Eyan Yeung

In this series of liveProjects, you’ll build a complete message notification system from scratch using Spring Boot and a microservices architecture. Stepping into the role of a software engineer working for a bank, you’ll design and implement a solution to handle the high-volume traffic of the bank’s notification management platform. You’ll get hands-on experience building microservices around event handling concepts and implementing service resiliency patterns. Complete every project in this series, and you’ll be ready to start creating loosely coupled and highly cohesive microservices.

In this series of liveProjects, you’ll use data science and natural language processing techniques to perform the kind of real-world work routinely conducted by data scientists in the marketing sector. You’ll build an effective solution that can scrape, analyze, and monitor chatter on a Reddit forum to determine the opinions of your company’s customers. Each project in this series can stand alone or be worked through together, as you go hands on with data collection, data exploration, utilizing transfer learning, and building effective data dashboards.

In this liveProject, you’ll build an interactive dashboard that will allow the marketing team at your company to monitor any mention of your company’s products on Reddit. You’ll start by visualizing relevant subreddit data, then build a model to monitor your mentions on Reddit. Finally, you’ll deploy the Streamlit dashboard on Heroku. Streamlit offers a simple and easy way to build a highly interactive and beautiful dashboard with just a few lines of codes, whilst Heroku offers free web hosting for data apps.

In this liveProject, you’ll use transformer-based deep learning models to predict the tag of Reddit subreddits to help your company understand what its customers are saying about them. Transformers are the state of the art, large-scale deep language models pretrained on a huge corpus of text, and are capable of understanding the complexity of grammar really well. You’ll train this model on your own data set, and tune its hyperparameters for the best results.

In this liveProject, you’ll clean and analyze data scraped from Reddit to determine customer opinions of your products within a set time period. You’ll utilize common natural language processing techniques such as stemming, tokenization, and latent dirichlet allocation (LDA) to discover patterns in people’s opinions, and then visualize your results and summarize your findings.

In this liveProject, you’ll harvest customer opinions about your company’s products from the comments left on the subreddit for your company, and store them in a database for future analysis. You’ll connect to the Reddit API, identify and clean up the data fields you need, and store the data into MongoDB.

In this liveProject, you’ll use powerful libraries like Spring Cloud Library Sleuth, Zipkin, and Actuator to help find and debug an error in the microservices of a notification management platform. The distributed nature of microservices makes them challenging to debug, and finding where a problem occurs can be maddening. You’ll set up monitoring services that can use correlation IDs to link together transactions across multiple services, aggregate log data from various services into a single searchable source, and visualize the flow of a user transaction across multiple services.

In this liveProject, you’ll leverage the Spring Cloud Resiliency libraries to improve the fault tolerance of the microservices that make up a notification management platform. You’ll work to ensure that each microservice can easily recover from failure, avoiding cascading failures and guaranteeing that other microservices in the system will be unaffected by any individual failure.

In this liveProject, you’ll set up and configure a Spring Cloud Gateway to act as an entry point for applications to connect to a notification management platform. Spring Cloud Gateway provides a flexible way of routing requests based on criteria, and focuses on cross-cutting concerns such as security, resiliency, and monitoring. It’s the perfect API gateway to implement complexity separately from the client.

In this liveProject, you’ll implement Service Discovery and Registry Modules so that the services of a bank’s notification management platform can dynamically register and be available for other services to discover at runtime. You’ll make use of the Spring Cloud Registry to register your services before you configure healthy endpoints and set up discovery.

In this liveProject, you’ll build the foundational notification services for a bank’s new notification management platform. Rather than build one single monolithic service, you’ll establish four separate microservices to handle notification preferences, notification formatting, the notification gateway that dispatches emails or SMS messages, and the application that orchestrates all of these functions. You’ll leverage Spring Boot and other Java services to help you create these microservices, which will form the bedrock of an entire notification platform.