Data Engineering Podcast

SEPTEMBER 30, 2019

Summary Building an end-to-end data pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Kedro is a framework that provides an opinionated workflow that lets you focus on the parts that matter, so that you don’t waste time on gluing the steps together. In this episode Tom Goldenberg explains how it works, how it is being used at Quantum Black for customer projects, and how it can help you structure your own.

Data Pipeline 100

Data Pipeline Machine Learning Media Data 100

KDnuggets

OCTOBER 2, 2019

Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.

Algorithm 121

Algorithm Datasets 121

Teradata

SEPTEMBER 29, 2019

The Teradata Certification program is celebrating its 20th anniversary! Find out how it can advance your career by making you a certified expert on Vantage.

Programming 66

Programming Certification IT 66

Confluent

SEPTEMBER 30, 2019

Day one of the event, summarized for your convenience. They say you never forget your first Kafka Summit. Mine was in New York City in 2017, and it had, what, 300 people? Today we welcomed nearly 2,000 to a giant ballroom in San Francisco. There were laser beams. There were two Tony Stark allusions in the first 60 seconds. And most importantly, there was a vibrant and ever-growing community present.

Kafka 17

Kafka Architecture Cloud Designing 17

Advertisement

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.

Database

Rockset

OCTOBER 1, 2019

Kubernetes at Rockset At Rockset, we use Kubernetes (k8s) for cluster orchestration. It runs all our production microservices — from our ingest workers to our query-serving tier. In addition to hosting all the production infrastructure, each engineer has their own Kubernetes namespace and dedicated resources that we use to locally deploy and test new versions of code and configuration.

SQL 40

SQL Systems Metadata Accessible 40

KDnuggets

OCTOBER 2, 2019

As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.

Data Preparation 121

Data Preparation Machine Learning IT Data 121

Confluent

OCTOBER 4, 2019

This tutorial describes how to set up an Apache Kafka ® cluster on Enterprise Pivotal Container Service (Enterprise PKS) using Confluent Operator , which allows you to deploy and run Confluent Platform at scale on virtually any Kubernetes platform, including Pivotal Container Service (PKS). With Enterprise PKS , you can deploy, scale, patch, and upgrade all the Kubernetes clusters in your system without downtime.

Kafka 16

Kafka Accessible Accessibility Engineering 16

KDnuggets

OCTOBER 1, 2019

Data science education in Europe has been reevaluated and new recommendations are leading the way to the next generation of data science Master's courses to better support and train students.

Data Science 119

Data Science Education Data 119

KDnuggets

SEPTEMBER 30, 2019

This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.

Healthcare 106

Healthcare Systems IT 106

KDnuggets

OCTOBER 4, 2019

This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.

Data Analysis 118

Data Analysis SQL Data IT 118

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.

Certification

KDnuggets

SEPTEMBER 30, 2019

Three new releases that will help researchers streamline the implementation of reinforcement learning programs.

Programming 121

Programming 121

KDnuggets

OCTOBER 4, 2019

Find out what was presented at the 6th annual Deep Learning Summit in London where industry leaders, academics, researchers, and innovative startups presenting the latest technological advancements and industry application methods in the field of deep learning.

Deep Learning 92

Deep Learning Technology 92

KDnuggets

OCTOBER 3, 2019

There is no clear outline on how to study Machine Learning/Deep Learning due to which many individuals apply all the possible algorithms that they have heard of and hope that one of implemented algorithms work for their problem in hand. Below, I've listed out some of the steps that one should adopt while solving a machine learning problem.

Machine Learning 90

Machine Learning Engineering Deep Learning Algorithm 90

KDnuggets

OCTOBER 1, 2019

There are three types of emotion AI, and their combinations. In this article, I’ll briefly go through these three types and the challenges of their real-life applications.

91

91

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

SEPTEMBER 30, 2019

This article will introduce the different type of data sets, data object and attributes.

Data 123

Data Datasets 123

KDnuggets

SEPTEMBER 30, 2019

In advance of the Data Science Salon taking place in Seattle on Oct 17, we asked our speakers to shed some light on how Artificial Intelligence and Machine Learning are impacting one of America’s most disruptive industries. Read for more insight, and then register with KDnuggets exclusive link for 20% off tickets.

Machine Learning 85

Machine Learning Data Science Retail Data 85

KDnuggets

OCTOBER 1, 2019

We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.

92

92

KDnuggets

OCTOBER 4, 2019

In this guide, we will explore a range of research papers that have sought to solve the challenging task of automating neural network design.

Architecture 84

Architecture Designing Machine Learning 84

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 2, 2019

The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.

Architecture 67

Architecture IT 67

KDnuggets

OCTOBER 3, 2019

While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.

Data 80

Data 80

KDnuggets

OCTOBER 3, 2019

You won’t want to miss the opportunity to learn about the future of deep learning first-hand at ODSC West in San Francisco, Oct 29 - Nov 1. So don’t forget to register soon for 20% off.

Deep Learning 59

Deep Learning 59

KDnuggets

OCTOBER 2, 2019

This week, find out what the future of analytics and data science holds; get an introduction to spaCy for natural language processing; find out how to use time series analysis for baseball; get to know your data; read 6 bits of advice for data scientists; and much, much more!

Data Science 56

Data Science Python Data Process 56

Advertisement

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.

Project

KDnuggets

OCTOBER 2, 2019

Also: Top KDnuggets tweets, Sep 18-24: Python Libraries for Interpretable Machine Learning; Scikit-Learn: A silver bullet for basic ML; Automatic Version Control for Data Scientists; My journey path from a Software Engineer to BI Specialist to a Data Scientist.

Python 53

Python BI Software Engineer Software Engineering 53

KDnuggets

OCTOBER 3, 2019

DeepMind researchers created a model to be able to replay past experiences in a way that simulate the mechanisms in the hippocampus.

Building 52

Building 52

KDnuggets

OCTOBER 2, 2019

This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.

Building 51

Building Education Data 51

Confluent

OCTOBER 3, 2019

We recently launched a new artificial intelligence (AI) data extraction API called Scrapinghub AutoExtract , which turns article and product pages into structured data. At Scrapinghub, we specialize in web data extraction , and our products empower everyone from programmers to CEOs to extract web data quickly and effectively. Example of article extraction on Introducing a Cloud-Native Experience for Apache Kafka ® in Confluent Cloud.

Kafka 15

Kafka Cloud Amazon Web Services Google Cloud 15

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

Engineering

Confluent

OCTOBER 1, 2019

If you looked at the Kafka Summits I’ve been a part of as a sequence of immutable events (and they are, unless you know something about time I don’t), it would look like this: New York City 2017, San Francisco 2017, London 2018, San Francisco 2018, New York City 2019, London 2019, San Francisco 2019. That makes this the seventh Summit I’ve attended.

Kafka 14

Kafka Database Cloud Systems 14

KDnuggets

SEPTEMBER 30, 2019

Also: 12 Deep Learning Researchers and Leaders; Natural Language in Python using spaCy: An Introduction; A Single Function to Streamline Image Classification with Keras; Which Data Science Skills are core and which are hot/emerging ones?; 6 bits of advice for Data Scientists.

Deep Learning 56

Deep Learning Data Science Python Data 56

Confluent

SEPTEMBER 30, 2019

Go from zero to production on Apache Kafka ® without talking to sales reps or building infrastructure. Apache Kafka is the standard for event-driven applications. But it’s not without its challenges, and the ops burden can be heavy. Organizations that successfully build and run their own Kafka environment must make significant investments in engineering and operations to account for failover and security.

Kafka 19

Kafka Cloud Amazon Web Services Google Cloud 19