DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

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. Set up a data pipeline that delivers predictions to HubSpot and automatically initiate offers within the business rules you set.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

O'Reilly on Data

MARCH 27, 2019

Watch highlights from expert talks covering AI, machine learning, data analytics, and more. People from across the data world are coming together in San Francisco for the Strata Data Conference. The journey to the data-driven enterprise from the edge to AI. Watch " Winners of the Strata Data Awards 2019.".

Uncertainty 207

Uncertainty Forecasting Data-driven Machine Learning 207

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot Blog

OCTOBER 18, 2022

The DataRobot expo booth at the 2022 conference showcased our AI Cloud platform with industry-specific demonstrations including Anti-Money Laundering for Financial Services , Predictive Maintenance for Manufacturing and Sales Forecasting for Retail. Monitoring with Machine Learning.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses.

Deep Learning 82

Deep Learning Machine Learning Forecasting Visualization 82

DataRobot

JUNE 29, 2021

Well, just imagine a production machine learning model that always stays accurate after it’s deployed—all by itself. Machine learning models trained on 2019 data didn’t know what to do. Calling something a feature diminishes its value, and Continuous AI is too important for that. Why do I think this? Over and over.

Machine Learning 80

Machine Learning Data Processing Forecasting Modeling 80

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning provides the technical basis for data mining.

Data mining 77

Data mining KDD Data Science Forecasting 77

Business Over Broadway

MAY 22, 2019

I conducted a customer experience best practices study and found that loyalty leading companies focus analytics efforts on customer understanding rather than internal metrics. 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

IBM Big Data Hub

AUGUST 7, 2023

This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. AI models can be designed to detect anomalies in real-time site performance data.

Optimization 58

Optimization Forecasting Data-driven Strategy 58

DataRobot Blog

MARCH 10, 2022

As manufacturing plants start to inject autonomous machines into their day-to-day operations, there is a growing need to monitor these devices and forecast maintenance requirements before failure and downtime. Time-Series Forecasting?—? High-level example of a common machine learning lifecycle.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

CIO Business Intelligence

JUNE 1, 2022

The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races. Cloud-connected cars are now commonplace in the mainstream connected car market that is forecast to surpass $166 billion by 2025. billion by 2030. A Competitive Differentiator.

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

Birst BI

AUGUST 23, 2018

A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. However, there are simply not enough data scientists in the world to deliver on the AI potential. The Outsourcing of Data Science Functions.

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

datapine

MARCH 31, 2022

BI developer skills encompass crafting and executing data-driven queries upon request as well as the ongoing technical development of a company’s BI platforms or solutions. Here is a more specific rundown of BI developer skills: Demonstrable experience in the areas of BI development or data science.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

OCTOBER 14, 2019

From artificial intelligence and machine learning to blockchains and data analytics, big data is everywhere. A global retailer like Amazon with its same-day shipping and multi-channel services might have billions of data points across several sectors. Gartner estimates a retail IT spend forecast of $210.9

Big Data 74

Big Data Software Data mining Prescriptive Analytics 74

Sisense

AUGUST 14, 2020

In Augmented Apps , we examine how product teams are exploring AI and Machine Learning to make their products more intuitive and enhance the user experience. . This is because a specialized context narrows the scope of the training data used in training the dialog algorithm. Predictive analytics AI boosts web app performance.

Machine Learning 98

Machine Learning Visualization Predictive Analytics Testing 98

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 80

Machine Learning Modeling Deep Learning Predictive Modeling 80

DataRobot Blog

DECEMBER 7, 2019

This blog provides a unique take on using machine learning to predict free agent signings in the off-season. For those interested in data science , most of our models registered R-squared values against our training data of between 0.7 We wanted to predict who will sign for how much, and which team will they go to.

Machine Learning 52

Machine Learning Forecasting Metrics Modeling 52

AWS Big Data

OCTOBER 26, 2023

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Amazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using SQL commands familiar to many roles such as executives, business analysts, and data analysts.

Modeling 64

Modeling Data Warehouse Machine Learning Testing 64

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. ln this post he describes where and how having “humans in the loop” in forecasting makes sense, and reflects on past failures and successes that have led him to this perspective.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. On the one hand, basic statistical models (e.g.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

DataRobot Blog

APRIL 4, 2022

Media data (usually weekly): media costs, media ratings generated (TVRs, magazine copies, digital impressions, likes, shares, etc.), The standard practice is that the data should be aggregated into a weekly format and span at least the last two to three years (ideally around five years). Classical Modeling Considerations.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

datapine

OCTOBER 21, 2020

Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends. But with big data tools in healthcare, it’s possible to streamline your staff management activities in a wealth of key areas. These analyses allowed the researchers to see relevant patterns in admission rates.

Big Data 364

Big Data Data Analytics Analytics Cost-Benefit 364

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M)

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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

DECEMBER 16, 2021

But let me share a few useful and exciting methods that I have been using lately: Time series – new series modelers: One of the challenges that you can face when forecasting demand is how to work around products that are seasonal or are newly introduced. See How DataRobot Extends the Capabilities for Expert Data Scientists.

Forecasting 52

Forecasting Metrics Data Science Modeling 52

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. James Warren, on the other part, is a successful analytics architect with a background in machine learning and scientific computing.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Sisense

FEBRUARY 24, 2021

Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts. The role of the chief data officer.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

bridgei2i

JANUARY 25, 2022

The foremost is that it solves a variety of problems in modern day machine learning. Earlier, machine learning was all about creating and training a model. But now we are looking at machine learning as a holistic process, from data handling, model building to operationalization. Shivalika: Exactly.

Machine Learning 52

Machine Learning Modeling Data Processing Enterprise 52

The Unofficial Google Data Science Blog

APRIL 19, 2021

To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration. If, over the long run, it really did rain about 40 percent of the time, that means your forecasts were well calibrated.

Modeling 122

Modeling IT Metrics Testing 122

AWS Big Data

MARCH 26, 2024

The following figure shows some of the metrics derived from the study. This can be achieved using AWS Entity Resolution , which enables using rules and machine learning (ML) techniques to match records and resolve identities. Organizations using C360 achieved 43.9% reduction in sales cycle duration, 22.8%

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. ROI = Profit/Budget). part of what makes this so difficult!

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Risk ROI Modeling Metrics 67

Insight

MARCH 28, 2019

Products to Leverage Employee Sentiment Insight Fellows utilized the Thinknum Glassdoor review data set, using the reviews and NLP techniques to build a trio of innovative data science products. Yanxia then used a combination of quantitative and qualitative metrics to interpret each topic group identified by LDA.

Dashboards 70

Dashboards Visualization Data Science Machine Learning 70

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. Example 11.9 0.85 = 0.15.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

APRIL 24, 2019

Think of it this way: Augmented Analytics automates data insight using machine learning and natural language generation. It automates data preparation and allows for easy data sharing. Users can compare results against plans and forecasts. What is Augmented Analytics?

Business Intelligence 0

Business Intelligence Analytics IT Metrics 0