AWS Big Data

JULY 8, 2024

In every Apache Flink release, there are exciting new experimental features. You can find valuable statistics you can’t normally find elsewhere, including the Apache Flink Dashboard. However, in this post, we are going to focus on the features most accessible to the user with this release.

Management 115

Management Dashboards Consulting Snapshot 115

Occam's Razor

APRIL 8, 2013

Remember that the raw number is not the only important part, we would also measure statistical significance. Airbnb had enough data points to be confident in their results. The result? The properties with professional photography had 2-3 times the number of bookings! By 2011, the company had 20 full-time photographers on staff.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

Octopai

MAY 2, 2022

Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. But the approaches and principles that form the basis of DataOps have been around for decades.

Manufacturing 98

Manufacturing Testing Data Quality Dashboards 98

FineReport

DECEMBER 19, 2019

Generally, companies will store data in local databases or public clouds. and others will use big data storage format like HBase and Parquet. Python and R are the two most widely used programming languages in the field of data analysis. Data Analysis Libraries. Most database systems use SQL. Programming Languages.

Knowledge Discovery 98

Knowledge Discovery Visualization Data mining Reporting 98

IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. Teams are comfortable with experimentation and skilled in using data to inform business decisions.

Modeling 96

Modeling Cost-Benefit Optimization Digital Transformation 96

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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

AWS Big Data

SEPTEMBER 13, 2024

We expect statistically equal distribution of jobs between the two clusters. contains(GroupName, 'eks-cluster-sg-bpg-cluster-')].GroupId" spark-cluster-a-v and spark-cluster-b-v are configured with a queue named dev and weight=50. For more information, refer to Weight Based Cluster Selection. contexts[] | select(.name

Management 111

Management Snapshot Cost-Benefit Testing 111

IBM Big Data Hub

SEPTEMBER 25, 2023

Presto provides a long list of functions, operators, and expressions as part of its open source offering, including standard functions, maps, arrays, mathematical, and statistical functions. Data Exploration and Innovation: The flexibility of Presto has encouraged data exploration and experimentation at Uber.

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86

AWS Big Data

JULY 3, 2023

Buy Experimentation findings The following table shows Sharpe Ratios for various holding periods and two different trade entry points: announcement and effective dates. To follow along with the examples below, you will need to use the notebook provided in the Quant Research example GitHub repository.

Snapshot 105

Snapshot Data Lake Testing Strategy 105

The Unofficial Google Data Science Blog

JULY 31, 2016

To figure this out, let's consider an appropriate experimental design. In other words, the teacher is our second kind of unit, the unit of experimentation. This type of experimental design is known as a group-randomized or cluster-randomized trial. When analyzing the outcome measure (e.g.,

Experimentation 42

Experimentation Testing Measurement Metrics 42

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 46

Slice and Dice Experimentation Data-driven Data Science 46

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data.

Experimentation 62

Experimentation Testing Statistics Metrics 62

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. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

Domino Data Lab

MAY 8, 2019

They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Having more data is generally better; however, there are subtle nuances. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

IBM Big Data Hub

MAY 20, 2024

As algorithm discovery and development matures and we expand our focus to real-world applications, commercial entities, too, are shifting from experimental proof-of-concepts toward utility-scale prototypes that will be integrated into their workflows.

Data Processing 94

Data Processing Optimization Experimentation Enterprise 94