Jon Peck

GitHub for Startups provides access to GitHub Enterprise, a powerful platform you can use to manage your DevOps lifecycle. But what does that mean? What can you actually do on GitHub? How do you get started?
And how can you get the most out of the tools available to your organization? Join us for a live session as we walk through the first steps with GitHub, an overview of how GitHub uses GitHub, and tips and best practices to have the best experience on GitHub.

What's the key to a thriving project? Other people! Learn how to invite contributions, and make your repo discoverable, understandable, secure, and friction-free with our collaboration-ready checklist.

Half of companies choose velocity over security, but these don’t need to be opposing options.

By combining a developer-first approach to eliminating vulnerabilities with a collaborative AI-powered DevOps toolchain, we can:
- double development velocity
- quarter remediation times
- increase developer happiness by 75%

Increase developer velocity, redefine collaboration, and secure your code by automating entire workflows with GitHub Enterprise. Learn from Glenn Wester, Principal Solutions Engineer at GitHub, and Sarah Khalife, Principal Solutions Engineer at GitHub, about how to reduce context switching and lower costs in your organization. You’ll leave this session with an understanding of how leveraging innersourcing, AI, and GitHub’s developer-first tooling across your tech stack can give teams better… Show more

Increase developer velocity, redefine collaboration, and secure your code by automating entire workflows with GitHub Enterprise. Learn from Glenn Wester, Principal Solutions Engineer at GitHub, and Sarah Khalife, Principal Solutions Engineer at GitHub, about how to reduce context switching and lower costs in your organization. You’ll leave this session with an understanding of how leveraging innersourcing, AI, and GitHub’s developer-first tooling across your tech stack can give teams better workflows and streamlined processes. Show less

Streamlining your tech stack’s complexity will help you finish projects faster and accomplish your business goals.

GitHub is world-renowned for its ability to promote meaningful collaborative work, and to accelerate development with a minimum of risk. Learn some of our strategies for building a culture of innovation, while minimizing product risk and scaling rapidly, with lessons learned from our own history and the millions of projects & companies we support worldwide.

Application delivery teams are under pressure to deliver value to customers as quickly as possible. But if you find that your teams are often slowed down by a variety of organizational and technical barriers, this session is for you. Join GitHub's Field CTO, Philip Holleran, and Senior Technical Advocate, Jon Peck, for an interactive discussion that will dive into three common barriers to shipping software fast, their potential negative impacts, and how application teams can use GitHub… Show more

Application delivery teams are under pressure to deliver value to customers as quickly as possible. But if you find that your teams are often slowed down by a variety of organizational and technical barriers, this session is for you. Join GitHub's Field CTO, Philip Holleran, and Senior Technical Advocate, Jon Peck, for an interactive discussion that will dive into three common barriers to shipping software fast, their potential negative impacts, and how application teams can use GitHub Enterprise to overcome them. Show less

DevOps makes building and shipping software faster, friendlier, and more collaborative—and automates almost all of the process. Successful DevOps implementation depends on many factors to be successful, including collaboration, tooling, transparency, and measurement. Join Senior Technical Advocate at GitHub, John Peck, and Principal Solutions Engineer at GitHub, Glenn Wester, in this interactive session where we'll discuss best practices, efficient techniques, and challenges you might face when… Show more

DevOps makes building and shipping software faster, friendlier, and more collaborative—and automates almost all of the process. Successful DevOps implementation depends on many factors to be successful, including collaboration, tooling, transparency, and measurement. Join Senior Technical Advocate at GitHub, John Peck, and Principal Solutions Engineer at GitHub, Glenn Wester, in this interactive session where we'll discuss best practices, efficient techniques, and challenges you might face when rolling out DevOps in the Enterprise. Show less

In this liveProject, you’ll fill the shoes of a developer for an ecommerce company. Customers provide reviews of your company’s products, which are used to give a product rating. Until now, assigning a rating has been manual: contractors read each review, decide whether it’s positive or negative, and assign a score. Your boss has decided that this is too expensive and time consuming. Your mission is to automate this process, dramatically increasing the speed of rating calculations, and… Show more

In this liveProject, you’ll fill the shoes of a developer for an ecommerce company. Customers provide reviews of your company’s products, which are used to give a product rating. Until now, assigning a rating has been manual: contractors read each review, decide whether it’s positive or negative, and assign a score. Your boss has decided that this is too expensive and time consuming. Your mission is to automate this process, dramatically increasing the speed of rating calculations, and decreasing the cost to your company. To complete this project you will have to train a machine learning model to recognize and rank positive and negative reviews, expose this model to an API so your website and partner sites can benefit from automatic ratings, and build a small webpage using FaaS, containers, and microservices that can run your model for demonstration. Show less

Machine Learning has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. When you have thousands of model versions, each written in any mix of frameworks (Python/R/Java/Ruby, PyTorch/SciKit/Caffe/Tensorflow etc), how do you efficiently deploy them as elastic, scalable, secure APIs with 10ms of latency and GPU access?

We’ve built, deployed, and scaled thousands of algorithms and machine learning models, using… Show more

Machine Learning has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. When you have thousands of model versions, each written in any mix of frameworks (Python/R/Java/Ruby, PyTorch/SciKit/Caffe/Tensorflow etc), how do you efficiently deploy them as elastic, scalable, secure APIs with 10ms of latency and GPU access?

We’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework. We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.

In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI”: a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable. Show less

Machine Learning has become an integral part of all major apps. From face recognition to product recommender engines, emotion detection to automated analytics, every product you touch contains, or can benefit from, AI -- so why is it still so difficult to identify, tune, and integrate Machine Learning?We'll investigate a number of approaches to this problem, from off-the-shelf APIs to options for training and hosting your own ML models. You'll walk away ready to hook thousands of different… Show more

Machine Learning has become an integral part of all major apps. From face recognition to product recommender engines, emotion detection to automated analytics, every product you touch contains, or can benefit from, AI -- so why is it still so difficult to identify, tune, and integrate Machine Learning?We'll investigate a number of approaches to this problem, from off-the-shelf APIs to options for training and hosting your own ML models. You'll walk away ready to hook thousands of different ready-to-run models into your app, or to productionize your own models in an on-demand, autoscaled, language-agnostic environment. Show less

Machine learning has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. When you have thousands of model versions, each written in any mix of frameworks (Python, R, Java, and Ruby, PyTorch, SciKit, Caffe, and TensorFlow, etc.), it’s difficult to know how to efficiently deploy them as elastic, scalable, secure APIs with 10 ms of latency and GPU access.

Algorithmia has seen many of the challenges faced in this… Show more

Machine learning has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. When you have thousands of model versions, each written in any mix of frameworks (Python, R, Java, and Ruby, PyTorch, SciKit, Caffe, and TensorFlow, etc.), it’s difficult to know how to efficiently deploy them as elastic, scalable, secure APIs with 10 ms of latency and GPU access.

Algorithmia has seen many of the challenges faced in this area. Jonathan Peck explores how the company built, deployed, and scaled thousands of algorithms and machine learning models using every kind of framework. You’ll learn some insights into the problems you’re likely to face and how to approach solving them. Jonathan examines the need for, and implementations of, a complete operating system for AI: a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable, and sharable. Show less

You've developed and trained your ML model, and it performs beautifully in your development environment -- but what happens when you move that into production, and it suddenly has to scale massively varying elastic workloads, compete with other models for memory and processing resources, or mesh with models deployed in other languages and frameworks? It isn't enough to simply fire up a machine instance, write a Flask wrapper, and call it a day: properly productionizing a model requires a deep… Show more

You've developed and trained your ML model, and it performs beautifully in your development environment -- but what happens when you move that into production, and it suddenly has to scale massively varying elastic workloads, compete with other models for memory and processing resources, or mesh with models deployed in other languages and frameworks? It isn't enough to simply fire up a machine instance, write a Flask wrapper, and call it a day: properly productionizing a model requires a deep understanding of container management, load balancing, CI/CD, dynamic resource allocation, and more. In this talk, we'll look at what your team does and does not need to build in order to move from weeks of deployment time to mere minutes, while preserving elasticity, low-latency, and flexibility. Show less

Machine Learning has become an integral part of all major apps. From face recognition to product recommender engines, emotion detection to automated analytics. Every product you touch contains, or can benefit from, AI — so why is it still so difficult to identify, tune, and integrate Machine Learning?

We’ll investigate a number of approaches to this problem, from off-the-shelf APIs to options for training and hosting your own ML models. You’ll walk away ready to hook thousands of… Show more

Machine Learning has become an integral part of all major apps. From face recognition to product recommender engines, emotion detection to automated analytics. Every product you touch contains, or can benefit from, AI — so why is it still so difficult to identify, tune, and integrate Machine Learning?

We’ll investigate a number of approaches to this problem, from off-the-shelf APIs to options for training and hosting your own ML models. You’ll walk away ready to hook thousands of different ready-to-run models into your app, or to productionize your own models in an on-demand, autoscaled, language-agnostic environment. Show less

Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those who don't. This talk will introduce the best practices of the tech companies already deploying, the tech stack that is needed, and the organization rhythms that are needed to be successful. This talk is ideal for engineers… Show more

Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those who don't. This talk will introduce the best practices of the tech companies already deploying, the tech stack that is needed, and the organization rhythms that are needed to be successful. This talk is ideal for engineers and leadership to attend together. Show less

You've gathered and cleaned your data, tuned your hyperparameters, trained a model that works great... on your own laptop. Where do you go from here? We'll look at a few ways of productionizing your model, from Flask on VMs to turnkey serverless hosting.

You'll leave with the ability to confidently and easily make your models available to anyone (or for your own private use) as an autoscaled, on-demand API!

You've trained machine learning models on your data, but how do you put them into production? When you have thousands of model versions, each written in any mix of frameworks (R/Java/Ruby/SciKit/Caffe/Tensorflow on GPUs etc), how do you efficiently deploy them as elastic, scalable, secure APIs with 10ms of latency?

ML has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. We've built, deployed, and scaled… Show more

You've trained machine learning models on your data, but how do you put them into production? When you have thousands of model versions, each written in any mix of frameworks (R/Java/Ruby/SciKit/Caffe/Tensorflow on GPUs etc), how do you efficiently deploy them as elastic, scalable, secure APIs with 10ms of latency?

ML has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. We've built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework. We've seen many of the challenges faced in this area, and in this talk I'll share some insights into the problems you’re likely to face, and how to approach solving them.

In brief, we’ll examine the need for, and implementations of, a complete "Operating System for AI": a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable. Show less

In this series of courses, you'll learn how to:

- Make use of a gigantic catalog of Machine Learning functions
- Manage your data and account
- Deploy your own ML models and Serverless Functions on Algorithmia

This course will introduce students to the fundamentals of serverless computing. What does a serverless stack look like, where and how can we use it, what different forms can it take, and how will it reduce our Dev Ops overhead. We'll then progress to actual implementations on specific platforms, including AWS Lambda, Azure functions, Google Cloud Functions, and Algorithmia. By the end of the course, students will be able to integrate serverless computing into their own software solutions, in… Show more

This course will introduce students to the fundamentals of serverless computing. What does a serverless stack look like, where and how can we use it, what different forms can it take, and how will it reduce our Dev Ops overhead. We'll then progress to actual implementations on specific platforms, including AWS Lambda, Azure functions, Google Cloud Functions, and Algorithmia. By the end of the course, students will be able to integrate serverless computing into their own software solutions, in different environments and platforms. Show less

Politicians tweet to broadcast their ideas, criticize their opponents, and build support. They have near-complete control over what they choose to broadcast (unlike TV & journalism where a third-party is involved).

Citizens use Twitter as an open forum, often mentioning politicians by handle (username), and politicians have almost zero direct control over citizens’ tweets

What do politicians choose to talk about? How do people talk about them?

In this talk, we explore a… Show more

Politicians tweet to broadcast their ideas, criticize their opponents, and build support. They have near-complete control over what they choose to broadcast (unlike TV & journalism where a third-party is involved).

Citizens use Twitter as an open forum, often mentioning politicians by handle (username), and politicians have almost zero direct control over citizens’ tweets

What do politicians choose to talk about? How do people talk about them?

In this talk, we explore a newly-created tool for analyzing tweets by and about political candidates, using Google App Engine and Algorithmia. Show less