Data Engineering Podcast

JULY 22, 2019

Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access. In this episode Zhamak Dehghani shares an alternative approach in the form of a data mesh.

Data Lake 100

Data Lake Hadoop Data Architecture 100

KDnuggets

JULY 26, 2019

Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.

Python 123

Python Architecture 123

Teradata

JULY 23, 2019

Large enterprises are investing heavily in cloud-based analytics technologies. What qualities should they be looking for in these cloud vendors? Find out more.

Cloud 69

Cloud IT Technology 69

Confluent

JULY 24, 2019

Using Jaeger tracing, I’ve been able to answer an important question that nearly every Apache Kafka ® project that I’ve worked on posed: how is data flowing through my distributed system? Quick disclaimer: if you’re simply looking for an answer to that question, this post won’t provide that answer directly. Instead, in this post I will point you to an earlier blog post where I already answered that question and then I will focus on what should be your next question: now that I’m relying on Jaege

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Kafka Systems Bytes Project 54

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

Rockset

JULY 25, 2019

Introduction to Operational Analytics Operational analytics is a very specific term for a type of analytics which focuses on improving existing operations. This type of analytics, like others, involves the use of various data mining and data aggregation tools to get more transparent information for business planning. The main characteristic that distinguishes operational analytics from other types of analytics is that it is “analytics on the fly," which means that signals emanating from the vari

Software Engineer 40

Software Engineer Software Engineering Engineering PostgreSQL 40

KDnuggets

JULY 24, 2019

Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.

Deep Learning 122

Deep Learning 122

Confluent

JULY 23, 2019

The Kafka Summit Program Committee recently published the schedule for the San Francisco event, and there’s quite a bit to look forward to. For starters, it is a two-day event, which means we get to attend 14 talks, miss out on 42 talks (that we’ll later watch on video), and spend two days hanging out with our favorite community friends. While the keynotes have not been announced yet (they will be soon!

Kafka 18

Kafka Hadoop Media Software Engineer 18

KDnuggets

JULY 25, 2019

As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.

SQL 116

SQL Data Science Data Relational Database 116

KDnuggets

JULY 26, 2019

Education, coding, SQL, big data platforms, storytelling and more. These are the 13 skills you need to master to become a rockstar data scientist.

Education 123

Education Big Data SQL Data 123

KDnuggets

JULY 26, 2019

This article takes a closer look at the four fantastic things we should keep in mind when approaching every new data science project.

Data Science 122

Data Science Project Data Data Preparation 122

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

JULY 25, 2019

Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.

Certification 123

Certification Machine Learning Data Science Data 123

KDnuggets

JULY 25, 2019

Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.

Deep Learning 93

Deep Learning IT Data 93

KDnuggets

JULY 25, 2019

ODSC focuses on research at its conferences and invites the experts pushing the boundaries of AI to speak. Between the two upcoming conferences, researchers from more than 20 of the top research institutes in the country (Open AI, NASA’s JPL, Google, MIT CSAIL, BAIR, The Turing Institute, and Max Planck and more) will deliver talks and lead trainings at ODSC West 2019.

IT 54

IT 54

KDnuggets

JULY 24, 2019

Developers are always searching for answers to questions about their code. But how do they ask the right questions? Facebook is creating new NLP neural networks to help search code repositories that may advance information retrieval algorithms.

Coding 51

Coding Algorithm Programming Process 51

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

JULY 24, 2019

Also: Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; The Hundred-Page Machine Learning Book Book Review; The Evolution of a ggplot; Notes on Feature Preprocessing: The What, the Why, and the How.

Machine Learning 51

Machine Learning Coding Data Science Python 51

KDnuggets

JULY 24, 2019

Learn how to incorporate security into your practices without slowing down your project. Read this ActiveState blog post to learn more.

Data Science 57

Data Science Project Data 57

KDnuggets

JULY 25, 2019

Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?

Machine Learning 47

Machine Learning Data Project IT 47

KDnuggets

JULY 25, 2019

Responsible for operational leadership and management of the Master of Science in Business Analytics programs. Serves as a thought partner with the program Associate Dean to develop and execute program strategy.

Programming 46

Programming Management 46

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