DataRobot Blog

OCTOBER 18, 2022

As AI-driven use cases increase, the number of AI models deployed increases as well, leaving resource-strapped data science teams struggling to monitor and maintain this growing repository. “We These accelerators are specifically designed to help organizations accelerate from data to results. See DataRobot AI Cloud in Action.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Algorithm: A set of rules to follow to solve a problem or to decide on a particular action (e.g., They provide more like an FAQ (Frequently Asked Questions) type of an interaction.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Most data scientists are strong in one or two of these areas, but not all three. This frees up data scientists to focus on more complex analytical tasks.

ROI 52

ROI Machine Learning Experimentation Data Science 52

The Unofficial Google Data Science Blog

APRIL 23, 2024

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Figure 4: Visualization of a central composite design. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. production, default) values.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JUNE 13, 2019

Visually examine the poorest matches, trying to understand what the MNIST authors could have done differently to justify these differences without at the same time changing the existing close matches. Start with a first reconstruction algorithm according to the information found in the [separate] paper introducing the MNIST dataset.

Testing 83

Testing Data Science Experimentation Metadata 83

DataRobot

APRIL 19, 2021

When the machine learning algorithm is trained, data scientists take the outputs (predictions), apply business rules to turn predictions into decisions, and send these predictions and decisions into the organization’s computer systems. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

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

FineReport

DECEMBER 19, 2019

At the same time, it also advocates visual exploratory analysis. The visualization component library of FineReport is very rich. It can be used as a portal for data reporting, or as a platform for business analysis. Data Analysis Libraries. Pandas is a Python data science library that is constantly improving.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. The ML Solution. A/B testing).

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

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. “Instead of general interpretability, we focus on the use of interpretations to produce insight from ML models as part of the larger data–science life cycle. ” and advocate.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

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 Visualization Experimentation Modeling 52

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. By Michael Milton.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

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. Example: A student is struggling with a complex math concept.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

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. Figure 4 is a visual representation of these steps.

Testing 58

Testing Experimentation Measurement Modeling 58

Alation

SEPTEMBER 28, 2022

Moreover, as the highly fragmented technologies of the data industry are now ever-expanding, having a DataOps mindset and infrastructure helps data teams to build a sustainable way of adopting new technologies as a company grows. This requires that data engineers embrace learning and integrating new technologies, such as AI tools.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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

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

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Meta-Orchestration . DevOps Infrastructure Tools.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

MARCH 7, 2023

While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. Here are some force-multiplying differences achievable by agile data teams: Want that dashboard, then update the data catalog.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

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

DataRobot Blog

OCTOBER 27, 2022

As highlighted in the DataRobot interface above, the Data Drift tab is enhanced with a Drill Down section for users to visualize drift details. This is important as data drift can look different at different time granularities; drift can happen at any time and at any rate. MLOps Is Vital for Enterprise AI.

Data Science 52

Data Science Experimentation Visualization Modeling 52

IBM Big Data Hub

OCTOBER 20, 2023

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

Machine Learning 71

Machine Learning Manufacturing Cost-Benefit Deep Learning 71

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. ML model interpretability and data visualization. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataRobot

SEPTEMBER 14, 2021

And for those that do make it past the experimental stage, it typically takes over 18 months for the value to be realized. This helps experts save time on mundane coding tasks so they can spend more time focusing on experimenting with data, advanced algorithms, and other high-value data science activities.

Machine Learning 94

Machine Learning Modeling Experimentation Visualization 94

Decision Management Solutions

JUNE 25, 2019

She emphasized the importance to data science teams of business translation and of partnering with domain experts. It’s often hard to get domain expertise and data science skills in the same person – hence the need for translation. DecisionsFirst indeed! Finally, Shingai Manjengwa , CEO, Fireside Analytics Inc.

Predictive Analytics 75

Predictive Analytics Analytics Data Science Experimentation 75

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. They have the foundations of data infrastructure. Yet, this challenge is not insurmountable.

Management 110

Management Machine Learning Experimentation Metrics 110

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

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

datapine

JANUARY 31, 2022

BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. What Are The Benefits of Business Intelligence?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

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. Visualizing data using t-SNE. Plotting Word Vectors.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

MAY 11, 2017

The Smarten Advanced Data Discovery gives users the freedom to leverage data beyond simple visual data analysis and dashboards. Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer.

Experimentation 49

Experimentation Visualization Business Intelligence Predictive Analytics 49

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. Experimentation and collaboration are built into the core of the platform. Hyperparameter Tuning.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99