IBM Journey to AI blog

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

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Ocean Protocol

JANUARY 25, 2024

The platform continues to mature as the web3 platform to crowdsource solutions to AI & ML challenges, business intelligence, applied data science, and predictive analytics. Desights is the application that the Ocean Data Science team uses to conduct data challenges.

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ODSC - Open Data Science

MARCH 28, 2023

Obsess Over Quantifying Impact The impact of an AI/ML model can be measured in money saved, revenue added, risk avoided, time saved, and other metrics. They rapidly prototype ML models to arrive at the right strategy, ultimately enhancing the return on their AI investment. This shift is similar to what we’d see in software engineering.

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IBM Journey to AI blog

FEBRUARY 2, 2023

They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights. In today’s world, data warehouses are a critical component of any organization’s technology ecosystem.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Post-Call Analytics provides an entire architecture around ingesting audio files in a fully automated workflow with AWS Step Functions , which is initiated when an audio file is delivered to a configured Amazon Simple Storage Service (Amazon S3) bucket.

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Pickl AI

SEPTEMBER 23, 2024

Analysts use statistical and computational techniques to derive meaningful insights that drive business strategies. Machine Learning Machine Learning (ML) is a crucial component of Data Science. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets.

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Tableau

NOVEMBER 24, 2019

Amazon SageMaker for Tableau QuickStart: The Amazon SageMaker for Tableau QuickStart , developed by Tableau and AWS partner Interworks , uses the Tableau Analytics Extensions API to integrate Amazon SageMaker machine learning (ML) models with Tableau's calculated fields to power predictive analytics.

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phData

DECEMBER 12, 2023

Step 2: Analyze the Data Once you have centralized your data, use a business intelligence tool like Sigma Computing , Power BI , Tableau , or another to craft analytics dashboards. A complete view of the fan, rather than pieces of information spread across various departments, means less guesswork and more data insights.

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AWS Machine Learning Blog

APRIL 12, 2023

The PdMS includes AWS services to securely manage the lifecycle of edge compute devices and BHS assets, cloud data ingestion, storage, machine learning (ML) inference models, and business logic to power proactive equipment maintenance in the cloud. It’s an easy way to run analytics on IoT data to gain accurate insights.

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Heartbeat

NOVEMBER 20, 2023

ML algorithms can analyze network data, identify suspicious patterns, and prevent or mitigate attacks. ML algorithms can offer enhancements that raise the overall effectiveness and scalability of the blockchain network by examining previous data and network performance. We pay our contributors, and we don't sell ads.

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IBM Journey to AI blog

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. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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Pickl AI

OCTOBER 16, 2024

It’s popular in corporate environments for Data Analysis and Business Intelligence. Advanced analytics tools integrate with RDBMS to offer predictive analytics capabilities, helping businesses anticipate trends and behaviours. Frequently Asked Questions What is RDBMS?

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IBM Journey to AI blog

JULY 11, 2023

They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics that enable faster decision making and insights. Data warehouses are a critical component of any organization’s technology ecosystem.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise. IBM watsonx.ai

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Alation

MARCH 21, 2023

For example, they can create micro segmentations that incorporate multiple factors such as: Age Motive Socioeconomic status Reason for travel Geographic region These micro segmentations enable travel businesses to market more effectively to unique consumer types.

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IBM Journey to AI blog

SEPTEMBER 11, 2023

Don Haderle, a retired IBM Fellow and considered to be the “father of Db2,” viewed 1988 as a seminal point in its development as D B2 version 2 proved it was viable for online transactional processing (OLTP)—the lifeblood of business computing at the time. Db2 (LUW) was born in 1993, and 2023 marks its 30th anniversary.

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Data Science Dojo

AUGUST 16, 2024

Applications : Business Intelligence : Power BI’s Copilot is especially valuable for business users who need to quickly derive insights from data without having extensive technical knowledge. It democratizes access to data analytics across an organization.

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IBM Journey to AI blog

JUNE 8, 2023

Essential data is not being captured or analyzed—an IDC report estimates that up to 68% of business data goes unleveraged—and estimates that only 15% of employees in an organization use business intelligence (BI) software.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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