Data Engineering Podcast

NOVEMBER 18, 2019

Summary With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. What’s worse is that any time you have to migrate to a new architecture, all of your analytical code has to change too.

Data Lake 100

Data Lake Scala Data Warehouse Hadoop 100

KDnuggets

NOVEMBER 22, 2019

If you are a new Data Scientists early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.

Data 108

Data 108

Confluent

NOVEMBER 20, 2019

Today marks a new release of KSQL, one so significant that we’re giving it a new name: ksqlDB. Like KSQL, ksqlDB remains freely available and community licensed, and you can […].

IT 111

IT Process Management 111

Uber Engineering

NOVEMBER 19, 2019

A restaurant’s menu is arguably its most important feature. When ordering online or via the app with Uber Eats, potential customers can’t peer in through a restaurant’s windows or smell the scents wafting from their kitchens, so digital menus become … The post Introducing Menu Maker: Uber Eats’ New Menu Management Tool appeared first on Uber Engineering Blog.

Management 84

Management Engineering IT Architecture 84

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

Management

Teradata

NOVEMBER 19, 2019

How do you ensure your Customer Data Platform is enabling breakthrough customer experience business outcomes, rather than hindering them? Find out more!

Data 84

Data 84

KDnuggets

NOVEMBER 22, 2019

To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.

Machine Learning 116

Machine Learning Project Python 116

Netflix Tech

NOVEMBER 22, 2019

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! Please stop by our “Living Room” for an opportunity to connect or reconnect with Netflixers. We’ve compiled our speaking events below so you know what we’ve been working on. We look forward to seeing you there!

AWS 40

AWS Entertainment Software Engineer Software Engineering 40

Teradata

NOVEMBER 17, 2019

Moving part of your analytic ecosystem to the cloud requires the inspection of all the ecosystem elements to make sure they perform well over a WAN. Read more.

Cloud 58

Cloud Architecture 58

KDnuggets

NOVEMBER 21, 2019

This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API.

Python 113

Python Process 113

Confluent

NOVEMBER 19, 2019

In the past 12 months, games and other forms of content made with the Unity platform were installed 33 billion times reaching 3 billion devices worldwide. Apart from our real-time […].

Cloud 84

Cloud AWS 84

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Data

Netflix Tech

NOVEMBER 22, 2019

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! Please stop by our “Living Room” for an opportunity to connect or reconnect with Netflixers. We’ve compiled our speaking events below so you know what we’ve been working on. We look forward to seeing you there!

AWS 40

AWS Entertainment Software Engineer Software Engineering 40

KDnuggets

NOVEMBER 22, 2019

We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.

Data 112

Data 112

KDnuggets

NOVEMBER 19, 2019

This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood.

IT 105

IT 105

KDnuggets

NOVEMBER 21, 2019

This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

Process 94

Process Deep Learning Data Coding 94

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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.

Database

KDnuggets

NOVEMBER 21, 2019

This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way.

Python 98

Python 98

KDnuggets

NOVEMBER 21, 2019

Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problem, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.

Building 85

Building Machine Learning Data 85

KDnuggets

NOVEMBER 18, 2019

When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.

Deep Learning 84

Deep Learning Process Data IT 84

KDnuggets

NOVEMBER 21, 2019

Also: Bring the scientific rigor of reproducibility to your Data Science projects; Neutrinos Lead to Unexpected Discovery in Basic Math ; The media gets really excited about AI. Maybe a bit too excited.

Data Science 77

Data Science Media Data Project 77

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 20, 2019

Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems.

Machine Learning 72

Machine Learning Data Preparation Data 72

KDnuggets

NOVEMBER 19, 2019

As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.

Data Science 69

Data Science Data Project Process 69

KDnuggets

NOVEMBER 19, 2019

In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.

Programming Language 90

Programming Language Data Science Programming Management 90

KDnuggets

NOVEMBER 21, 2019

Brush up on your Python basics with this post on creating, using, and manipulating tuples.

Python 92

Python Programming 92

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

NOVEMBER 20, 2019

In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.

Deep Learning 85

Deep Learning Data 85

KDnuggets

NOVEMBER 20, 2019

The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

Machine Learning 59

Machine Learning 59

KDnuggets

NOVEMBER 20, 2019

The fundamental fact is that more information than ever will need to be analyzed on millions of devices. And that’s where 5G will make accessing data dramatically faster and more efficient. At Samsung, we’re excited about what 5G can truly enable and to be a central player in the new 5G world.

Accessible 55

Accessible Accessibility Data 55

KDnuggets

NOVEMBER 18, 2019

Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.

Machine Learning 54

Machine Learning Project Python 54

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

Data Analysis

KDnuggets

NOVEMBER 19, 2019

KDnuggets is calling for original blogs and contributions from new authors on AI, Data Science, Machine Learning, and related topics. The authors of most popular such blogs in December will be profiled in KDnuggets.

Machine Learning 50

Machine Learning Data Science Data 50

KDnuggets

NOVEMBER 20, 2019

Read tips and tricks that helped one Data Scientist to get better at Machine Learning; Learn how to make ML project cost-effective; Consider submitting a blog to KDnuggets - you can be profiled here; and study how to manipulate Python lists.

Machine Learning 49

Machine Learning Project Python Data 49

KDnuggets

NOVEMBER 19, 2019

Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.

Deep Learning 47

Deep Learning Machine Learning Data 47

KDnuggets

NOVEMBER 18, 2019

Join this live webinar from cnvrg, Continual Learning with Human-in-the-loop, Nov 26 @ 12 PM EST, and learn the role of human-in-the-loop in your ML pipeline, how to close the loop in your pipeline, and much more.

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46

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