End to end machine learning on Google Cloud Platform

In this workshop, we will cover starting, experimenting, serving and scaling a machine learning solution. We will take a representative problem and cover the following phases of machine learning:
Data Analysis/Inspection: We will perform data analysis (cover few key strategies of data analysis prior to machine learning)
Feature engineering: We will uncover a few techniques of feature engineering, learning of the previous step and approaches for feature engineering that will help you improve data quality prior to kicking off machine learning.
Representative model selection: In this phase we will summarize model selection criteria and select a couple of models suitable to our problem.
Training, Serving and Scaling in Cloud: In this phase we will uncover model evaluation metrics, understand serving skew and ability to scale both training and serving of Machine learning solutions.

Generate thousands of invoices in mere seconds

This workshop aims to show what approaches we’re taking when designing operation and business-critical systems. We will kick off with a mini-project that applies to scenarios like generating and delivering bank statements, invoices, advanced payment notifications, tickets, and so on.

The main problem that we will try to address here is the scalability and reliability of such a solution while keeping an eye on how we can keep our code clean and modular. We will be using NodeJS together with Serverless framework on top of AWS’s Lambda serverless compute service and a few other services from their portfolio.

We will end talking about some of the advantages of going serverless and reviewing the implications of adopting such solutions from a developer, DevOps, and the business’s perspective.

Hands on Enterprise Blockchain

A significant percentage of the IT infrastructures that act as a foundation for businesses, governmental institutions, and organizations, including key enterprise sectors are composed of outdated legacy systems that are unable to cope with the demands and requirements of the fast-paced digital society we live in. As a result, the discrepancies generated by the inability to provide an adequate response to market demands raises a series of red flags that take the shape of multiple operational bottlenecks and friction points which ultimately translate to a decrease in customer satisfaction, financial losses, as well as a predisposition to fall victim to the ever-evolving cybercrime spectrum.

With the ability to provide a tamper-resistant ecosystem for company data, as well as unparalleled levels of traceability of the data introduced in the system, blockchain seems more than suited to rejuvenate legacy IT infrastructures, giving companies a competitive edge and access to a set of valuable characteristics: data immutability, integrity, data traceability, distribution, and decentralization.

During the workshop “Hands on Enterprise Blockchain”, Modex Software Tech Lead Dan Popescu and Loredana Bourceanu will showcase how companies can make a transition to a blockchain infrastructure without needing to lock a significant pool of funds and resources into building a new infrastructure from the ground up. Dan and Loredana will make a hands-on practical demonstration on how the innovative Modex Blockchain Database solution intervenes in the development stack by positioning itself between the database and the existing application server to grant access to a blockchain backend.

Automated Bug Finding with Fuzzing

Everest Munro-Zeisberger is the CTO and co-founder of Fuzzbuzz, a cybersecurity startup based in SF that builds fuzzing tools and infrastructure to help developers effortlessly find severe bugs and vulnerabilities in their code. Prior to starting Fuzzbuzz, he built security tooling on Google’s Clusterfuzz team, one of the largest fuzzing operations in the world, before going on to build a distributed fuzzing framework at Coinbase.

In this workshop, we’ll cover the basics of Fuzzing, the powerful automated software testing tool used by security professionals, researchers, and hackers to find severe bugs and vulnerabilities in mission-critical software that have eluded manual testing.

We will learn about the most effective modern fuzzing techniques, and why security teams at companies like Google and Microsoft trust fuzzing to detect the severe vulnerabilities that developers miss. To date, fuzzing has found over 20 thousand bugs in Google Chrome, and has been used to detect 100s of bugs in Windows prior to release.

We’ll go hands-on and learn how fuzzing could have caught Heartbleed, the bug that cost the internet half a billion dollars to fix. Finally, we’ll apply what we learned to find a vulnerability in a web application, going through the four important steps of fuzzing: Attack Surface Analysis, Harness Building, Instrumentation, and Scaling.

Digital Health Automation Testing in a Nutshell

Healthcare is known for being a heavily regulated domain when it comes to product development and software is no exception. Therefore, developing solutions implies paying special attention to quality. Teams spend quite some effort designing processes to meet market-specific standards and then they spend even more following them. Not so fun, you might think, right? Well, having an adequate tool belt for the job will surely spice up your engineering experience a bit. When it comes to managing your test base there are plenty of cool options on the market, both for manual and automation testing.

In this workshop, we’ll be discussing some of the testing tools we opt for when putting together the tech-stack for a project. To automate or not to automate? “Yes!” is our answer. Cypress and Postman are great buddies that make us so confident and quick to respond. Join us in trying them out! We’ll be using our Macadamian HealthQ screening solution to quickly set up a demo, all over the stack. It’s easy and you’ll like it!