KDnuggets

SEPTEMBER 13, 2019

While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.

Machine Learning 123

Machine Learning 123

Teradata

SEPTEMBER 11, 2019

AI & Deep Learning allow organizations to maximize player performance while minimizing player risk through better insights from performance and wellness data.

Deep Learning 98

Deep Learning Data 98

Netflix Tech

SEPTEMBER 10, 2019

Toby Mao , Sri Sri Perangur , Colin McFarland Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix. ABlaze: The standard view of analyses in the XP UI Suppose you’re running a new video encoding test and theorize that the two new encodes should reduce play delay, a metric describing how long it takes for a video to play after you press the s

Python 88

Python Raw Data SQL Datasets 88

Rockset

SEPTEMBER 13, 2019

What Is Grafana? Grafana is an open-source software platform for time series analytics and monitoring. You can connect Grafana to a large number of data sources, from PostgreSQL to Prometheus. Once your data source is connected, you can use a built-in query control or editor to fetch data, and build dashboards from your data source. Grafana is frequently deployed for a wide variety of use cases, including DevOps and AdTech.

PostgreSQL 52

PostgreSQL SQL Building Engineering 52

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

SEPTEMBER 12, 2019

It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.

IT 123

IT Machine Learning Data Science Data 123

Teradata

SEPTEMBER 9, 2019

There are a lot of misperceptions about Teradata. Learn more about what Teradata Vantage really is: a cloud-first integrated data and analytics platform.

Cloud 63

Cloud Data Analytics Data 63

Confluent

SEPTEMBER 13, 2019

Static Membership is an enhancement to the current rebalance protocol that aims to reduce the downtime caused by excessive and unnecessary rebalances for general Apache Kafka ® client implementations. This applies to Kafka consumers, Kafka Connect, and Kafka Streams. To get a better grasp on the rebalance protocol, we’ll examine this concept in depth and explain what it means.

Kafka 21

Kafka Cloud Metadata Process 21

KDnuggets

SEPTEMBER 11, 2019

As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

Machine Learning 123

Machine Learning Systems Python 123

Data Engineering Podcast

SEPTEMBER 9, 2019

Summary The first stage in every data project is collecting information and routing it to a storage system for later analysis. For operational data this typically means collecting log messages and system metrics. Often a different tool is used for each class of data, increasing the overall complexity and number of moving parts. The engineers at Timber.io decided to build a new tool in the form of Vector that allows for processing both of these data types in a single framework that is reliable an

Building 100

Building Kafka Media Data 100

KDnuggets

SEPTEMBER 10, 2019

In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.

Algorithm 123

Algorithm Python Data Data Science 123

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

SEPTEMBER 10, 2019

How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

Machine Learning 123

Machine Learning Python 123

KDnuggets

SEPTEMBER 12, 2019

There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

Data Science 123

Data Science Data Machine Learning 123

KDnuggets

SEPTEMBER 9, 2019

This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.

Python 123

Python Data Programming Data Science 123

KDnuggets

SEPTEMBER 9, 2019

In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

Machine Learning 122

Machine Learning Data 122

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

SEPTEMBER 9, 2019

BERT is changing the NLP landscape and making chatbots much smarter by enabling computers to better understand speech and respond intelligently in real-time.

122

122

KDnuggets

SEPTEMBER 9, 2019

In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

Deep Learning 122

Deep Learning Architecture 122

KDnuggets

SEPTEMBER 12, 2019

This article explains the Support Vector Machines (SVM) algorithm in an easy way.

Algorithm 120

Algorithm Machine Learning 120

KDnuggets

SEPTEMBER 13, 2019

I am a Git god, why do I need another version control system for Machine Learning Projects?

Machine Learning 120

Machine Learning Datasets Data Science Project 120

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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 Engineer

KDnuggets

SEPTEMBER 13, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and Sebastian Ruder. This post highlights key insights and takeaways and provides updates based on recent work.

119

119

KDnuggets

SEPTEMBER 11, 2019

Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

Machine Learning 118

Machine Learning 118

KDnuggets

SEPTEMBER 9, 2019

I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location.

Building 112

Building Data Machine Learning Python 112

KDnuggets

SEPTEMBER 9, 2019

Recently, Alphabet’s subsidiaries Waymo and DeepMind partnered to find a more efficient process to train self-driving vehicles algorithms and their work took them back to one of the cornerstones of our history as species: evolution.

Algorithm 100

Algorithm Process 100

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!

Data

KDnuggets

SEPTEMBER 12, 2019

It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier.

Machine Learning 96

Machine Learning Algorithm IT Python 96

KDnuggets

SEPTEMBER 11, 2019

Data Driven Government is coming to Washington, DC, Sep 26, and includes a stellar lineup of experts who will share the emerging trends and best practices of government agencies in the current use of data analytics to enhance mission outcomes. Use code KDNUGGETS to get 15% off.

Government 92

Government Data Analytics Data Coding 92

KDnuggets

SEPTEMBER 10, 2019

ODSC has developed a mini-bootcamp, designed to reduce the time and monetary costs of discovering which pathway into data science you should take. In this article, we’ll discuss seven reasons why ODSC’s Mini-Bootcamp might be right for you.

Data Science 88

Data Science Data Designing Education 88

KDnuggets

SEPTEMBER 12, 2019

Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, has announced that Clearsense, a scalable data platform as a service built for healthcare, has chosen the smart data discovery platform to automatically discover and catalog relationships across immense amounts of medical and clinical data.

Healthcare 81

Healthcare Medical Data 81

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

Software Engineering

KDnuggets

SEPTEMBER 10, 2019

Also: Top Handy SQL Features for Data Scientists; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs.

Machine Learning 77

Machine Learning SQL Data 77

KDnuggets

SEPTEMBER 11, 2019

How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML,

Python 64

Python Data Building 64

KDnuggets

SEPTEMBER 9, 2019

Also: Python Libraries for Interpretable Machine Learning; TensorFlow vs PyTorch vs Keras for NLP; Advice on building a machine learning career and reading research papers by Prof. Andrew Ng; Object-oriented programming for data scientists: Build your ML estimator.

Machine Learning 61

Machine Learning Python Programming Data 61

KDnuggets

SEPTEMBER 11, 2019

Python Libraries for Interpretable Machine Learning; How #AI will transform #healthcare (and can it fix US healthcare system?); Building Recommendation System - an overview ; I wasn't getting hired as a Data Scientist. So I sought data on who is.

Healthcare 59

Healthcare Python Machine Learning Systems 59

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