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

JANUARY 27, 2019

In my previous articles Predictive Model Data Prep: An Art and Science and Data Prep Essentials for Automated Machine Learning, I shared foundational data preparation tips to help you successfully. by Jen Underwood. Read More.

Machine Learning 92

Machine Learning Modeling Predictive Modeling Predictive Analytics 92

Smarten

APRIL 10, 2023

Investment in predictive analytics benefits everyone in the organization, including business users and team members, data scientists and the organization in general. Team members can bridge the gap of data science skills so they don’t have to wait for IT or data scientists to help them produce a report or perform analytics.

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Predictive Analytics Predictive Modeling Analytics Prescriptive Analytics 52

Cloudera

JULY 18, 2018

Additionally, organizations are increasingly restrained due to budgetary constraints and having limited data sciences resources. It is fair to say that healthcare faces many challenges, including developing, deploying, and integrating machine learning and artificial intelligence (AI) into clinical workflow and care delivery.

Machine Learning 50

Machine Learning Predictive Analytics Analytics Prescriptive Analytics 50

Smarten

APRIL 25, 2024

By providing business users with the right solution, you can ensure dependable data quality and offer your users valuable data insight that will support good business decisions. Users can harness the power of statistics and machine learning to uncover hidden insights and improve the overall quality of your data.

Data Quality 62

Data Quality Analytics Machine Learning Visualization 62

CIO Business Intelligence

OCTOBER 13, 2023

Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

MAY 18, 2023

This report outlines the combination of traditional decision automation tools with machine learning models and other technologies. It provides a low-code environment where business experts can manage decision automation projects and apply their knowledge to model decisions without extensive coding or deep AI knowledge.

Reporting 94

Reporting Scorecard Predictive Modeling Machine Learning 94

Cloudera

MARCH 21, 2022

Data Science tools, algorithms, and practices are rapidly evolving to solve business problems on an unprecedented scale. This makes data science one of the most exciting fields to be in. There are well-known barriers that slow down predictive modeling or application development.

Machine Learning 79

Machine Learning Predictive Modeling Data Science Testing 79

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ Access to Flexible, Intuitive Predictive Modeling. Forecasting. Classification. Hypothesis Testing. Correlation.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

Sisense

APRIL 10, 2020

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. NLP can be used on written text or speech data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: predictive maintenance, log data pipeline optimization, connected vehicles, industrial IoT, fraud detection, patient monitoring, network monitoring, and more. DATA FOR ENTERPRISE AI. The post Announcing the 2021 Data Impact Awards appeared first on Cloudera Blog.

Digital Transformation 66

Digital Transformation Machine Learning Optimization Data Lake 66

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). Foundation models can use language, vision and more to affect the real world.

Risk 76

Risk Modeling Management Metadata 76

Smarten

FEBRUARY 17, 2022

Without understanding the shift in workflow, responsibilities and how the use of data will change the enterprise, it is unlikely that the business will succeed in its Citizen Data Scientist initiative.

Predictive Modeling 76

Predictive Modeling Digital Transformation Enterprise Data Science 76

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

JUNE 23, 2022

They are confident that predictions can be made with some degree of accuracy and confidence, but the real question now is “so what?”. Most successful ML and AI projects are replacing an existing prediction with a more accurate and/or faster one. When that happens, the data science team needs to move forward with a Phase 2.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

Sisense

SEPTEMBER 9, 2019

We were most interested in exploring if it was possible to predict the return rate on a new product based on historical return rates of products with similar features. Then we ran Kraken’s machine learning and predictive modeling engine to get the results. It will be iterative.

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. For instance, a parsing model could identify the subject, verb and object of a complete sentence. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

IBM Big Data Hub

MARCH 7, 2023

The specific approach we took required the use of both AI and edge computing to create a predictive model that could process years of anonymized data to help doctors make informed decisions. We wanted to be able to help them observe and monitor the thousands of data points available to make informed decisions.

Risk 52

Risk Technology Informatics Predictive Modeling 52

DataRobot Blog

APRIL 1, 2022

As a Customer-Facing Data Scientist and an Evangelist at DataRobot, I would like to share with you a success story at Steward Health Care , the largest for-profit private hospital operator in the United States. Also, degrading models over time can pose a significant risk to the business. Challenge #3.

Machine Learning 59

Machine Learning Cost-Benefit Predictive Modeling Modeling 59

DataRobot Blog

JUNE 3, 2022

Generative Pre-trained Transformer 3 (GPT-3) is a language model that utilizes deep-structured learning to predict human-like text. GPT-3 was created by OpenAI – a San Francisco-based artificial intelligence research laboratory – as the third-generation language prediction model in the GPT-n series. Download now.

Risk 52

Risk Predictive Modeling Modeling Technology 52

DataRobot Blog

NOVEMBER 9, 2017

I work for a software company that has built an automated machine learning platform. I’ve spent the last 2+ years working with business analysts and MBA’s to build predictive models. We started offering a course called Data Science, Machine Learning, and AI for Executives a while back, and it’s been very successful.

Machine Learning 52

Machine Learning Data-driven Software Sales 52

Smarten

JUNE 17, 2022

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

The Unofficial Google Data Science Blog

JANUARY 31, 2017

By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. Some branches of machine learning (e.g.

Machine Learning 73

Machine Learning Modeling Statistics Predictive Modeling 73

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. 42% of data scientists are solo practitioners or on teams of five or fewer people. 42% of data scientists are solo practitioners or on teams of five or fewer people.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

AUGUST 12, 2021

For data, this refinement includes doing some cleaning and manipulations that provide a better understanding of the information that we are dealing with. In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

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. It also owns Google’s internal time series forecasting platform described in an earlier blog post. Our team does a lot of forecasting. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. imputation of missing values). ref: [link].

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Cloudera

APRIL 11, 2023

Applied ML Prototypes (AMPs) are fully built end-to-end data science solutions that allow data scientists to go from an idea to a fully working machine learning model in a fraction of the time. As you can tell, we left the guidance pretty open ended.

Forecasting 71

Forecasting Machine Learning Recreation/Entertainment Sales 71

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. Historically, fraud detection has been performed by analysts who are responsible for identifying and tracing suspicious patterns in the data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Cloudera

NOVEMBER 18, 2020

OVO UnCover enables access to real-time customer data using advanced, intelligent data analytics and machine learning to personalize the customer product interaction experience. The pipeline provides its clinicians fast access to real-time patient data and prediction models.

Internet Publishing and Broadcasting 119

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 119

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

Cloudera

APRIL 13, 2021

That’s because CDP has made it possible for them to modernize their legacy data platforms and extend machine learning (ML) and real-time analytics to public cloud, all while gaining cross-functional collaboration across the enterprise. . Use case #4: An analytics provider for life sciences processes data 72x faster.

Enterprise 74

Enterprise Cost-Benefit Data Warehouse Data Architecture 74

Alation

APRIL 14, 2022

The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Interpreting better results: Statistical techniques allow users to make predictions for unseen data, more easily improving the accuracy of output and results. A regression line is the best fit straight line.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Cloudera

SEPTEMBER 5, 2018

This is typically done using supervised machine learning techniques such as linear regression, which benefit greatly from having deeper and wider data. More data sets for longer historical time periods usually means better predictive models. Link analysis.

Machine Learning 40

Machine Learning Risk Cost-Benefit Big Data 40