CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. Training and running these models require massive computing power, leading to a significant carbon footprint.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

O'Reilly on Data

MARCH 24, 2020

This is important: consider how many modern models need to operate at scale and in real time (such as Google’s search engine and the relevant tweets that Twitter surfaces in your feed). Model results will then be less reliant on individuals making hundreds of micro-decisions. Why is it important to automate data preparation?

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

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.

Predictive Analytics 139

Predictive Analytics Analytics Predictive Modeling Data mining 139

Cloudera

DECEMBER 4, 2024

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. The emergence of GenAI, sparked by the release of ChatGPT, has facilitated the broad availability of high-quality, open-source large language models (LLMs).

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

O'Reilly on Data

JUNE 18, 2019

Bottom-up solutions with human-guided ML pipelines (such as Tamr, Paxata, or Informatica— full disclosure: Ihab Ilyas is co-founder of Tamr ) show how to leverage the available rules and human expertise to train scalable integration models that work on thousands of sources and large volumes of data. Data programming.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

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”). It is an essential asset.

Analytics 167

Analytics Machine Learning Data Science Data Collection 167

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics in business Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes. Predictive models can help businesses attract, retain, and nurture their most valued customers.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software. Careers, Data Scientist, Generative AI, Hiring, IT Jobs

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

CIO Business Intelligence

APRIL 25, 2022

The course includes instruction in statistics, machine learning, natural language processing, deep learning, Python, and R. It offers a bootcamp in data science and machine learning for individuals with experience in Python and coding. It culminates with a capstone project that requires creating a machine learning model.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Decision Management Solutions

JANUARY 16, 2020

The Machine Learning Times (previously Predictive Analytics Times) is the only full-scale content portal devoted exclusively to predictive analytics. ” In his article, Eric warns, “Predictive models often fail to launch. ” In his article, Eric warns, “Predictive models often fail to launch.

Machine Learning 87

Machine Learning Predictive Modeling Predictive Analytics Deep Learning 87

CIO Business Intelligence

JANUARY 19, 2024

Cost: $180 per exam Location: Online Duration: Self-paced Expiration: Credentials do not expire SAS Certified Advanced Analytics Professional The SAS Certified Advanced Analytics Professional credential validates your ability to analyze big data with a variety of statistical analysis and predictive modeling techniques.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. used for cleaning, manipulating, and analyzing data.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Sisense

APRIL 10, 2020

Python’s readable syntax makes it easy to learn and understand, since it can be read much like a human language. Python’s readable syntax makes it easy to learn and understand, since it can be read much like a human language. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. What is data analytics? Data analytics is a discipline focused on extracting insights from data. Data analytics tools.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Smart Data Collective

SEPTEMBER 19, 2019

There are other elements of speech synthetization technology that rely on machine learning. Machine learning is changing the direction of this radical technology. The Machine Learning Technology That Drives TTS. They talked about two very important machine learning approaches: Parametric TTS and Concatenative TTS.

Machine Learning 105

Machine Learning Technology Deep Learning Predictive Modeling 105

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. Most data starts as messy and must be molded to be usable.

Data Science 110

Data Science Statistics Predictive Analytics Recreation/Entertainment 110

Domino Data Lab

OCTOBER 7, 2020

The accuracy of any predictive model approaches 100%. Property 4: The accuracy of any predictive model approaches 100%. This means models can always be found that predict group characteristic with high accuracy. There should be no model to accurately predict even and odd rows with random data.

Statistics 145

Statistics Testing Predictive Modeling Big Data 145

Smart Data Collective

MAY 17, 2021

Big data solutions are often created and supported using various technologies from IIoT to machine learning and AI. All that performance data can be fed into a machine learning tool specifically designed to identify certain events, failures or obstacles. Big data is at the heart of the digital revolution. billion in 2018.

Big Data 132

Big Data Cost-Benefit IoT Machine Learning 132

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. In the training phase, the primary objective is to use existing examples to train a model. Safeguarding PII is not a new problem.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

Smarten

APRIL 19, 2024

This approach does demand skills, data curation, and significant funding, but it will serve the market for third-party, specialized models. In a recent survey of C-suite executives, 80% of said they believe AI will transform their organizations, and 64% said it is the most transformational technology in a generation.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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? Model Explainability. Model Interpretability. Model Reproducibility. Machine Learning Model Lineage.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

DMBS2

DECEMBER 12, 2017

As I wrote back then in a post about the connection between machine learning and the rest of AI , It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response. Accordingly, it can be reasonable to equate machine learning and AI.

Machine Learning 50

Machine Learning Predictive Modeling Deep Learning Modeling 50

IBM Big Data Hub

DECEMBER 20, 2023

Even in cases where an ML model isn’t itself biased or faulty, deploying it in the wrong context can produce errors with unintended harmful consequences. To deploy reinforcement learning, an agent takes actions in a specific environment to reach a predetermined goal. With repetition, the agent learns the best strategies.

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

DataRobot Blog

DECEMBER 15, 2022

By simplifying Time Series Forecasting models and accelerating the AI lifecycle, DataRobot can centralize collaboration across the business—especially data science and IT teams—and maximize ROI. The machine learning life cycle always starts with the dataset. Why is it so difficult to do it manually? Settings for Time Series projects.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

bridgei2i

DECEMBER 15, 2021

Imperative to predicting user preferences or interests and suggestions, the recommendation engine market size is projected to reach $12.03 Anticipating Demand through Predictive Modelling on OTT. AI in Recommendation Engines for OTT Platforms. billion by 2025.[1] The Future of AI in Media & Entertainment.

Recreation/Entertainment 52

Recreation/Entertainment Metadata Advertising Predictive Modeling 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. What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. positive, negative or neutral).

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

MAY 17, 2024

Poorly run implementations of traditional or generative AI technology in commerce—such as deploying deep learning models trained on inadequate or inappropriate data—lead to bad experiences that alienate both consumers and businesses.

B2B 64

B2B Interactive Optimization Data-driven 64

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. There are four critical components needed for a successful migration: AI/ML models to automate the discovery and semantics of the data. Collaboration and governance. Low-code, no-code operation.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Domino Data Lab

APRIL 21, 2021

Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. anomaly detection) or supervised model (classification), and requires less maintenance as the model can be automatically retrained to keep its associations up to date.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 1, 2021

This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. It is not possible to fully understand the inferential process of a deep neural network and to prove that it would generalise as expected. According to Fox et al.,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

SEPTEMBER 11, 2019

For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. ML and AI topics claim top spots. 221) to 2019 (No.

IoT 25

IoT Big Data Data Warehouse Uncertainty 25

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 82

Cost-Benefit Insurance Machine Learning Unstructured Data 82

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

CIO Business Intelligence

MAY 6, 2025

To make analytics a competitive differentiator, we must move from descriptive insights to predictive foresight and ultimately to prescriptive action. Ive seen clients use predictive models to forecast sales pipeline health, identify fraud risk in real-time, or assess which patients are most likely to be readmitted post-discharge.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78