Easy to build Python Dashboards using Financial data APIs
Automated tests are a great way to iterate fast and ensure features didn't break. This talk discusses how to speed up your builds and dev cycle even more by running tests asynchronously using a pytest plugin called asyncio-cooperative.
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.
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 Naïve Bayes (NB).
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!
Building a modern web app requires so much: HTML, CSS, JS, Python, SQL, React, Bootstrap, Webpack, Django... What if we could build a better abstraction?
In this talk, I'll introduce Anvil, a full-stack Python environment where everything is a Python object, from your UI components to your database rows. I'll walk you through how and why we constructed this new approach to the web.
We'll start with a question: Why is web programming hard? It's because your data takes so many forms: database rows, Python objects, JSON on REST, JS objects, HTML DOM, and finally pixels. Most of a web developer's job is translating between these awkwardly different representations. Frameworks like Django help, but now you have a stack of leaky abstractions: web frameworks, ORMs, JS frameworks, CSS frameworks, build tools... These frameworks help you go faster, but they double the amount you need to know!
So I'll show our stab at an answer: A framework where everything is a Python object, requests to the server are function calls, and Python is a browser-side language. I'll talk about running Python in the browser. I'll talk about full-stack autocompletion. There will even be live coding.
As a developer, you are the first line when it comes to security for any products you may be building. There is often a misconception that all software security vulnerabilities are due to misconfigurations, using unmaintained open source libraries, using "insecure" languages, or by making dumb mistakes like hard-coding passwords. In actuality, it can be very easy to make potentially extreme security mistakes even only using built-in functions and libraries bundled with the latest version of Python. This talk will cover a number of these potential security mistakes that can be all too easy to make.
Dask is a parallel computing library for Python people. This talk will be a gentle introduction to Dask, showing how you can improve the speed of data science code on your laptop with a simple "pip install". Then we will use the same code to process big data on a cluster of machines. We will be going through an end-to-end data science pipeline, from ETL and exploratory analysis to machine learning model training and scoring.
We will cover:
- Example using publicly available data and single-node Python
- Pandas for data cleaning/transformation
- Scikit-learn for machine learning
- How to parallelize this workflow on a laptop and then a cluster using Dask
- Distributed model training
- Distributed inference/scoring
"DevOps" is the operations people’s crafty plan to make developers do other people's work, but we are smart enough to see right through this naive rebranding trick! Baruch suggests you think about it: we, the developers, have written all the code. It passes all the tests; it obviously works, and works well (Are we a little proud? We are!); so we are DONE. Now, out of the blue, a bunch of "thought leaders" (all with an operations background, mind you!) are trying to tell us that we have to learn YAML, Docker, Kubernetes, and Terraform to deploy our software because suddenly it is our concern?! In this talk, we'll discuss why developers do or don’t need DevOps. We'll consider arguments made by DevOps visionaries and see whether they hold water. Hopefully, by the end of the talk, we'll understand whether DevOps really helps developers to deploy better code to production more often, or if it is just another scam made up by marketing and evangelists.