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

Analytics Vidhya

SEPTEMBER 6, 2021

This article was published as a part of the Data Science Blogathon Introduction: Artificial Neural Networks (ANN) are algorithms based on brain function and are used to model complicated patterns and forecast issues. The […]. The post Introduction to Artificial Neural Networks appeared first on Analytics Vidhya.

Deep Learning 256

Deep Learning Forecasting Data Science Publishing 256

Analytics Vidhya

NOVEMBER 1, 2021

This article was published as a part of the Data Science Blogathon Introduction In this article, we will cover everything from gathering data to preparing the steps for model training and evaluation. Deep learning algorithms can have huge functional uses when provided with quality data to sort through.

Deep Learning 296

Deep Learning Forecasting Sales Data Science 296

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Analytics Vidhya

OCTOBER 28, 2020

Overview This article will see how to create a stacked sequence to sequence the LSTM model for time series forecasting in Keras/ TF 2.0. . The post Multivariate Multi-step Time Series Forecasting using Stacked LSTM sequence to sequence Autoencoder in Tensorflow 2.0 / Keras appeared first on Analytics Vidhya.

Forecasting 174

Forecasting Modeling Analytics Deep Learning 174

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets.

Data Science 124

Data Science Machine Learning Predictive Modeling Experimentation 124

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Models can be designed, for instance, to discover relationships between various behavior factors.

Predictive Analytics 134

Predictive Analytics Analytics Predictive Modeling Forecasting 134

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 234

Machine Learning Technology Deep Learning Data Science 234

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

Cloudera

OCTOBER 26, 2022

Deep Learning for Anomaly Detection : ?? Apply modern, deep learning techniques for anomaly detection to identify network intrusions. Deep Learning for Image Analysis : Build a semantic search application with deep learning models. AMPs so far include: .

Machine Learning 76

Machine Learning Deep Learning Cost-Benefit Forecasting 76

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

O'Reilly on Data

FEBRUARY 11, 2019

Recent improvements in tools and technologies has meant that techniques like deep learning are now being used to solve common problems, including forecasting, text mining and language understanding, and personalization. AI and machine learning in the enterprise. Deep Learning. Model lifecycle management.

Big Data 207

Big Data Technology Machine Learning Deep Learning 207

Cloudera

FEBRUARY 25, 2021

AMPs move the starting line for any ML project by enabling data scientists to start with a full end-to-end project developed for a similar use case, including a trained and deployed ML model, as well as prebuilt predictive business applications, out of the box. The work of a machine learning model developer is highly complex.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

CIO Business Intelligence

JANUARY 25, 2024

That doesn’t mean getting certifications in deep learning or mastering natural language processing. A developer creating an AI app can’t assume their company has unlimited access to the datasets of commercial large language models. If you start with that deep understanding, then you can use AI to do much more at a larger scale.”

Deep Learning 92

Deep Learning Cost-Benefit Statistics Manufacturing 92

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Programming knowledge is needed for the typical tasks of transforming data, creating graphs, and creating data models. Basics of Machine Learning. Machine learning is the science of building models automatically.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

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 one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Cloudera

JULY 27, 2021

Fitting Prophet models with complex seasonalities for electricity demand forecasting. The entirely custom front-end to one of our prototype applications with a probabilistic model of NPC real estate. We can build assistive diagnostic interfaces for model building, as in Structural Time Series.

Data Science 115

Data Science Enterprise Experimentation Machine Learning 115

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment. Modeling and Evaluation.

Marketing 361

Marketing Experimentation Metrics Testing 361

Cloudera

OCTOBER 27, 2020

Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries. While point predictions limit us to asking “what is the demand forecast for Tuesday?”

Deep Learning 61

Deep Learning Machine Learning Forecasting Uncertainty 61

IBM Big Data Hub

MAY 17, 2024

Poorly run implementations of traditional or generative AI in commerce—such as models trained on inadequate or inappropriate data—lead to bad experiences that alienate consumers and businesses. AI models analyze vast amounts of data quickly and accurately.

B2B 66

B2B Interactive Optimization Forecasting 66

IBM Big Data Hub

DECEMBER 5, 2023

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications. The impact of these investments will become evident in the coming years.

Unstructured Data 91

Unstructured Data Cost-Benefit Experimentation Machine Learning 91

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data. Objectives and Usage.

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Predictive Analytics Analytics Predictive Modeling Data mining 121

CIO Business Intelligence

JUNE 7, 2022

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

Data Analytics 123

Data Analytics Diagnostic Analytics Management Analytics 123

DataRobot Blog

SEPTEMBER 13, 2022

Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. Data scientists drive business outcomes. BARC ANALYST REPORT.

Forecasting 52

Forecasting Data-driven Machine Learning Optimization 52

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

DataRobot

APRIL 14, 2020

ODSC has always been an exceptional place to highlight these solutions and share some of the thought leadership around AI and machine learning, which is why we are excited to be a part of ODSC Virtual Conference & Expo this week. Read on to learn more about the presentations.

Deep Learning 52

Deep Learning Machine Learning Forecasting Data Science 52

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

Machine Learning 73

Machine Learning Manufacturing Cost-Benefit Deep Learning 73

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

Enterprise 120

Enterprise Modeling Testing Data-driven 120

CIO Business Intelligence

JUNE 2, 2022

Among the hot technologies, artificial intelligence and machine learning — a subset of AI that that makes more accurate forecasts and analysis as it ingests data — continue to be of high interest as banks keep a strong focus on costs while trying to boost customer experience and revenue. Gartner highlights AI trend in banking.

Predictive Analytics 137

Predictive Analytics Technology Analytics Machine Learning 137

DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Explanations around data, models , and blueprints are extensive throughout the platform so you’ll always understand your results.

Experimentation 98

Experimentation Unstructured Data Deep Learning Modeling 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. However, the growing influence of ML isn’t without complications.

Machine Learning 75

Machine Learning Modeling Deep Learning Predictive Modeling 75

IBM Big Data Hub

DECEMBER 19, 2023

Supervised learning Supervised learning techniques use real-world input and output data to detect anomalies. A machine learning model trained with labeled data will be able to detect outliers based on the examples it is given. This usually helps to make the model’s predictions more accurate.

Machine Learning 66

Machine Learning Optimization Unstructured Data Sales 66

Smart Data Collective

OCTOBER 13, 2021

Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields). In forecasting future events. However, there will always be a decisive human factor, at least for a few decades yet. Prescriptive analytics.

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Visualization Cost-Benefit Big Data Prescriptive Analytics 135