IBM Big Data Hub

NOVEMBER 3, 2023

To add to the complexity, there is a shortage of finding the right people with the right skills to take on development or data science projects. When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

The Unofficial Google Data Science Blog

APRIL 23, 2024

This blog post discusses such a comprehensive approach that is used at Youtube. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. And we can keep repeating this approach, relying on intuition and luck.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Yet, despite the complexity (or because of it), data scientists and researchers curate and use different languages, tools, packages, techniques, and frameworks to tackle the problem they are trying to solve.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. A new drug promising to reduce the risk of heart attack was tested with two groups.

Testing 104

Testing Risk Data-driven Statistics 104

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Data Science and Beyond

APRIL 5, 2021

The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. I summarised this work in a post on the company’s blog , and discussed it in an interview with PublishPress.

Experimentation 61

Experimentation Testing Publishing Measurement 61

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

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

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 95

Cost-Benefit Modeling Manufacturing Interactive 95

Domino Data Lab

APRIL 7, 2019

If you are in research, excellent libraries like Allen NLP and NLP Architect are designed to make experimentation easier, although at the expense of feature completeness, speed and robustness. Image Credit: Parsa Ghaffari on the Raylien Blog. These can be either classical or deep-learning-based pipelines.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Testing and Data Observability.

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

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

Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

Management 244

Management Metrics Machine Learning Modeling 244

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. This blog post does not discuss objectives.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

JULY 31, 2016

This blog post explores how this problem arises in applications at Google, and how we can 'mind our units' through analysis and logging. At the end of the semester, you administer a proficiency test to the students and compare test performance across the groups. However, this is not always the case.

Experimentation 42

Experimentation Testing Measurement Metrics 42

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. In fact, this blog has published posts on this very topic. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime.

Experimentation 68

Experimentation Sales Metrics Measurement 68

DataRobot Blog

FEBRUARY 9, 2022

Refreshing models according to the business schedule or signs of data drift. Constantly creating and testing new challenger models. Models need to be simplified by constant iteration and experimentation. When your business has a backlog of use cases , its data scientists need to spend hours working on each problem.

IT 52

IT Experimentation Machine Learning Data Processing 52

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

There has been debate as to whether the term “data science” is necessary. Others argue that attaching the “science” is clear indication of a “wannabe” (think physics, chemistry, biology as opposed to computer science, social science and even creation science ). Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. Wouldn't it be great if we didn't require individual data to estimate an aggregate effect? A geo experiment is an experiment where the experimental units are defined by geographic regions. by turning campaigns off).

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? This blog post provides details for how we can make inferences without waiting for complete uptake. This blog post provides details for how we can make inferences without waiting for complete uptake. What if their uptake rate is not uniform?

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. Models are at the heart of of data science and increasing predictive accuracy of models is a core expectation upon industry. We can take this model to other, novel data and perform a final hold-out test assessment.

Testing 79

Testing Modeling Machine Learning Metrics 79

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

DataRobot

JANUARY 20, 2022

As health and care delivery converges, analytical staff will be required to work across more boundaries with larger volumes of data than ever before. . The DataRobot and Snowflake platforms include extensive built-in trust features to enable explainability and end-to-end bias and fairness testing and monitoring over time.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

DataRobot

JUNE 2, 2021

They went on to say that investing in MLOps directly answers one of the biggest questions facing AI practitioners in the enterprise: how to move from experimentation to transformation. This blog post explores some of the most interesting findings in the report about the growing importance of MLOps.

Experimentation 52

Experimentation Machine Learning Reporting Modeling 52

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Thankfully not only have modern data analysis tools made data collection cheap and easy, they have made the process of exploratory data analysis cheaper and easier as well. We must correct for multiple hypothesis tests.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Smarten

MAY 11, 2017

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer. Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation.

Experimentation 49

Experimentation Visualization Business Intelligence Predictive Analytics 49

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Our scientists and teams are familiar with working in Spark on EMR and using data with our existing feature store and data warehouse.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99