Data Engineering Podcast

OCTOBER 28, 2019

Summary Despite the fact that businesses have relied on useful and accurate data to succeed for decades now, the state of the art for obtaining and maintaining that information still leaves much to be desired. In an effort to create a better abstraction for building data applications Nick Schrock created Dagster. In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and

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Building Data Pipeline Programming Language Metadata 100

KDnuggets

OCTOBER 30, 2019

Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.

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Data 118

Confluent

OCTOBER 31, 2019

The relationship between Apache Kafka® and machine learning (ML) is an interesting one that I’ve written about quite a bit in How to Build and Deploy Scalable Machine Learning in […].

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Machine Learning Kafka Building Process 49

Teradata

OCTOBER 27, 2019

At Teradata Universe, we held a roundtable on Next-gen Concepts for Player Performance and Wellness. Learn how insights using AI are readily available for the next-gen of high performers.

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

KDnuggets

OCTOBER 31, 2019

A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

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Python Building Machine Learning Engineering 116

KDnuggets

NOVEMBER 1, 2019

As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

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Machine Learning 93

KDnuggets

NOVEMBER 1, 2019

This article focused on building an Artificial Neural Network using the Numpy Python library.

Building 101

Building Python 101

KDnuggets

OCTOBER 28, 2019

Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

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Data Collection Data Datasets IT 71

KDnuggets

OCTOBER 29, 2019

Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

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Machine Learning IT 61

KDnuggets

NOVEMBER 1, 2019

This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

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Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

KDnuggets

OCTOBER 30, 2019

The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

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Deep Learning 60

KDnuggets

OCTOBER 31, 2019

Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value your Big Data products.

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Big Data Data Analytics Data Science Data 64

KDnuggets

OCTOBER 31, 2019

AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.

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Datasets Data Data Preparation IT 55

KDnuggets

OCTOBER 28, 2019

Also: Introduction to Natural Language Processing (NLP); Anomaly Detection, A Key Task for AI and Machine Learning, Explained; How to Become a (Good) Data Scientist — Beginner Guide.

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Data Management Machine Learning Management Data 52

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science

KDnuggets

NOVEMBER 1, 2019

Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.

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Machine Learning Coding Engineering Process 50

KDnuggets

OCTOBER 28, 2019

Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.

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Machine Learning Datasets 50

KDnuggets

OCTOBER 30, 2019

While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.

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KDnuggets

OCTOBER 30, 2019

This week in KDnuggets: Feature Selection: Beyond feature importance?; Time Series Analysis: A Simple Example with KNIME and Spark; 5 Advanced Features of Pandas and How to Use Them; How to Measure Foot Traffic Using Data Analytics; Introduction to Natural Language Processing (NLP); and much, much more!

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Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

Building

KDnuggets

OCTOBER 30, 2019

Also: Highest paid positions in 2019 are DevOps, Data Scientist, Data Engineer (all over $100K) - Stack Overflow Salary Calculator, Updated; A neural net solves the three-body problem 100 million times faster; The Last SQL Guide for Data Analysis You’ll Ever Need; How YouTube is Recommending Your Next Video.

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KDnuggets

OCTOBER 28, 2019

DataTech is a one-day conference on 16 Mar 2020, at the Technology and Innovation Centre in Glasgow, focusing on key topics in data science, and welcoming members of industry, academia, and the public sector alike. DataTech provides a forum for these different communities to meet, share knowledge and expertise, and forge new collaborations. We are currently welcoming workshop, talk and poster proposals for the DataTech20 conference.

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KDnuggets

OCTOBER 31, 2019

These results will go into each each region and employment type to find out the differences and similarities especially between people from Industry and Students.

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KDnuggets

OCTOBER 29, 2019

Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI.

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IT 61

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Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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KDnuggets

OCTOBER 29, 2019

In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

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Machine Learning Coding Software Engineer Software Engineering 55

Teradata

OCTOBER 29, 2019

Teradata CEO Oliver Ratzesberger discusses the company's new strategic partnerships with Deutsche Telekom and Google Cloud. Read more!

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Google Cloud Cloud 8