FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. Big data is changing our world.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

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. Just-in-Time” manufacturing increases production while optimizing resources.

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI analyzes the data and identifies a rare genetic mutation linked to a specific disease.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

IBM Big Data Hub

DECEMBER 14, 2023

It is very easy to turn a terraform template into a deployable architecture and open the door to cost optimization, compliance management, self-service, standardization and repeatability. The extension of the CIO office’s dedicated data center to the IBM public cloud allowed for experimentation and growth with zero risk.

Experimentation 66

Experimentation Testing Software Management 66

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” We build models to test our understanding, but these models are not “one and done.” The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Data Science 142

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Load the dataset into Amazon S3.

Snapshot 84

Snapshot Data Lake Testing Strategy 84

Occam's Razor

AUGUST 12, 2013

There are few things more complicated in analytics (all analytics, big data and huge data!) The outcome in either scenario is a restructuring of the organization that is exquisitely geared towards taking advantage of portfolio optimization. than multi-channel attribution modeling. From a Venn -diagram. Or we could not.)

Modeling 161

Modeling Optimization Marketing Interactive 161

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. The following examples are also available in the sample notebook in the aws-samples GitHub repo for quick experimentation.

Data Lake 85

Data Lake Snapshot Metadata Optimization 85

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. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. This approach seeks to optimize resource utilization or infrastructure efficiency. 2) By workload type. Splitting compute clusters by type of workload is another good strategy. 3) By workload priority.

Metadata 87

Metadata Data Lake Optimization Strategy 87

datapine

FEBRUARY 4, 2020

If it has been optimized for SEO though, you shouldn’t stop measuring it after the first week, as it needs a couple of months to reach its “cruising traffic”, and you can get several thousands of monthly visits. Using this data can provide insights on whether your investments are stable or need more optimization to deliver specified targets.

Reporting 280

Reporting Marketing Advertising Metrics 280

AWS Big Data

JULY 26, 2023

From preview to GA and beyond Today, we are excited to announce the preview of the vector engine, making it available for you to begin testing it out immediately. We recognize that many of you are in the experimentation phase and would like a more economical option for dev-test.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

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. Keep in mind that data science is fundamentally interdisciplinary. Having more data is generally better; however, there are subtle nuances. PSL models are easy to use and fast.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

SEPTEMBER 15, 2014

Company UX leaders are happy to stink less by taking the sub-optimal path of responsive design, rather than create a mobile-unique experience (your customers tend to do different things on your desktop site than your mobile site!). Media-Mix Modeling/Experimentation. Media-Mix Modeling/Experimentation. Many reasons.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

AUGUST 13, 2012

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. I can use that to hypothesize what an optimal budget allocation might look like.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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

IBM Big Data Hub

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. Uber’s prowess as a transportation, logistics and analytics company hinges on their ability to leverage data effectively. But the simplicity ends there.

OLAP 87

OLAP Data Lake Data-driven Snapshot 87

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. Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. Takes three months. Stop obsessing about bounce rate.

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Years and years of practice with R or "Big Data." Test for analytics experience AND explore the level of analytical thinking the job candidate possesses.

Marketing 136

Marketing Analytics Machine Learning Strategy 136