Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? What is Data Quality in Machine Learning?

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Precisely

DECEMBER 16, 2024

AIOps, or artificial intelligence for IT operations, combines AI technologies like machine learning, natural language processing, and predictive analytics, with traditional IT operations. Tool overload can lead to inefficiencies and data silos. Understanding AI Operations (AIOps) in IT Environments What is AIOps?

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Flipboard

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. To view this series from the beginning, start with Part 1.

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Dataconomy

OCTOBER 4, 2023

It also allows for decision-making by connecting existing data silos within organizations. The technologies that support systems to meet the above requirements are the Industrial Internet of Things , Machine Learning, and the public cloud.

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Dataconomy

OCTOBER 4, 2023

It also allows for decision-making by connecting existing data silos within organizations. The technologies that support systems to meet the above requirements are the Industrial Internet of Things, Machine Learning, and the public cloud.

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Dataversity

FEBRUARY 17, 2022

With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite.

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Pickl AI

SEPTEMBER 12, 2024

Introduction Machine Learning has evolved significantly, from basic algorithms to advanced models that drive today’s AI innovations. A key advancement is Federated Learning, which enhances privacy and efficiency by training models across decentralised devices. What is Federated Learning?

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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DrivenData Labs

MARCH 30, 2023

Privacy-enhancing technologies (PETs) have the potential to unlock more trustworthy innovation in data analysis and machine learning. Federated learning is one such technology that enables organizations to analyze sensitive data while providing improved privacy protections. What motivated you to participate?

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AWS Machine Learning Blog

DECEMBER 29, 2022

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Now let’s prepare a dataset that could be used for machine learning. arrange(card_brand).

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities. In such scenarios, you can use FedML Octopus.

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Flipboard

JANUARY 7, 2025

Almost half of AI projects are doomed by poor data quality, inaccurate or incomplete data categorization, unstructured data, and data silos. Avoid these 5 mistakes

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AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

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Precisely

DECEMBER 12, 2024

When integrated with AI, mainframe data has the potential to serve as a key asset in addressing one of the most persistent challenges in AIbias. Understanding Bias in AI Bias in AI arises when the data used to train machine learning models reflects historical inequalities, stereotypes, or inaccuracies.

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AWS Machine Learning Blog

MARCH 15, 2024

Medical data restrictions You can use machine learning (ML) to assist doctors and researchers in diagnosis tasks, thereby speeding up the process. However, the datasets needed to build the ML models and give reliable results are sitting in silos across different healthcare systems and organizations.

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IBM Journey to AI blog

JANUARY 30, 2024

Multicloud architecture not only empowers businesses to choose a mix of the best cloud products and services to match their business needs, but it also accelerates innovation by supporting game-changing technologies like generative AI and machine learning (ML).

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IBM Journey to AI blog

APRIL 15, 2024

Organizations gain the ability to effortlessly modify and scale their data in response to shifting business demands, leading to greater agility and adaptability. A data virtualization platform breaks down data silos by using data virtualization.

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Smart Data Collective

JUNE 3, 2019

The promise of significant and measurable business value can only be achieved if organizations implement an information foundation that supports the rapid growth, speed and variety of data. This integration is even more important, but much more complex with Big Data. Big Data is Transforming the Financial Industry.

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IBM Journey to AI blog

OCTOBER 18, 2024

Strong integration capabilities ensure smooth data flow between departments, eliminating data silos. Choose a solution that includes AI-driven insights as a core feature, enabling your business to leverage machine learning for more accurate forecasts and strategic planning. Flexibility is key.

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Precisely

JANUARY 17, 2023

Technology helped to bridge the gap, as AI, machine learning, and data analytics drove smarter decisions, and automation paved the way for greater efficiency. AI and machine learning initiatives play an increasingly important role.

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ML @ CMU

MAY 12, 2023

Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., In this post we discuss our research on federated learning , which aims to tackle this challenge by performing decentralized learning across private data silos.

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AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

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DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI.

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Precisely

AUGUST 12, 2024

Data silos Limited integration capabilities Fragmented communications Workflow problems Limited scalability The fact is, your legacy systems can create great risks for your business. A unified CXP can also provide real-time data insights, helping you understand customer preferences and behavior.

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phData

APRIL 18, 2023

This is due to a fragmented ecosystem of data silos, a lack of real-time fraud detection capabilities, and manual or delayed customer analytics, which results in many false positives. Snowflake Marketplace offers data from leading industry providers such as Axiom, S&P Global, and FactSet.

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Analytics Vidhya

AUGUST 31, 2022

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a central data repository that allows us to store all of our structured and unstructured data on a large scale.

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IBM Journey to AI blog

OCTOBER 30, 2023

The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability. By migrating to watsonx.data on AWS, companies can break down data silos and enable real-time analytics, which is crucial for timely decision-making.

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.

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IBM Journey to AI blog

MAY 9, 2023

Supporting the data management life cycle According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. [2] ” Notably, watsonx.data runs both on-premises and across multicloud environments.

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Ocean Protocol

APRIL 14, 2023

Predict NFT floor prices using machine learning to win prizes! We’re announcing our NFT Price Analysis data challenge due this April 30th, 2023 at 11:59 PM UTC! Contestants are asked to submit data analytics reports and machine learning models to analyze the floor prices of NFTs.

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Precisely

JUNE 2, 2023

Innovators in the industry understand that leading-edge technologies such as AI and machine learning will be a deciding factor in the quest for competitive advantage when moving to the cloud. To learn more, read our ebook. To learn more, read our ebook 5 Tips to Modernize Data Integration for the Cloud.

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Precisely

FEBRUARY 12, 2024

Here are some of the key trends and challenges facing telecommunications companies today: The growth of AI and machine learning: Telecom companies use artificial intelligence and machine learning (AI/ML) for predictive analytics and network troubleshooting.

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Precisely

SEPTEMBER 26, 2024

However, achieving success in AI projects isn’t just about deploying advanced algorithms or machine learning models. The real challenge lies in ensuring that the data powering your projects is AI-ready. Above all, you must remember that trusted AI starts with trusted data. The stakes are very high.

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Alation

OCTOBER 5, 2021

Without a doubt, no company can achieve lasting profitability and sustainable growth with a poorly constructed data governance methodology. Today, all companies must pursue data analytics, Machine Learning & Artificial Intelligence (ML & AI) as an integral part of any standard business plan.

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Ocean Protocol

APRIL 13, 2023

But that’s exactly why Ocean Protocol hosts regular Predict-ETH competitions — to help you sharpen your machine learning skills, learn from others, and potentially make some cash along the way. About Ocean Protocol Ocean Protocol is an ecosystem of open source data sharing tools for the blockchain.

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Women in Big Data

FEBRUARY 1, 2023

She started as a Web Analyst and Online Marketing Manager, and discovered her passion for data, Big Data, data science and machine learning. Source: One Data Laura says: “Treating data as a product creates value for businesses by shifting the focus from data being a cost center to data being a revenue generator.

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Precisely

JUNE 26, 2023

They shore up privacy and security, embrace distributed workforce management, and innovate around artificial intelligence and machine learning-based automation. The key to success within all of these initiatives is high-integrity data. Do the takeaways we’ve covered resonate with your own data integrity needs and challenges?

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