Data Engineering Podcast

JULY 28, 2019

Summary The ETL pattern that has become commonplace for integrating data from multiple sources has proven useful, but complex to maintain. For a small number of sources it is a tractable problem, but as the overall complexity of the data ecosystem continues to expand it may be time to identify new ways to tame the deluge of information. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration.

Data Integration 100

Data Integration Metadata Architecture Media 100

KDnuggets

JULY 29, 2019

Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

Machine Learning 121

Machine Learning Data Science Data 121

Netflix Tech

JULY 30, 2019

v2.0 and beyond By Anoop Panicker and Kishore Banala Conductor is a workflow orchestration engine developed and open-sourced by Netflix. If you’re new to Conductor, this earlier blogpost and the documentation should help you get started and acclimatized to Conductor. Netflix Conductor: A microservices orchestrator In the last two years since inception, Conductor has seen wide adoption and is instrumental in running numerous core workflows at Netflix.

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Metadata Media AWS Transportation 76

Teradata

JULY 28, 2019

In a cost-cutting culture, directly linking data projects to top business initiatives is a good way to keep them from getting clipped. Learn more.

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Data Project 73

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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have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth.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.

Cloud

Pandora Engineering

JULY 29, 2019

Credit: Kanok Sulaiman Disclaimer: These are my experiences from being a Pandora software developer intern in the summer of 2019. All opinions expressed are my own, and represent no one except myself. I recently spent the last summer of my undergraduate program as an intern for Pandora Media in Oakland, CA. I gained a lot from my experience, and I’m writing this post to detail the application process, the lessons that I learned, and the company culture.

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Java Recruitment Algorithm Computer Science 52

KDnuggets

JULY 29, 2019

At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.

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Datasets Data 118

Teradata

JULY 30, 2019

It's not enough just to drive personalization in your marketing efforts, you need to take a multi-dimensional approach. Find out why it's worth the investment.

IT 49

IT 49

Confluent

AUGUST 1, 2019

The Kafka Streams API boasts a number of capabilities that make it well suited for maintaining the global state of a distributed system. At Imperva, we took advantage of Kafka Streams to build shared state microservices that serve as fault-tolerant, highly available single sources of truth about the state of objects in our system. Why we chose Kafka Streams.

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Kafka Systems Building Metadata 20

KDnuggets

JULY 30, 2019

The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.

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Process Machine Learning 113

KDnuggets

JULY 31, 2019

I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.

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IT Data Science Data 113

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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?

Business Intelligence

KDnuggets

AUGUST 2, 2019

This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.

Deep Learning 113

Deep Learning Python 113

KDnuggets

AUGUST 1, 2019

By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.

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Deep Learning Programming Coding Python 115

KDnuggets

AUGUST 1, 2019

Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.

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Algorithm 106

KDnuggets

AUGUST 1, 2019

Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.

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Deep Learning Project Management 103

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.

Data

KDnuggets

JULY 30, 2019

Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.

Data Science 99

Data Science Datasets Data Process 99

KDnuggets

AUGUST 1, 2019

A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model.

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Machine Learning Technology IT 93

KDnuggets

AUGUST 2, 2019

Lessons learned from repeatedly smashing my head with a 2-meter long metal pole for a college engineering course.

Data Science 118

Data Science Data Engineering Education 118

KDnuggets

JULY 30, 2019

This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.

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Data Science Data Machine Learning 86

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.

Manufacturing

KDnuggets

JULY 30, 2019

This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.

Data 86

Data Designing Process Data Science 86

KDnuggets

JULY 31, 2019

We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.

Machine Learning 89

Machine Learning Data 89

KDnuggets

AUGUST 2, 2019

The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support.

Machine Learning 114

Machine Learning Data Analytics Python Data 114

KDnuggets

JULY 29, 2019

This free ebook is a great resource for data science beginners, providing a good introduction into Python, coding with Raspberry Pi, and using Python to building predictive models.

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Python Data Science Coding Building 76

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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.

Cloud

KDnuggets

JULY 31, 2019

You can do more data science than you think from the terminal.

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Data Science Data 95

KDnuggets

JULY 29, 2019

Recently, AI researchers from Microsoft open sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine learning models based on blockchain technologies.

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Machine Learning Building Technology Project 53

KDnuggets

JULY 31, 2019

How Netflix works: the (hugely simplified) complex stuff that happens every time; Top Certificates and Certifications in Analytics, Data Science, ML; Nothing but NumPy: Understanding &Creating Neural Networks with Computation.

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Certification Data Science Data 49

KDnuggets

JULY 29, 2019

Also: Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras; Fantastic Four of Data Science Project Preparation; The Death of Big Data and the Emergence of the Multi-Cloud Era; The title CDO started out as a joke.

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Big Data Data Science Python Cloud 48

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.

Systems

KDnuggets

AUGUST 2, 2019

South Dakota State University is seeking a Data Visualization Developer and Analyst in Brookings, SD, to create business intelligence tools and reports to support the use of a campus-wide business intelligence and decision support system, compile multiple visualizations into intuitive dashboards for campus-wide use, and more.

Business Intelligence 47

Business Intelligence Data Systems 47

KDnuggets

JULY 29, 2019

Seeking a Lecturer / Sr Lecturer for the Monash Blockchain Technology Centre (Monash BTC): a visionary enterprise that will bring together world-leading expertise from across Monash to explore, develop and innovate the technology of blockchain, in collaboration with various industry and societal sectors.

Technology 46

Technology 46

KDnuggets

JULY 31, 2019

Learn the essential skills needed to become a Data Science rockstar; Understand CNNs with Python + Tensorflow + Keras tutorial; Discover the best podcasts about AI, Analytics, Data Science; and find out where you can get the best Certificates in the field.

Data Science 46

Data Science Certification Python Data 46

KDnuggets

JULY 31, 2019

When it comes to AI, there’s plenty of talk of the future of machines. But it’s the people behind AI development who have the insights needed to shape that future. Register now to catch all of our speakers at the Data Science Salon Miami, Sep 10-11, 2019.

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Data Science Data IT 45

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.

Project