October, 2014

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Benchmarking Performance: Your Options, Dos, Don'ts and To-Die-Fors!

Occam's Razor

'We are all blessed with more data than we know what to do with, and all for the price of a few lines of JavaScript added to your website. In this type of an environment, I've frequently stressed the value of identifying targets for your key performance indicators. [See step four in the process for creating your Digital Marketing and Measurement Model.].

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Moving Beyond CTR: Better Recommendations Through Human Evaluation

Edwin Chen

Imagine you're building a recommendation algorithm for your new online site. How do you measure its quality, to make sure that it's sending users relevant and personalized content? Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Take Google's search engine. In many cases, improving the quality of search results will decrease CTR!

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Greek Media Monitoring Kaggle competition: My approach

Data Science and Beyond

A few months ago I participated in the Kaggle Greek Media Monitoring competition. The goal of the competition was doing multilabel classification of texts scanned from Greek print media. Despite not having much time due to travelling and other commitments, I managed to finish 6th (out of 120 teams). This post describes my approach to the problem. Data & evaluation The data consists of articles scanned from Greek print media in May-September 2013.

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Understanding Business Requirements Are Essential For Analytics Projects

Wise Analytics

Analytics projects and the associated data preparation, storage, and management require continual effort. Sometimes mid-market organizations have a good sense of what they want to achieve and how they need to get there, but overlook the value of gathering in-depth business and technical requirements before diving into software and hardware selection.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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Data Science 101 : Playing with Scraping in Python

MLWhiz

This is a simple illustration of using Pattern Module to scrape web data using Python. We will be scraping the data from imdb for the top TV Series along with their ratings We will be using this link for this: [link] This URL gives a list of top Rated TV Series which have number of votes atleast 5000. The Thing to note in this URL is the “&start=” parameter where we can specify which review should the list begin with.

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Nutanix Simplicity reaches Microsoft System Center

Nutanix

We recently wrote a set of blogs about our integration with Windows Server 2012 R2, Hyper-V and the Microsoft ecosystem. The first post detailed our integration with Hyper-V and the strategic value of our partnership with Microsoft.

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Web-Scale Wish: A Non-Profit Datacenter Makeover

Nutanix

The right technology in the right hands has the power to change lives and organizations. This holiday season, Nutanix will gift web-scale converged infrastructure to a non-profit organization chosen by you, our community.

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Nutanix Metro Availability

Nutanix

Simply put, Metro Availability policies complete the Data availability spectrum by enabling customers to seamlessly keep applications online even on full site disasters.

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Nutanix Metro Availability

Nutanix

Simply put, Metro Availability policies complete the Data availability spectrum by enabling customers to seamlessly keep applications online even on full site disasters.

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Microsoft TechEd Barcelona Meets Web-Scale with Nutanix!

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

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.

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Announcing the Nutanix Technology Champion (NTC) Program

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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Announcing Nutanix Metro Availability

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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Announcing.NEXT 2015, the Web-Scale Conference

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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Reminder: VCDX Study Workshop for Mock Defenses at VMware HQ (Oct 4-5)

Nutanix

There’s no question that enterprise application usability has for the most part been left behind.

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How to Build an Experimentation Culture for Data-Driven Product Development

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

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What is data science?

Data Science and Beyond

Data science has been a hot term in the past few years. Despite this fact (or perhaps because of it), it still seems like there isn't a single unifying definition of data science. This post discusses my favourite definition. Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. — Josh Wills (@josh_wills) May 3, 2012 One of my reasons for doing a PhD was wanting to do something more interesting than “vani