Business Over Broadway

AUGUST 4, 2020

Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g.,

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Deploy: includes validating, publishing and delivering working models into a business environment.

Data Science 75

Data Science Testing Modeling Publishing 75

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

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. I’m also proud to announce an exciting new product: Domino Model Monitor (DMM). 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

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot Blog

MARCH 8, 2023

Leveraging DataRobot’s JDBC connectors, enterprise teams can work together to train ML models on their data residing in SAP HANA Cloud and SAP Data Warehouse Cloud, as well as have an option to enrich it with data from external data sources.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Sound familiar? What is CDSW?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

CIO Business Intelligence

MAY 23, 2022

Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light.

Experimentation 125

Experimentation Cost-Benefit Data Lake Data Science 125

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. What’s going on?

Modeling 101

Modeling IT Data Science Experimentation 101

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. But what does it mean?

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

IBM Big Data Hub

MAY 16, 2019

The next chapter is all about moving from experimentation to true transformation. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI as core to the business strategy. Companies are entering “chapter two” of their digital transformation. It’s about gaining speed and scale.

Experimentation 85

Experimentation Data Science Digital Transformation Modeling 85

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? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 124

Machine Learning Data Science Experimentation IoT 124

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. Organization: Columbia University Price: Students pay Columbia Engineering’s rate of tuition (US$2,362 per credit).

Big Data 121

Big Data Data Analytics Analytics Predictive Modeling 121

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

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model 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. Model interpretability continues to spark public discourse among industry. Model interpretability continues to spark public discourse among industry. Insights are drawn from Bin Yu, W.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

DataRobot

JUNE 17, 2021

I remember my peers recommended staying in academia, but I thought it would be a distraction–all I wanted to do was work on Eureqa and try to create AI to help scientists to discover new laws of physics in raw data. At the time, I had a small following of people interested in using Eureqa to derive mathematical formulas and models.

Modeling 52

Modeling Experimentation Machine Learning Measurement 52

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 Strategy 142

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep 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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo. Automate with Rapid Iteration to Get to Scale and Compliance.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

CIO Business Intelligence

NOVEMBER 21, 2023

Set parameters and emphasize collaboration To address one root cause of shadow IT, CIOs must also establish a governance and delivery model for evaluating, procuring, and implementing department technology solutions.

IT 130

IT Risk Cost-Benefit Risk Management 130

Domino Data Lab

MARCH 20, 2019

Our mission at Domino is to enable organizations to put models at the heart of their business. Today we’re announcing two major new capabilities in Domino that make model development easier and faster for data scientists. This pain point is magnified in organizations with teams of data scientists working on numerous experiments.

Management 97

Management Experimentation Data Science Modeling 97

CIO Business Intelligence

DECEMBER 19, 2023

Many technology investments are merely transitionary, taking something done today and upgrading it to a better capability without necessarily transforming the business or operating model. If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation.

Digital Transformation 140

Digital Transformation Unstructured Data Machine Learning Risk Management 140

The Unofficial Google Data Science Blog

JULY 22, 2020

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

Experimentation 95

Experimentation Statistics Testing Strategy 95

IBM Big Data Hub

MAY 9, 2023

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models.

Metadata 75

Metadata Risk Experimentation Dashboards 75

AWS Big Data

JUNE 20, 2023

Feature engineering is a process of identifying and transforming raw data (images, text files, videos, and so on), backfilling missing data, and adding one or more meaningful data elements to provide context so a machine learning (ML) model can learn from it.

Data Lake 71

Data Lake Data Science Recreation/Entertainment Experimentation 71

IBM Big Data Hub

OCTOBER 4, 2022

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. Platforms and practices not optimized for AI.

Metadata 101

Metadata Risk Risk Management Experimentation 101

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Model Deployment Customers need the flexibility to deploy models into different environments.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

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. Semi-structured data falls between the two.

Unstructured Data 132

Unstructured Data Data Analytics Analytics Structured Data 132

DataRobot Blog

OCTOBER 18, 2022

Monitoring and Managing AI Projects with Model Observability. 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.

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). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. Examples: Cars, Trucks, Taxis. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

MAY 12, 2022

Driverless AI offers automated pipeline; AI adapts to incoming data. Drag-and-drop Modeler for creating pipelines, IBM integrations. Full IDE for data scientists, automation for non-coding users, drag-and-drop designer. SPSS Modeler is a drag-and-drop tool for creating data pipelines that lead to actionable insights.

Predictive Analytics 136

Predictive Analytics Analytics Statistics Machine Learning 136