For a limited time only, save up to 40% on these newly released MEAP books, liveVideos, and liveProjects!
Sale ends June 28!
Save 40% on Software Mistakes and Tradeoffs
In Software Mistakes and Tradeoffs you’ll learn from costly mistakes that Tomasz Lelek and Jon Skeet have encountered over their impressive careers. You’ll explore real-world scenarios where poor understanding of tradeoffs lead to major problems down the road, to help you make better design decisions.
Tomasz Lelek has years of experience working with various production services, architectures, and programming languages. Jon Skeet is a staff developer relations engineer at Google, currently working on the Google Cloud Client Libraries for .NET.
Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you’ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well.
Yuan Tang is a senior software engineer at Ant Group, where he works on AI infrastructure and AutoML platforms on Kubernetes.
Rust Web Development is a hands-on guide to building server-based web applications with Rust. If you’ve build web servers using Java, C#, or PHP, you’ll instantly fall in love with the performance and development experience Rust delivers. This book shows you how to work efficiently using pure Rust, along with important Rust libraries.
Bastian Gruber is a Solutions Architect at Twilio Inc.
Testing Web APIs teaches you to plan and implement the perfect testing strategy for your web APIs. In it, you’ll explore dozens of different testing activities to help you develop a custom testing regime for your projects. You’ll learn to take a risk-driven approach to API testing, and build a strategy that goes beyond the basics of code and requirements coverage.
Mark Winteringham is the DojoBoss at Ministry of Testing.
Grokking Continuous Delivery is a practical guide to implementing and using continuous delivery in your software projects. It’s full of tool-agnostic best practices that you can apply to any software project. You’ll get a complete overview of all the pieces of a CD pipeline and learn how to fit them together for both new and legacy codebases.
Christie Wilson is a software engineer at Google, with over a decade of experience dealing with complex deployment environments and high-criticality systems.
Semi-Supervised Deep Learning with GANs for Melanoma Detection
In this liveProject series, you’ll take on the role of a computer vision engineer creating a proof of concept for an image recognition mobile app—one with world-changing potential. You’ll build a machine learning model that can identify cancerous moles in low-resolution photos from a phone’s camera.
Ingrid Hrga is a Ph.D. student at the University of Rijeka, Croatia..
Deep Reinforcement Learning for Self-Driving Robots
In this liveProject, you’ll investigate reinforcement learning approaches that will allow autonomous robotic carts to navigate a warehouse floor without any bumps and crashes. Your challenges will include teaching warehouse navigation with tabular Q-learning, leveraging neural networks and Deep Q-Network models to avoid collisions, and putting together a simulation environment to handle custom reinforcement learning problems.
Hans Gunnoo is an AI engineer at Deloitte Digital. Byron Galbraith is the Chief Technology Officer and co-founder of Talla..
Putting machine learning into production can often be a complex task. The Kubeflow platform helps streamline this process with simple and scalable ML workflow deployment. In this liveProject, you’ll put Kubeflow into action to help your team roll out their new license plate recognition deep learning system.
Benjamin Tan is a Data Engineer working at EasyMile Ptd Ltd as part of the R&D team in Singapore.
Deep Neural Networks for Image Segmentation in Fine Art
In this liveProject, you’ll use your machine learning skills to create an index of the art. Your challenges will include classifying your training data, considering pretrained models to help aid categorization, implementing a customized CNN that can identify genre, and applying image semantic segmentation in order to facilitate the classification by identifying the elements present on the art painting.
Dr. David G. Stork is widely considered a pioneer in the application of rigorous computer vision, image analysis and artificial intelligence to problems in the history and interpretation of fine art. Max Dehaut is a tech lead for Codit Luxembourg (Proximus Group), overseeing all technical aspects within the local region of Codit.