KDnuggets

OCTOBER 28, 2019

Learn how Bayes Theorem is in Machine Learning for classification and regression!

Machine Learning 123

Machine Learning 123

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 Kafka 100

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 46

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

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline

KDnuggets

OCTOBER 30, 2019

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

Data 122

Data 122

KDnuggets

OCTOBER 31, 2019

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

Python 122

Python Building Machine Learning Engineering 122

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.

Machine Learning 97

Machine Learning 97

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 IT Datasets 84

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.

Big Data 74

Big Data Data Analytics Data Science Data 74

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 74

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

Data

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.

Machine Learning 74

Machine Learning IT 74

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.

Machine Learning 72

Machine Learning Management Data 72

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 70

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 65

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

Systems

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.

Datasets 64

Datasets Data Data Preparation IT 64

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.

Data Management 61

Data Management Machine Learning Management Data 61

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.

Machine Learning 57

Machine Learning Coding Engineering Process 57

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.

Machine Learning 56

Machine Learning Datasets 56

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

Manufacturing

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.

Machine Learning 55

Machine Learning Data Science Data Process 55

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.

Data Science 53

Data Science Data Deep Learning Scala 53

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!

Data Analytics 53

Data Analytics Process Data Python 53

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.

Machine Learning 52

Machine Learning Project SQL Data Analysis 52

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With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you

Data Engineering

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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Data Science Technology Data 52

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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