Learning Path: [SOLD OUT] Introduction to AI/ML for Software Engineers
Introduction to AI/ML for Software Engineers" is a fast-paced learning path on machine learning from a software professional’s point of view. The class is designed with the goal of providing students with a hands-on introduction to machine learning concepts and systems, as well as giving them the practical skills to walk away with the foundational skills to embark on ML projects in a professional setting.
Over the course of two days, attendees will be put through several hands-on exercises that stimulate learning through writing and executing code, instead of passive lectures. Students will get first-hand experience at cleaning data, implementing machine learning programs, and solving real problems in tuning, deploying, scaling, and maintaining machine learning systems.
Each attendee will be provided with a comprehensive virtual machine programming environment that is preconfigured for the tasks in the learning path, as well as any future machine learning experimentation and development that they will do. This environment consists of all of the most essential machine learning libraries and programming environments friendly to even novices at machine learning. As a capstone at the end of the session, students have a chance to formulate and embark on implementing a real machine learning system, from data collection to deployment.
Day 1 Topics
- Introduction to machine learning
- Hands-on guided exploration of Python machine learning libraries:
- Data-wrangling using Numpy and Pandas
- Scikit-learn’s functions and capabilities
- Data visualization using Matplotlib/Seaborn
- Walkthrough of the most commonly used machine learning algorithms (with quick hands-on examples/visualizations for select algorithms)
- Supervised learning algorithms
- Linear/logistic regression
- Support Vector Machines
- Decision trees/Random forests
- Unsupervised learning algorithms
- Clustering
- Semi-supervised learning
- Two-hour example: Building (and bypassing) an email spam filter with scikit-learn
- Loading data efficiently
- Using a labelled email/spam corpus training and test set, extract salient features to build a word model of spam
- Model tuning, cross-validation, and evaluation process
- With complete knowledge of the system, manually craft a piece of spam to bypass the filter
- Principles behind selecting the best machine learning models for different use-cases
- Solving practical problems in real-world machine learning deployments
- How to explain the predictions made by your model (using LIME)
- How to approach the problem of class imbalance (using imbalanced-learn)
- How to approach model/result evaluation in an unbiased way
- How to efficiently approach model parameter tuning (grid search etc.)
Day 2 Topics
- Deep learning
- Using Keras/TensorFlow for anomaly detection with convolutional neural networks
- Choosing the appropriate model for implementing different types of problems: efficacy comparison of different machine learning techniques for solving the anomaly detection problem, and what other considerations to have
- Two-hour example: Building a simple network intrusion detection system with two different machine learning models
- Importance of understanding the data and the threat model before designing a solution for the problem
- Model tuning, cross-validation, and evaluation process
- Guided comparisons of the performance characteristics for each implementation
- Visualizing and presenting the data for ease of analysis by security operation professionals.
- Streaming pipelines for machine learning using Apache Spark MLlib (PySpark)
- Overview of Apache Spark
- General architecture
- Distributed, scalable machine learning deployments with Spark
- Guided example of a streaming architecture for network anomaly detection using reinforcement learning on Spark
- Evaluating machine learning systems
- Techniques in bias detection, performance/efficacy measurement, and error analysis
- Evaluation of learning system architecture in adversarial scenarios
- Capstone project (in teams of 1-3)
- Formulate a real machine learning system
- Design a strategy for data collection, feature engineering, model selection, deployment, scaling, maintenance, and version control.
Practical Elements
All of the modules from 3-10 (described above in course outline) are practical elements.
Last Year's Tracks
Monday, 16 November
-
Architecting for Confidence: Building Resilient Systems
Your system will fail. Build systems with the confidence to know when they do and you won’t.
-
Remotely Productive: Remote Teams & Software
More and more companies are moving to remote work. How do you build, work on, and lead teams remotely?
-
Operating Microservices
Building and operating distributed systems is hard, and microservices are no different. Learn strategies for not just building a service but operating them at scale.
-
Distributed Systems for Developers
Computer science in practice. An applied track that fuses together the human side of computer science with the technical choices that are made along the way
-
The Future of APIs
Web-based API continue to evolve. The track provides the what, how, and why of future APIs, including GraphQL, Backend for Frontend, gRPC, & ReST
-
Resurgence of Functional Programming
What was once a paradigm shift in how we thought of programming languages is now main stream in nearly all modern languages. Hear how software shops are infusing concepts like pure functions and immutablity into their architectures and design choices.
Tuesday, 17 November
-
Social Responsibility: Implications of Building Modern Software
Software has an ever increasing impact on individuals and society. Understanding these implications helps build software that works for all users
-
Non-Technical Skills for Technical Folks
To be an effective engineer, requires more than great coding skills. Learn the subtle arts of the tech lead, including empathy, communication, and organization.
-
Clientside: From WASM to Browser Applications
Dive into some of the technologies that can be leveraged to ultimately deliver a more impactful interaction between the user and client.
-
Languages of Infra
More than just Infrastructure as a Service, today we have libraries, languages, and platforms that help us define our infra. Languages of Infra explore languages and libraries being used today to build modern cloud native architectures.
-
Mechanical Sympathy: The Software/Hardware Divide
Understanding the Hardware Makes You a Better Developer
-
Paths to Production: Deployment Pipelines as a Competitive Advantage
Deployment pipelines allow us to push to production at ever increasing volume. Paths to production looks at how some of software's most well known shops continuous deliver code.
Wednesday, 18 November
-
Java, The Platform
Mobile, Micro, Modular: The platform continues to evolve and change. Discover how the platform continues to drive us forward.
-
Security for Engineers
How to build secure, yet usable, systems from the engineer's perspective.
-
Modern Data Engineering
The innovations necessary to build towards a fully automated decentralized data warehouse.
-
Machine Learning for the Software Engineer
AI and machine learning are more approachable than ever. Discover how ML, deep learning, and other modern approaches are being used in practice by Software Engineers.
-
Inclusion & Diversity in Tech
The road map to an inclusive and diverse tech organization. *Diversity & Inclusion defined as the inclusion of all individuals in an within tech, regardless of gender, religion, ethnicity, race, age, sexual orientation, and physical or mental fitness.
-
Architectures You've Always Wondered About
How do they do it? In QCon's marquee Architectures track, we learn what it takes to operate at large scale from well-known names in our industry. You will take away hard-earned architectural lessons on scalability, reliability, throughput, and performance.