Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

You must use metrics that are unique to the medium. Ready for the best email marketing campaign metrics? So for our email campaign analysis let’s look at metrics using that framework. Optimal Acquisition Email Metrics. Allow me to rush and point out that this metric is usually just directionally accurate.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). Immediately actionable web analytics (your biggest worries covered). It is a book about Web Analytics 2.0.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Likely not. Omniture cannot save you. Only you can save yourself.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

AUGUST 13, 2012

That means: All of these metrics are off. The central team is responsible for analytics frameworks, centralized contracts (tools, consultants), for aggregated company level analysis, complex project execution (experimentation, media mix models etc) and for setting standards. "Was the data correct?" Hopefully soon!

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. What one critical metric will help you clearly measure performance for each strategy above? All that is great.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

JUNE 6, 2019

At the other end of the spectrum, the admin may instantiate a number of low-priority dev clusters – these clusters may often run at capacity, not require performance guarantees, but also provide more agility and flexibility for experimentation. Cloudera Manager 6.2 Learn more about Cloudera’s platform here.

Metadata 86

Metadata Data Lake Optimization Strategy 86

Occam's Razor

OCTOBER 19, 2010

Move from a data collection obsession and develop a crush on data analysys. Mongoose Metrics ~ ifbyphone. I know Mongoose Metrics a bit more and have been impressed with their solution and evolution over the last couple of years. Experimentation and Testing Tools [The "Why" – Part 1]. Three tools.

Analytics 135

Analytics Testing Measurement Optimization 135

Occam's Razor

MAY 27, 2015

It is an investment in numerous report writers or data (puking) automation or hiring a small army in India or Philippines to do that, before investing in any smart Analyst. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. < /A > ]. Cookies are all we need!

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. And for good reason!

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

JANUARY 7, 2014

Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) But if their primary output is just data, and not actions to take expressed in English or verbally in weekly senior staff meeting, then they are simply Reporting Squirrels. Overview & Importance of Qualitative Metrics.).

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

Marketer, is not spent with data you''ll fail to achieve professional success.]. Many used some data, but they unfortunately used silly data strategies/metrics. And silly simply because as soon as the strategy/success metric being obsessed about was mentioned, it was clear they would fail. You'll get fired.

Marketing 166

Marketing Metrics Advertising Measurement 166