DataRobot Blog

OCTOBER 18, 2022

Model Observability – the ability to track key health and service metrics for models in production – remains a top priority for AI-enabled organizations. These accelerators are specifically designed to help organizations accelerate from data to results. DataRobot Fireside Chat at Big Data & AI Toronto 2022. Request a Demo.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Data scientist skills.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

Domino Data Lab

SEPTEMBER 10, 2020

Yet despite all this hard work, few models ever make it into production (VentureBeat AI concluded that just 13% of data science projects make it into production) and in terms of delivering value to the business, Gartner predicts that only 20% of analytics projects will deliver business outcomes that improve performance.

Modeling 101

Modeling IT Data Science Experimentation 101

DataRobot Blog

FEBRUARY 28, 2023

A seamless user experience when deploying and monitoring DataRobot models to Snowflake Monitoring service health, drift, and accuracy of DataRobot models in Snowflake “Organizations are looking for mature data science platforms that can scale to the size of their entire business. Learn more about DataRobot hosted notebooks.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

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 ”.

Experimentation 41

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Domino Data Lab

DECEMBER 4, 2020

Approximating the region under the graph of as a series of trapezoids and calculating the sum of their area (in the case of non-uniformly distributed data points) is given by. Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Mean residence time. and many others.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Cloudera

APRIL 9, 2021

To effectively leverage their predictive capabilities and maximize time-to-value these companies need an ML infrastructure that allows them to quickly move models from data pipelines, to experimentation and into the business. Finally, CML has built-in model security and governance thanks to Cloudera Shared Data Experience (SDX).

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

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

AWS Big Data

NOVEMBER 2, 2023

It showcases the potential of Graviton2 in delivering enhanced price-performance ratios, making it an attractive choice for organizations seeking to optimize their big data workloads. GoDaddy benchmark During our initial experimentation, we observed that arm64 on EMR Serverless consistently outperformed or performed on par with x86_64.

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Cost-Benefit Big Data Testing Optimization 88

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

DataRobot

JUNE 23, 2021

To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. This allows data science teams to incorporate any machine learning algorithm or feature engineering method and seamlessly combine them with hundreds of built-in methods. So let’s dig in!

Machine Learning 52

Machine Learning Modeling Experimentation Visualization 52

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. accounting for effects "orthogonal" to the randomization used in experimentation. accounting for effects "orthogonal" to the randomization used in experimentation.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. Consider the case where experiment metrics are evaluated at the per-user level.

Testing 58

Testing Experimentation Measurement Modeling 58

Sisense

JANUARY 21, 2020

One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. To better understand when the data should be grouped, you should be familiar with causal inference. How common is Simpson’s paradox ? See “ How likely is Simpson’s paradox? (See

Testing 104

Testing Risk Data-driven Statistics 104

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

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. of users tagged Cuckoo’s Calling with ‘science-fiction’, but 10% of users tagged it with ‘mystery’, we should be more confident in the latter label.

Modeling 59

Modeling Metadata Publishing Sales 59

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

O'Reilly on Data

OCTOBER 13, 2020

Ideally, AI PMs would steer development teams to incorporate I/O validation into the initial build of the production system, along with the instrumentation needed to monitor model accuracy and other technical performance metrics. But in practice, it is common for model I/O validation steps to be added later, when scaling an AI product.

Management 246

Management Metrics Machine Learning Modeling 246

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. And the goodness doesn’t stop there.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

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. Vignette: Data Science at fluff.ai

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. In statistics, such segments are often called “blocks” or “strata”.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

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. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method. There has been debate as to whether the term “data science” is necessary. 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

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. Naturally, this issue is of particular concern to us in the Play Data Science team.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

MAY 31, 2016

A geo experiment is an experiment where the experimental units are defined by geographic regions. This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. They are non-overlapping geo-targetable regions. by turning campaigns off).

Advertising 42

Advertising Testing Sales Statistics 42

Domino Data Lab

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. The Area under the ROC Curve. 0.85 = 0.15. Yet, because 0.51

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

AUGUST 26, 2019

Models are at the heart of of data science and increasing predictive accuracy of models is a core expectation upon industry. The project also covers building a pipeline for automating ML workflow and stay tuned for additional hyperparamater content on the Domino Data Science blog. In [1]: # setup. Best Score: 0.98

Testing 79

Testing Modeling Machine Learning Metrics 79

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

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

DataRobot

APRIL 29, 2021

This list includes: Rachik Laouar is Head of Data Science for the Adecco Group. Rachik is working to transform that company’s products through data analytics and AI and will be speaking on the topic, Executive Track: Turning an Industry Upside Down. . Eric Weber is Head of Experimentation And Metrics for Yelp.

Machine Learning 59

Machine Learning Experimentation Data-driven Data Science 59