Sat.Nov 16, 2019 - Fri.Nov 22, 2019

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Escaping Analysis Paralysis For Your Data Platform With Data Virtualization

Data Engineering Podcast

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
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Advice for New and Junior Data Scientists

KDnuggets

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 111
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Introducing ksqlDB

Confluent

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
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Introducing Menu Maker: Uber Eats’ New Menu Management Tool

Uber Engineering

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.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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Customer Data Platforms: Silo Killer or Yet Another Silo?

Teradata

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

Data 84
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Automated Machine Learning Project Implementation Complexities

KDnuggets

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.

More Trending

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Netflix at AWS re:Invent 2019

Netflix Tech

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
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Is There a Geographic Component in Your Analytic Cloud Architecture?

Teradata

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
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Geocoding Automation: Free and Paid with Python, Selenium and Google

KDnuggets

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 119
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Using Confluent Platform to Complete a Massive Cloud Provider Migration and Handle Half a Million Events Per Second

Confluent

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 87
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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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Netflix at AWS re:Invent 2019

Netflix Tech

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
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Three Methods of Data Pre-Processing for Text Classification

KDnuggets

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
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The Math Behind Bayes

KDnuggets

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
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Text Encoding: A Review

KDnuggets

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

Data 111
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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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The Notebook Anti-Pattern

KDnuggets

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 107
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Neural Networks 201: All About Autoencoders

KDnuggets

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.

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Generalization in Neural Networks

KDnuggets

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.

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Top KDnuggets tweets, Nov 13-19: A whole lot of Data Science Cheatsheets

KDnuggets

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.

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How to Drive Cost Savings, Efficiency Gains, and Sustainability Wins with MES

Speaker: Nikhil Joshi, Founder & President of Snic Solutions

Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.

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Pro Tips: How to deal with Class Imbalance and Missing Labels

KDnuggets

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.

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Reproducibility, Replicability, and Data Science

KDnuggets

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.

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Data Science for Managers: Programming Languages

KDnuggets

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

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Deep Learning for Image Classification with Less Data

KDnuggets

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

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The Cloud Development Environment Adoption Report

Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

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Python Tuples and Tuple Methods

KDnuggets

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

Python 92
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

KDnuggets

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.

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GitHub Repo Raider and the Automation of Machine Learning

KDnuggets

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.

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The Semiconductor Imperative for Driving Meaningful Innovation

KDnuggets

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.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

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Why write for KDnuggets? Calling for original blogs and new authors

KDnuggets

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.

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KDnuggets™ News 19:n44, Nov 20: How I Got Better at Machine Learning; Tips for a cost-effective ML project

KDnuggets

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.

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How to apply machine learning and deep learning methods to audio analysis

KDnuggets

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.

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Live Webinar: Continual Learning with Human-in-the-loop

KDnuggets

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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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.