Data Engineering Podcast

AUGUST 5, 2019

Summary Data is only valuable if you use it for something, and the first step is knowing that it is available. As organizations grow and data sources proliferate it becomes difficult to keep track of everything, particularly for analysts and data scientists who are not involved with the collection and management of that information. Lyft has build the Amundsen platform to address the problem of data discovery and in this episode Tao Feng and Mark Grover explain how it works, why they built it, a

PostgreSQL 100

PostgreSQL MongoDB Metadata Media 100

KDnuggets

AUGUST 8, 2019

Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.

Machine Learning 123

Machine Learning Data 123

Uber Engineering

AUGUST 7, 2019

A common axiom among Uber engineers states that building new features is like fixing a car’s engine while driving it. As we scaled up to our present level of support for 14 million trips per day, the car in that … The post Migrating Functionality Between Large-scale Production Systems Seamlessly appeared first on Uber Engineering Blog.

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Systems Engineering Building IT 84

Teradata

AUGUST 4, 2019

Self-service analytics are increasingly being implemented by organizations that want to promote a data-driven culture. But how sustainable is it? Read more.

IT 16

IT Data 16

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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.

nodeSWAT

AUGUST 6, 2019

This post will walk you through the process of how to turn this awesome chat tool into a handy monitoring & alerting tool for your application. All this without any 3rd party modules and minimal code to keep the footprint small. Note: This post is using now outmoded integration method. Slack has introduced new ways to manage and send messages via Apps.

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Coding Accessible Accessibility Management 52

KDnuggets

AUGUST 6, 2019

In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.

Coding 115

Coding Python 115

Teradata

AUGUST 6, 2019

Digital transformation & regulatory requirements have long challenged Banks. Teradata has deep experience in ushering them through the transformation process.

Banking 15

Banking Process 15

KDnuggets

AUGUST 7, 2019

Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications.

Data Science 120

Data Science IT Data Analytics Data 120

KDnuggets

AUGUST 7, 2019

Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!

Deep Learning 122

Deep Learning Building 122

KDnuggets

AUGUST 7, 2019

There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.

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Coding Algorithm Machine Learning IT 107

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.

KDnuggets

AUGUST 6, 2019

Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.

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Python Machine Learning 114

KDnuggets

AUGUST 8, 2019

Simply put, data science is an attempt to understand given data using the scientific method. That's why data science is a scientific discipline. You are free (and encouraged!) to apply data science to business use cases, just as you are encouraged to apply it to many other domains.

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Data Science Process Data IT 105

KDnuggets

AUGUST 9, 2019

Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image.

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Algorithm Python 104

KDnuggets

AUGUST 7, 2019

In this tutorial, you’ll use Python and Pandas to explore a dataset and create visual distributions, identify and eliminate outliers, and uncover correlations between two datasets.

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Data Analysis Python Datasets Data 102

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

AUGUST 5, 2019

Over the past many months, I’ve received hundreds of messages from people asking me how they could get started with Data Science. Therefore, I thought it would be useful to write down a framework for those wanting to get started with Data Science.

Data Science 102

Data Science Data IT 102

KDnuggets

AUGUST 5, 2019

This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside.

Machine Learning 101

Machine Learning 101

KDnuggets

AUGUST 6, 2019

Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more.

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Aggregated Data Data Python 100

KDnuggets

AUGUST 7, 2019

Also: Ten more random useful things in R you may not know about; 5 Probability Distributions Every Data Scientist Should Know; Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment; Programmers rejoice! Deep TabNine offer code autocompletion with #deeplearning.

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Process IT Machine Learning Coding 95

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

AUGUST 8, 2019

The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems.

Systems 93

Systems 93

KDnuggets

AUGUST 9, 2019

Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.

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Deep Learning 91

KDnuggets

AUGUST 5, 2019

Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.

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Deep Learning Technology Data Machine Learning 88

KDnuggets

AUGUST 5, 2019

This workshop, Aug 14 @ 12 PM ET, will give you the proper tools and tactics to manage the entire lifecycle of your machine learning projects, from research to exploration to development and production.

Data Science 84

Data Science Management Machine Learning Project 84

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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

AUGUST 9, 2019

A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.

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Coding Python 95

KDnuggets

AUGUST 9, 2019

The position requires a passion for research, a proven research track record in computer vision, an ability to work independently as well as lead a team, and a willingness to work on inter-disciplinary research projects and seek external funding. The successful candidate will align with the group goal on building a world-class computer vision team.

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Project Building 51

KDnuggets

AUGUST 7, 2019

This week on KDnuggets: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree; How a simple mix of object-oriented programming can sharpen your deep learning prototype; Can we trust AutoML to go on full autopilot?; Ten more random useful things in R you may not know about; 25 Tricks for Pandas; and much more!

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Data Science Deep Learning Programming Data 49

KDnuggets

AUGUST 5, 2019

Also: GPU Accelerated Data Analytics & Machine Learning; Understanding Tensor Processing Units; Top 13 Skills To Become a Rockstar Data Scientist; Five Command Line Tools for Data Science; Ten more random useful things in R you may not know about.

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Data Science Machine Learning Data Analytics Data 47

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

KDnuggets

AUGUST 6, 2019

Waste Management is seeking a Data Scientist in Houston, TX, to support their digital marketing, customer and other business segment teams with insights gained from analyzing customer data.

Management 45

Management Data 45

Teradata

AUGUST 7, 2019

Teradata partners Microsoft & Informatica announced that they are taking on cloud analytic migrations. Find out what this means for our on-premises customers.

Cloud 15

Cloud 15

Confluent

AUGUST 8, 2019

We’re excited to announce Tutorials for Apache Kafka ® , a new area of our website for learning event streaming. Kafka Tutorials is a collection of common event streaming use cases, with each tutorial featuring an example scenario and several complete code solutions. It’s the fastest way to learn how to use Kafka with confidence. We’re building this because we know that event streaming is a radically different way of thinking.

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Kafka Data Warehouse Programming Coding 22