2021-08-12 18:00:00 location TBD
(50 Minutes)
By: Zax Rosenberg
Experience Level: Intermediate
Building machine learning (ML) models is faster and easier now than ever before. The proliferation of open-source libraries means data scientists can leverage cutting-edge pre-trained models in just a few lines of code. Yet it remains true that most ML models never make it to production. Why? Because making it to production (and staying in production) are about more than just model and code quality. In particular, this talk will discuss how MLOps can greatly accelerate and increase the chances of model success.
Specifically, the talk will walk through the full ML lifecycle and answer: What is MLOps? Why is it important? How can MLOps infrastructure be set up quickly, easily, and with open source tools? How can the system be designed in a user-friendly way, but without too much magic? How can user adoption be accelerated?
While its expected that data-science-related professionals will garner the most value from this talk, no prior MLOps/ML background is required to understand the contents of the talk.
(20 Minutes)
By: Manaswita Tyagi
Experience Level: Novice
In Today’s world, AI has become an essential tool for achieving and creating the unthinkable. It is helping in creating innovative solutions for almost every industry there is. In the wake of this ever-growing demand for computerized intelligence, what constitutes an active research domain is how AI-based intelligence can be interpreted and utilized by HR (Human Resources) from predictive analysis to automation. As the HR department is solely responsible for recruiting and bringing valuable talent to the industry, it becomes essential that this task is done with maximum efficiency. Through this project, we intend to predict which employee would prefer a job change and which employee would stay in a company and help assess the input resources required to put in an employee. This presentation will take you through the principles of using python, opinion mining, and various widely used classifiers, namely Random Forest (RF), Cat Boost Classifier, Support Vector Machine (SVM), and Naìˆve Bayes (NB).
Thu, Jul 15 2021 at 06:00 PM at Remote Project Night (chipy.town)
Thu, Jul 08 2021 at 06:00 PM at Remote Meeting
(30 Minutes)
By: Andrew Knight
Experience Level: Intermediate
Test data for automated tests can be a nightmare to manage. Data must be prepped in advance, loaded before testing, and cleaned up afterwards. Sometimes, teams don't have much control over the data in their systems under test—it's just dropped in, and it can change arbitrarily. Hard-coding values into tests that reference system tests can make the tests brittle, especially when running tests in different environments. In this talk, I'll teach strategies for managing each type of test data: test case variations, test control inputs, config metadata, and product state. We will cover how to "discover" test data instead of hard-coding it, how to pass inputs into automation (including secrets like passwords), and how to manage data in the system. After this talk, you will wake up from the nightmare and handle test data cleanly and efficiently like a pro!
(30 Minutes)
By: Calvin Hendryx-Parker
Experience Level: Novice
Slides Link