The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

AWS Big Data

JANUARY 9, 2024

It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space to find matches. This functionality was initially released as experimental in OpenSearch Service version 2.4, This is also called embedding the text into the vector space.

Cost-Benefit 96

Cost-Benefit Visualization Modeling Machine Learning 96

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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). Media-Mix Modeling/Experimentation. Mobile content consumption, behavior along key metrics (time, bounces etc.) And again, a custom set of metrics.

Metrics 141

Metrics Reporting Analytics Marketing 141

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. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

FEBRUARY 4, 2020

To get started, you might want to equip yourself with a marketing BI software to analyze all your data and easily build professional reports. Structure your metrics. As with any report you might need to create, structuring and implementing metrics that will tell an interesting and educational data-story is crucial in our digital age.

Reporting 280

Reporting Marketing Advertising Metrics 280

The Unofficial Google Data Science Blog

JULY 31, 2016

At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. For example, you might compare the average test score among students exposed to the new curriculum to the average score among the controls. Sounds straightforward, right?

Experimentation 42

Experimentation Testing Measurement Metrics 42

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. To create the vector index, you must define the vector field name, dimensions, and the distance metric.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Occam's Razor

AUGUST 12, 2013

There are few things more complicated in analytics (all analytics, big data and huge data!) You only have to think about it for five seconds to realize it passes the ultimate test for everything: Common sense. Test that hypothesis using a percent of your budget and measure results. Or we could not.)

Modeling 161

Modeling Optimization Marketing Interactive 161

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. We sometimes refer to this as splitting “dev/test” from “production” workloads, but we can generalize the approach by referring to the overall priority of the workload for the business. Cloudera Manager 6.2

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

AUGUST 13, 2012

That means: All of these metrics are off. If you want to make the smartest decisions about your budget allocation then leveraging the time tested methodology of media mix modeling (at its core powered by controlled experiments) is the only way to go. "Was the data correct?" Pick metrics that matter.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

MAY 8, 2019

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

One purpose of data science is to inform actual “business decisions”, or, more precisely, decisions of importance to the team or community served by the data scientist. The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Second… well there is no second, it is all about the big action and getting a big impact on your bottom-line from your big investment in analytics processes, consulting, people and tools.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. There is no longer always intentionality behind the act of data collection — data are not collected in response to a hypothesis about the world, but for the same reason George Mallory climbed Everest: because it’s there. We ought not dredge our data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. 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. It is a really good metric.

Marketing 166

Marketing Metrics Advertising Measurement 166

IBM Big Data Hub

SEPTEMBER 25, 2023

There may be inaccuracy because of sampling, but it allows users to discover new viewpoints within the data. If the exploratory work needs to move on to testing and production, they can plan appropriately. Data Exploration and Innovation: The flexibility of Presto has encouraged data exploration and experimentation at Uber.

OLAP 90

OLAP Data Lake Data-driven Snapshot 90