KDnuggets
NOVEMBER 28, 2019
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.
Uber Engineering
NOVEMBER 26, 2019
When something goes wrong with a piece of code, engineers want to know all the relevant details of the error immediately so they can get right to work remedying the malfunction. . However, as technology has advanced, measuring system metrics and … The post Optimizing Observability with Jaeger, M3, and XYS at Uber appeared first on Uber Engineering Blog.
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Data Engineering Podcast
NOVEMBER 26, 2019
Summary The team at Sentry has built a platform for anyone in the world to send software errors and events. As they scaled the volume of customers and data they began running into the limitations of their initial architecture. To address the needs of their business and continue to improve their capabilities they settled on Clickhouse as the new storage and query layer to power their business.
Confluent
NOVEMBER 25, 2019
Potential advantages of hybrid cloud architectures include avoiding vendor lock-in, increasing system resilience, optimizing costs, and inducing price competition among cloud providers. Hybrid cloud architectures require the ability to securely […].
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.
KDnuggets
NOVEMBER 28, 2019
This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
InData Labs
NOVEMBER 26, 2019
Today we witness new technologies arising and new tendencies influencing the way we work and live. In order to stay competitive, it’s important to be aware of the latest trends, maintain a level of strong expertise, and offer best-in-class services. Our team is always striving to move with the times and keep all modern requirements. Запись TDA Announces InData Labs as the Winners in Global Agency Awards впервые появилась InData Labs.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
KDnuggets
NOVEMBER 27, 2019
Also: Deep Learning for Image Classification with Less Data; How to Speed up Pandas by 4x with one line of code; 25 Useful #Python Snippets to Help in Your Day-to-Day Work; Automated Machine Learning Project Implementation Complexities.
KDnuggets
NOVEMBER 29, 2019
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.
KDnuggets
NOVEMBER 28, 2019
Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.
KDnuggets
NOVEMBER 29, 2019
PyTorch Lightning is a lightweight framework which allows anyone using PyTorch to scale deep learning code easily while making it reproducible. In this tutorial we’ll use Huggingface's implementation of BERT to do a finetuning task in Lightning.
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.
KDnuggets
NOVEMBER 26, 2019
This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.
KDnuggets
NOVEMBER 25, 2019
In this article, we want to highlight some key data science use cases in marketing. Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.
KDnuggets
NOVEMBER 25, 2019
With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.
KDnuggets
NOVEMBER 26, 2019
Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.
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.
KDnuggets
NOVEMBER 27, 2019
A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input DataFrame is transformed as it passes through each stage. Now let’s see how this can be done in Spark NLP using Annotators and Transformers.
KDnuggets
NOVEMBER 27, 2019
As the fields of data science and analysis continue to expand, the next crop of bright minds is always needed. Learn more about the nuances of these jobs and find where you can fit in for a rewarding and interesting career.
KDnuggets
NOVEMBER 29, 2019
Read this article on training Markov chains to generate George R. R. Martin style text.
KDnuggets
NOVEMBER 26, 2019
A guide to build a content-based movie recommender model based on NLP.
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KDnuggets
NOVEMBER 25, 2019
Google recently published some work about modeling attention mechanisms in deep neural networks.
KDnuggets
NOVEMBER 27, 2019
This is a summary of a recent paper on an age-old topic: what visualisation should I use? No prizes for guessing “it depends!” Is this the paper to finally settle the age-old debate surrounding pie-charts??
KDnuggets
NOVEMBER 26, 2019
Join this live webinar: Fast, Agile, Service-Driven Insurance: Fuse Innovative Tech to Your Company DNA - AI, Chatbots, Automation and More, Dec 11 at 10:00am EST, to get actionable insight to develop your strategy.
KDnuggets
NOVEMBER 27, 2019
This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification.
Speaker: Margaret-Ann Seger, Head of Product, Statsig
Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating
KDnuggets
NOVEMBER 28, 2019
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
KDnuggets
NOVEMBER 25, 2019
Also: Automated Machine Learning Project Implementation Complexities; Text Encoding: A Review; The Notebook Anti-Pattern; Data Science for Managers: Programming Languages; 10 Free Must-read Books on AI.
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