of Continuous Delivery

This week on Ship It! Gerhard talks with Dave Farley, co-author of Continuous Delivery and the inventor of the Deployment Pipeline. Today, most of us ship code the way we do because 25 years ago, Dave cared enough to drive the change that we now call CI/CD. He is one of the great software engineers: opinionated, perseverant & focused since the heydays of the internet. Dave continues inspiring and teaching us all via his newly launched YouTube channel, courses and recent books. The apprentice finally meets the master 🙇‍♂️🙇‍♀️

Ship It! Ship It! #4

OODA for operational excellence

This week on Ship It! Gerhard talks with Ben Ford, former Royal Marine and founder of Commando Development, about the OODA loop (observe, orient, decide, act). Shipping is just a small part of it. The OODA loop that you know is probably the wrong one. We explore Mission & Command, Situational Awareness and a few other practices that will help you deal with complexity as you code and ship. As a former Royal Marine Commando, Ben learned these skills the hard way, and then refined them over many years as a software engineer. Check out the diagrams in the show notes - they are a work of art and precision.

Ship It! Ship It! #1

Introducing Ship It!

Welcome to Ship It! This is a new show from Changelog about shipping software - and all the details, challenges, and problems that surface. Changelog SRE Gerhard Lazu is taking us on a journey into the world of shipping code, infrastructure, ops, and the people making it happen.

Shipping is near and dear to every developers’ heart. We do it every day. It’s the essential first step. You have to ship it to share your ideas with the world. New episodes ship weekly.

The Changelog The Changelog #441

Inside 2021's infrastructure for Changelog.com

This week we’re talking about the latest infrastructure updates we’ve made for 2021. We’re joined by Gerhard Lazu, our resident SRE here at Changelog, talking about the improvements we’ve made to 10x our speed and be 100% available. We also mention the new podcast we’ve launched, hosted by Gerhard. Stick around the last half of the show for more details.

Ops tech.channable.com

Nix is the ultimate DevOps toolkit

At Channable we use Nix to build and deploy our services and to manage our development environments. This was not always the case: in the past we used a combination of ecosystem-specific tools and custom scripts to glue them together. Consolidating everything with Nix has helped us standardize development and deployment workflows, eliminate “works on my machine”-problems, and avoid unnecessary rebuilds. In this post we want to share what problems we encountered before adopting Nix, how Nix solves those, and how we gradually introduced Nix into our workflows.

If Nix is intriguing to you, you’re going to love an upcoming episode of The Changelog. 😉

HackerNoon Icon HackerNoon

Why ML in production is (still) broken and ways we can fix it

Hamza Tahir on HackerNoon:

By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.

Productionizing ML workflows has been a trending topic on Practical AI lately…

Why ML in production is (still) broken and ways we can fix it

Go Time Go Time #162

We're talkin' CI/CD

Continuous integration and continuous delivery are both terms we have heard, but what do they really mean? What does CI/CD look like when done well? What are some pitfalls we might want to avoid? In this episode Jérôme and Marko, authors of the book “CI/CD with Docker and Kubernetes” join us to share their thoughts.

Machine Learning huyenchip.com

The MLOps tooling landscape in early 2021 (284 tools)

Chip Huyen:

While looking for these MLOps tools, I discovered some interesting points about the MLOps landscape:

  1. Increasing focus on deployment
  2. The Bay Area is still the epicenter of machine learning, but not the only hub
  3. MLOps infrastructures in the US and China are diverging
  4. More interests in machine learning production from academia

If MLOps is new to you, Practical AI did a deep dive on the topic that will help you sort it out. Or if you’d prefer a shallow dive… just watch this.

Founders Talk Founders Talk #73

Balancing business and open source

Raj Dutt is the founder and CEO of Grafana Labs. Grafana has become the world’s most popular open source technology used to compose observability dashboards (we use Grafana here at Changelog). Raj and team are 100% focused on building a sustainable business around open source. They have this “big tent” open source ecosystem philosophy that’s driving every aspect of building their business around their open source, as well as other projects in the open source community. But, to understand the wisdom Raj is leading with today, we have to go back to where things got started. To do that we had to go back like Prince to 1999…

Gerhard Lazu changelog.com/posts

The new changelog.com setup for 2020

In this post I share the latest 2020 and beyond details for changelog.com’s infrastructure.

Why Kubernetes? How is Kubernetes simpler than what we had before? What was our journey to running production on Kubernetes? What worked well? What could have been better? What comes next for changelog.com? Read this post and listen to episode #419 to learn all the details.

Ops grafana.com

Grafana Tempo is a high volume, distributed tracing backend

Tempo is cost-efficient, requiring only object storage to operate, and is deeply integrated with Grafana, Prometheus, and Loki. Tempo can be used with any of the open source tracing protocols, including Jaeger, Zipkin, and OpenTelemetry. It supports key/value lookup only and is designed to work in concert with logs and metrics (exemplars) for discovery.

Add this to the incredibly impressive open source portfolio at Grafana Labs.

Kubernetes keel.sh

Keel is a tool for automating Kubernetes deployment updates

kubectl is the new SSH. If you are using it to update production workloads, you are doing it wrong. See examples on how to automate application updates.

We’re using this in our new Kubernetes-based infrastructure (more details on that coming to a podcast near you). Keel runs as a single container, scanning Kubernetes and Helm releases for outdated images. Super cool stuff, and even has a web interface (which we’re not using yet, but should).

Keel is a tool for automating Kubernetes deployment updates

Terminal github.com

What's new in htop 3

Everyone’s (or at least my) favorite system monitoring tool is still alive and kickin’ with a big 3.0 release. In addition to a new display option to show CPU frequency in CPU meters, optional vim key mapping mode, and many other goodies, the big news is this:

New maintainers - after a prolonged period of inactivity from Hisham, the creator and original maintainer, a team of community maintainers have volunteered to take over a fork at htop.dev and github.com/htop-dev to keep the project going.

Open source FTW!

More good news: Hisham has agreed to join us on Maintainer Spotlight!

Practical AI Practical AI #97

MLOps and tracking experiments with Allegro AI

DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.

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