AI and ML Learning Paths

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Learning Paths: Hands-on AL & ML software development skills

AI and Machine Learning are transforming the fundamentals of software development. For software engineers, software architects and software leaders, embracing these technologies and learning the core skills are critical components for capitalising on this new movement.

Our 2-day practical Learning Paths will teach you the essential tools, practices, and techniques that will equip you with practical skills you can use immediately. Get hands-on experience and engage in practical assessments to apply your learning.

All our Learning Paths are developed and delivered by software engineers driving innovation and change in AI and ML so you’ll learn from those actively working in this field.

Introduction to AI/ML for Software Engineers

Master the tools, practices and techniques of AI and Machine Learning in our 2-day Learning Paths.
All Learning Paths take place March 5-6, 2020 at the The Queen Elizabeth II Conference Centre London.

Introduction to AI/ML for Software Engineers

AI/ML is being widely adopted and incorporated into almost every kind of software application.  Software Engineers need to have a thorough grasp of what AI/ML is, and understand how to incorporate AI/ML into the software development lifecycle.

This Learning Path is a hands-on introduction to Machine Learning from a software professional’s point of view.  It will provide attendees with a solid understanding of Machine Learning concepts and practical skills to develop Machine Learning-powered applications.

Over the course of two days, attendees will work through multiple exercises to reinforce the learning of Machine Learning concepts and development lifecycle.  Attendees will get their hands dirty with exploratory data analysis, feature engineering, training, evaluating, and deploying Machine Learning models. Toward the end of this Learning Path, attendees will be challenged with a small Machine Learning project to apply their newly acquired skills to develop and train a Machine Learning model.

Learning Path Facilitator

Hien Luu is a technical lead of the Data Services Platform team at LinkedIn where he focuses on building big data infrastructure and big data applications. He loves working with big data technologies and recently became a contributor of Apache Pig project. He enjoys teaching and is currently an instructor of the Hadoop: Big Data Processing course at UCSC Silicon Valley Extension school. He has given presentations at various conferences and user groups like Hadoop Summit 2013, JavaOne, Silicon Valley CodeCamp and SVForm Software & Architecture user group.

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Prerequisite knowledge


What information do people need to know?

  • 1-2 years of professional coding experience

What tools/libraries/environments will people use within the course?

  • Python
  • Jupyter IPython
  • Scikit-learn
  • MLflow



Date and Location

March 05 - 06, 2020,

The Queen Elizabeth II Conference Centre


Each attendee will receive a Certificate of Achievement.

Training, Tracking, Testing and Deploying ML

Learning and implementing ML and AI methodologies is increasingly on many developers’ ToDo-Lists. Getting trained on these topics is a relatively easy thing to do with recent easy-to-use and ready-made libraries. However, it is more difficult to keep track of multiple experiments and to deploy them in a reproducible fashion into production using CI/CD. This workshop is targeted towards developers who want to learn the tooling to manage these topics. 

The value of this workshop is that it provides you with a guided walkthrough to produce a  working setup that addresses many common problems when deploying machine learning (and other services) into production. In particular, the workshop provides templates for CI/CD pipelines, kubernetes deployments, and machine learning and tracking. Once these templates are well understood, it should be possible to transfer them to other problems at your day job, which is a great advantage over learning on your own or trying to resolve bugs encountered during development. Additionally, this workshop offers a good overview of the possibilities of automation in DevOps. 

This is a two-day workshop and you should join if either of the following points applies to you:

  • You are a developer and want to expand your knowledge in DevOps and traceable machine learning
  • You are a technical lead, you can code and you want to see DevOps in action
  • You need to find a way to reliably deploy machine learning to production

The objectives of this workshop are that you can:

  • Set up a kubernetes cluster in AWS that provides an MLFlow instance logging to a database located in the cluster using the provided terraform files
  • Train a model in sklearn that is tracked by MLFlow
  • Write a CI/CD pipeline that tests and lints your code and automatically creates a docker container
  • Deploy a model using MLFlow and the created docker container
  • Test the deployed model to ensure it provides reasonable responses

It is explicitly not the goal of this workshop to introduce the tools in all their details. The focus is to provide attendees with a “starter-kit” of working code and knowledge, which can be expanded upon. This workshop shall provide guidance and points to start from which you can utilize in your day-to-day job.

As stated in the objectives, we will start with setting up a kubernetes cluster with services we will need, namely MLFlow and a PostgreSQL database. For this we will utilise a provided terraform script, which will be explained in detail and deployed by everyone individually.

This will leave everyone with their individual EKS cluster and services.

Then, we write code to train a machine learning model. This code will be tested, linted and dockerized using a gitlab CI/CD pipeline. After the Docker image is available we will execute it as a Kubernetes Job, which stores the final model to MLFlow.

On the second day, we will build a service that hosts the model we trained and exposes REST Api. This service will be again tested, linted and dockerized by a gitlab CI/CD pipeline. Then we will adapt our terraform files to deploy the new service into our kubernetes cluster.

Finally, we will test our deployment and query it for predictions.

Learning Path Facilitator

Jendrik is Head of Data Science at Nooxit. He formerly worked at Aurubis and Akka Germany on Data Science and Deep Learning in the field of industry 4.0 and autonomous machines.
At the same time he took part in the Udacity Self-Driving Car Nanodegree, participating with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California.
There, the group of students taught a car to drive around every turn of the race track autonomously. 

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Prerequisite knowledge

Attendee Prerequisites:

To attend this workshop you should be:

  • Very familiar with Python. All program code will utilise it. You should be as well familiar with scikit-learn and pandas, as this workshop doesn’t focus on training a machine learning problem, but more on how to track and deploy it.
  • You should have a basic understanding of either sh, bash or zsh
  • You should understand the concept of a Docker container
  • It is definitely a plus, if you are familiar with kubernetes and/or terraform

Attendee Tools/Equipment:

  • You have to have a laptop with WiFi capability
  • You have to have a gitlab account (a free one is enough)
  • We will provide virtual machines with the necessary tooling installed.
  • We will provide access to an AWS account for the time of the workshop.
  • We will provide all code required for the workshop via git

Documentation of the tools required as well as a setup script for Mac and Linux computers will be provided in advance. However, we will not likely be able to resolve errors related to local installations during the workshop, so if you don’t feel comfortable to resolve them yourself, please choose the option of VMs.



Date and Location

March 05 - 06, 2020,

The Queen Elizabeth II Conference Centre


Each attendee will receive a Certificate of Achievement.

Who are the learning paths for?

The AI and ML Learning Paths are for anyone currently working in professional software development from software engineers and software architects to software managers and leaders. Each Learning Path identifies the required skill level needed to take the course.

QCon London 2020

Features of all our AI and ML Learning Paths

In-depth sessions

2 days of in-depth, hands-on, practical sessions to help you understand the Machine Learning development process.

Get hands-on with the tools

Gain hands-on skills and use of tools to begin your own journey into building Machine Learning systems.

Learn from the people driving innovation in software development

Designed and delivered by software development professionals leading innovation in AI and Machine Learning.

Apply your learning

Pull all your learning together and apply what you’ve learned through practical assessments projects during the 2 days.

Connect with like-minded people

Meet other software developers, share experiences, and learn from each other.

Get up and running with practical skills you can use immediately

Leave the Learning Path with new skills, best practices and actionable techniques you can implement in your projects. Each attendee will receive a Certificate of Achievement.

Learn and grow as a team

Considering attending a Learning Path as a team? Participating in our AI and ML Learning Paths as a team supports your collaborative learning. Bounce ideas with each other, explore solutions to problems, engage with our facilitators, and discuss things with other attendees. You’ll leave with new ideas and things you want to try - all of which create a stronger team.

QCon London 2020
QCon London 2020

Venue and Hotel

The Queen Elizabeth II Conference Centre

The Learning Paths are co-located at QCon London at the The Queen Elizabeth II Conference Centre, Broad Sanctuary, Westminster.

Address: Queen Elizabeth II Conference Centre, Broad Sanctuary, Westminster, London SW1P 3EE

Tel: +44 20 7798 4000

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