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. Capabilities Beyond Classic Jupyter for End-to-end Experimentation.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

DataRobot Blog

AUGUST 10, 2022

Snowflake provides a state-of the-art data platform for collating and analysing workforce data, and with the combined addition of DataRobot Solution Accelerator models, trusts can have predictive models running with little experimentation — further accelerated by the wide range of supportive datasets available through the Snowflake Marketplace.

Forecasting 52

Forecasting Experimentation Data-driven Risk 52

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? Model Explainability. Model Interpretability. Model Reproducibility. Machine Learning Model Lineage.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

DataRobot Blog

JULY 28, 2022

The accuracy of machine learning models is highly dependent on the quality of the training data. Without high-quality data, no matter how advanced the model is, it will not produce good results. Distinguishing between signal and noise is one important aspect of machine learning model improvement. Therefore, a value below 0.5

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. Execute a statement that will generate a result set.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Modeling 103

Smarten

NOVEMBER 3, 2017

Business users can quickly and easily prepare and analyze data and visualize and explore data, notate and highlight data and share data with others to identify the important ‘nuggets’, buried in traditional data, and to connect the dots, find exceptions, identify patterns and trends and better predict results.

Analytics 40

Analytics Visualization Experimentation Cost-Benefit 40

CIO Business Intelligence

MARCH 5, 2024

Many other platforms, such as Coveo’s Relative Generative Answering , Quickbase AI , and LaunchDarkly’s Product Experimentation , have embedded virtual assistant capabilities but don’t brand them copilots. GitHub first launched its copilot in 2021 , and Microsoft 365 Copilot became generally available a few months ago.

Cost-Benefit 125

Cost-Benefit Advertising Experimentation Software 125

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Results are typically achieved through a scientific process of discovery, exploration, and experimentation, and these processes are not always predictable. Conclusion.

Cost-Benefit 294

Cost-Benefit Advertising ROI Machine Learning 294

DataKitchen

JANUARY 17, 2022

The business analysts creating analytics use the process hub to calculate metrics, segment/filter lists, perform predictive modeling, “what if” analysis and other experimentation. A large pharmaceutical Business Analytics (BA) team struggled to provide timely analytical insight to its business customers. Data is not static.

Experimentation 130

Experimentation Data Lake Marketing Predictive Modeling 130

Rocket-Powered Data Science

JULY 6, 2021

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. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

Data Science and Beyond

JULY 22, 2018

Kaggle was only about predictive modelling competitions back then, and so I believed that data science is about using machine learning to build models and deploy them as part of various applications. It is now much easier to deploy machine learning models, even without a deep understanding of how they work.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

AWS Big Data

NOVEMBER 14, 2023

With a combination of low-latency data streaming and analytics, they are able to understand and personalize the user experience via a seamlessly integrated, self-reliant system for experimentation and automated feedback. Data is a key strategic asset for every organization, and every company is a data business at its core.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. And I meant every word of it. I fundamentally believe that is wrong. For a few simple reasons: # 1 It's Not Expensive! You can start for free with a superb tool: Google's Website Optimizer. Look for actionable uniqueness. That is nice, well worth paying for. ]. #

Testing 112

Testing Experimentation Risk Optimization 112

O'Reilly on Data

SEPTEMBER 11, 2019

For example, even though ML and ML-related concepts —a related term, “ML models,” (No. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. If anything, this focus has shifted to the ML or predictive model. ML and AI topics claim top spots.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

AUGUST 22, 2019

For example, in this chapter, we’ll build a model to classify movie reviews as positive or negative. Depending on the particular task that we’ve designed our model for, as well as the dataset that we’re feeding into it, we may use all, some, or none of these data preprocessing steps. considered to be stop words.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

datapine

JANUARY 31, 2022

They’re about having the mindset of an experimenter and being willing to let data guide a company’s decision-making process. The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

DataRobot

JANUARY 20, 2022

— Collaborating via Snowflake Data Cloud and DataRobot AI Cloud Platform will enable multiple organizations to build a community movement where experimentation, innovation, and creativity flourish. ICSs can reduce the time taken to build population health registries and predictive models by up to 90 percent.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

Smarten

JULY 3, 2017

When an enterprise selects a self-serve business intelligence solution with Advanced Data Discovery , Smart Data Visualization , Plug n’ Play Predictive Analysis and Self-Serve Data Preparation , it can create an environment where business users are empowered and become greater assets to the organization. It’s simple!

Visualization 40

Visualization Predictive Modeling Business Intelligence Experimentation 40