GSoC is a awesome open source program for students to get some experience in open source development. It is an initiative by Google that helps organizations connect with students and get them started with their open-source projects. It is a great platform to explore new areas, maybe discover a new career path!
This project is an end-to-end security analytics system that turns raw production-style telemetry (auth logs, API gateway logs, and request traces) into clean data models, reliable security metrics, anomaly detection signals, and stakeholder-ready dashboards.
The goal is to simulate the kind of analytics engineering + security insights work that large consumer platforms need: measure ecosystem health, detect emerging threats (account takeover, brute force, abuse), and provide actionable, explainable signals for engineering and security teams.
For running flows, you need the compute capacity equivalent to or exceeding the requirements for running the models. Once you have the computer resources, follow the below steps.
Install Linux Screen on Ubuntu and Debian
We need this because RunAI is a wrapper around Kubernetes.
I am walking through all the steps required for implementing the operators in SOFIE with the help of an example of Leaky Relu Operator.
The Defination is available at ONNX Documentation here
This is the period of time between when accepted GSoC contributors are announced and the time they are expected to start coding. This time is an excellent one to introduce your GSoC contributors to the community, get them on the right mailing lists, introduce them to the codebase, discuss how they will work with their mentors on their timeline for the program, etc.
I had an unique experience during the community bonding period, i will share my experience and the kind of satisfaction i got after the completion of this period.
GANs are a relatively recent invention in field of ML. The primary objective of GANs was to generate new samples from the given dataset. And since the invention of GANs, they have grown to accomplish this task with better results and added features. Sketch to Color Image generation is an image-to-image translation model using Conditional Generative Adversarial Networks. In this project we learnt to create a Conditional GAN to predict colorful images from the given black and white sketch inputs without knowing the actual ground truth.
This section contains the technologies we used for this project.
