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This is an email from Open Data Science Newsletter, a newsletter by ODSCJournal.

Embedding Interactive Python Plots, Improving Your Model Performance with Auto-Encoders, and Jobs

Embedding Interactive Python Plots on the Web

Here’s a guide on how to use Plotly Chart Studio and Datapane to share Python plots on the web.

Improve Your Model Performance with Auto-Encoders

A correct feature engineering strategy tends to improve the performance of the model. In this article, we will discuss and implement feature extraction using autoencoders.

How to Start Your Next Data Engineering Project

Factoring in the right skills, tools, and platforms for the job, here are a few things to consider for your next data engineering job.

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Four Core Skills Needed for Machine Learning

Including mathematical and computer science knowledge, these are the four core machine learning skills you need to get started in the AI field.

Top 7 Most Essential Python Libraries For Beginners

To get started with Python for data science, these are a few popular Python libraries that you should know, including NumPy, Pandas, and others.

5 Essential Machine Learning Safety Topics For Better AI

Want to learn more about machine learning safety? Check out this rundown of a few free videos that you can watch on Ai+ Training.

How to Learn Data Science in the Shortest Amount of Time

From bootcamps to watching videos, there are a number of ways that you can learn data science quickly to catch up on in-demand skills.

An Introduction to Port Scans and Port Protection

One of the most basic forms of scouting out a company’s security defenses is through using port scans. How can you protect against them?

First Speakers Announced for ODSC West 2022

We’re happy to announce the first 50 ODSC West speakers! Learn more about them here and see why they’re game-changers in AI.

Volunteer for ODSC West 2022

Our Volunteer program is a great way to get involved in ODSC — one of the world’s largest conferences and communities of artificial intelligence and data science experts. Get a chance to collaborate with our community/media partner team and support us with the organization of ODSC 2022!

Video of the Week: Ella Hilal, PhD on How to Effectively Scale ML & AI in Any Organization

In this talk, Dr. Ella Hilal speaks about how you can bring the power of AI into any organization, walking through the key principles for effectively scaling machine learning and artificial intelligence. She covers a variety of principles from creating a strong internal data culture, to incorporating machine learning models as a seamless part of the product experience, to why complex models are not always the best solution (at first).

Upcoming Webinars:

Laia Subirats — Intelligent Tutoring Systems in higher education

Tue, Jul 5, 2022 12:00 PM — 1:00 PM EDT

Data science can be used in higher education both to forecast the final grades of the students considering continuous evaluations and to help students to perform their tasks when the teacher is not with them. These two use cases can help both students and teachers to improve the learning experience.

Lightning Interview: “Interpretable Machine Learning”

Fri, Jul 8, 2022 1:00 PM EDT

You’re going to want to mark your calendars! On July 8th, we will interview ODSC West speaker Serg Masis — Climate & Agronomic Data Scientist at Syngenta. He will lead an engaging conversation on the challenges of machine learning interpretability. Listen in and ask questions, live!

Experiment Management is just the beginning: A no-nonsense guide to upgrading your data science workflow

Wed, Jul 20, 2022 12:00 PM — 1:00 PM EDT

This webinar will cover a use case (ML model training) from beginning to end in the most efficient way possible, which is what MLOps is all about. It’s using open-source tooling, so you can already start: https://github.com/allegroai/clearml

Jonathan Cosme — CPUs vs GPUs for Your End-to-End Data Science Workflows

Thu, Jul 28, 2022 12:00 PM — 1:00 PM EDT

We know that model training and inference are faster on GPU, but the slowest, most draining part of a data scientist’s typical day is processing data into the structure the model requires. Can GPUs help us with this challenge as well? To answer this question, we’ll compare cycles per second and costs of CPU vs GPU, look at speed gains with the Rapids.ai framework, and calculate ROI as our AI/ML models scale. Join us as we lay out the compelling case for why you should be using GPUs for your end-to-end data science workflows, including ETL jobs.



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