Ocean Protocol

MARCH 8, 2023

Launched on February 1st 2023, the contestants of our Air Quality challenge were asked to use Ocean.py’s open-source tool, Compute-to-Data, to publish predictions of air pollutant concentrations in a fully decentralized manner. Contestants also submitted a report about their algorithmic approach to predictions.

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AWS Machine Learning Blog

MARCH 15, 2024

Additionally, locally trained information can expose private data if reconstructed through an inference attack. To mitigate these risks, the FL model uses personalized training algorithms and effective masking and parameterization before sharing information with the training coordinator. In such scenarios, you can use FedML Octopus.

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Dataversity

OCTOBER 25, 2023

We exist in a diversified era of data tools up and down the stack – from storage to algorithm testing to stunning business insights.

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Ocean Protocol

MARCH 6, 2023

Launched in January 2023, contestants of the ETH price prediction data challenge were asked to engage with the Ocean.py This challenge aimed to activate relevant communities of Web3-native data scientists and guide them towards potential use cases such as community-owned algorithms via data NFTs and DeFi protocol design.

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Ocean Protocol

JANUARY 5, 2023

The Ocean Protocol — Gitcoin DataBuilder hackathon seeks to protect public goods funding and data decentralization by encouraging data scientists around the globe to develop insightful anti-Sybil algorithms and implement them into composable legos while using end-to-end decentralized technologies.

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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. This holistic view empowers businesses to make data-driven decisions, optimize processes and gain a competitive edge.

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IBM Journey to AI blog

OCTOBER 30, 2023

Real-time data analytics helps in quick decision-making, while advanced forecasting algorithms predict product demand across diverse locations. AWS’s scalable infrastructure allows for rapid, large-scale implementation, ensuring agility and data security.

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Becoming Human

MARCH 16, 2023

AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. 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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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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Dataconomy

APRIL 27, 2023

The role of digit-computers in the digital age Handle multi-user access & data integrity OLTP systems must be able to handle multiple users accessing the same data simultaneously while ensuring data integrity. Building in these characteristics at a later stage can be costly and resource-intensive.

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Ocean Protocol

DECEMBER 27, 2022

Launched in November 2022, contestants of the ETH price prediction data challenge were asked to engage with Ocean.py This challenge aimed to activate relevant communities of Web3-native data scientists and guide them towards potential use cases such as community-owned algorithms via data NFTs and DeFi protocol design.

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Pickl AI

DECEMBER 3, 2024

Current Challenges in Data Analytics Despite the advancements in Data Analytics technologies, organisations face several challenges: Data Quality: Inconsistent or incomplete data can lead to inaccurate insights. Poor-quality data hampers decision-making and can result in significant financial losses.

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Dataconomy

JULY 19, 2023

Assess the uniqueness and viability of the AI algorithms being used, as well as their potential applications in real-world scenarios. Its privacy-preserving features make it ideal for applications that require sensitive data, such as healthcare and financial services. Data security : Data can be vulnerable to security risks.

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DrivenData Labs

MARCH 30, 2023

Modeling ¶ Most teams experimented with a variety of modeling algorithms, and many noted that the privacy techniques in their solutions could be paired with more than one family of machine learning models. We are excited to take on this challenge and continue pushing the boundaries of machine learning research.

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Alation

OCTOBER 5, 2021

Yet there is often lack of awareness of the trustworthiness (or lack thereof) of the data that these algorithms are being trained on. It is commonplace for a company to create an enterprise data governance strategy that fails to even consider the end user. Roadblock #3: Silos Breed Misunderstanding.

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IBM Journey to AI blog

OCTOBER 13, 2023

In addition, digital transformation initiatives have created the proliferation of applications, creating data siloes. It can allow you to empower users from all areas of your business to identify and act on situations in the moment, helping them avoid getting lost in algorithms, heavy code, or disparate data sources.

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Women in Big Data

FEBRUARY 1, 2023

She goes on to explain the one of the most beneficial features of One Data’s enabling technology, One Data Cartography , is record linkage combined with data quality. This feature enables holistic and seamless data tracking across system boundaries, based on algorithms and automatic checks for quality anomalies.

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ODSC - Open Data Science

MAY 17, 2023

In other cases, the details of why an AI algorithm reached a certain outcome are not publicly available because the companies that developed them consider the information proprietary. Critics warn such algorithms could erode democracy. That’s often called the “black-box problem.”

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Alation

MAY 2, 2023

Some of the key benefits of this include: Simplified data governance Data governance and data analytics support each other, and a strong data governance strategy is integral to ensuring that data analytics are reliable and actionable for decision-makers.

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Pickl AI

DECEMBER 11, 2024

Efficiency emphasises streamlined processes to reduce redundancies and waste, maximising value from every data point. Common Challenges with Traditional Data Management Traditional data management systems often grapple with data silos, which isolate critical information across departments, hindering collaboration and transparency.

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Precisely

FEBRUARY 12, 2024

They use advanced algorithms to proactively identify and resolve network issues, reducing downtime and improving service to their subscribers. Location-based data is often subject to additional regulatory requirements as well, further adding to the challenges of spatial data governance.

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Ocean Protocol

APRIL 13, 2023

About Ocean Protocol Ocean Protocol is an ecosystem of open source data sharing tools for the blockchain. Ocean Protocol is spearheading the movement to unlock a New Data Economy in Web3 by breaking down data silos and opening access to high quality data.

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phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

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Dataconomy

MAY 31, 2023

Integration capabilities allow businesses to connect their SolaaS solution with their existing software ecosystem, ensuring smooth data exchange and eliminating data silos. Together, humans and algorithms collaborate to provide an effective and enduring solution.

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Pickl AI

DECEMBER 4, 2023

So, what is Data Intelligence with an example? For example, an e-commerce company uses Data Intelligence to analyze customer behavior on their website. Through advanced analytics and Machine Learning algorithms, they identify patterns such as popular products, peak shopping times, and customer preferences.

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ODSC - Open Data Science

JULY 13, 2023

For example, feeding an algorithm statistics about consumer purchasing behavior from stores in one location might lead to poor optimization in another because the data might not be applicable. A retailer must connect data silos across the entire organization for proper consolidation. The underlying issue is quality.

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Alation

MAY 2, 2023

Some of the key benefits of this include: Simplified data governance Data governance and data analytics support each other, and a strong data governance strategy is integral to ensuring that data analytics are reliable and actionable for decision-makers.

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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. It also enables models to be trained on diverse data sources, potentially leading to better generalisation and performance. Examples include Google’s predictive text and healthcare applications.

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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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Flipboard

FEBRUARY 12, 2025

This means figuring out the best result out of many possible outcomes, which is almost impossible to hardcode in an RPA algorithm with classical automation methods. Agents will be more adaptable and robust than conventional robotic process automation (RPA) for longtail and highly extensive tasks.

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The MLOps Blog

MAY 17, 2023

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

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Pickl AI

DECEMBER 10, 2024

Unified Data Fabric Unified data fabric solutions enable seamless access to data across diverse environments, including multi-cloud and on-premise systems. These solutions break down data silos, making it easier to integrate and analyse data from various sources in real-time.

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Alation

AUGUST 2, 2022

The problem many companies face is that each department has its own data, technologies, and information handling processes. This causes data silos to form, which can inhibit data visibility and collaboration, and lead to integrity issues that make it harder to share and use data.

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Pickl AI

JULY 16, 2024

User Experience Monitoring (UEM): User behavior data, application responsiveness Event Correlation and Anomaly Detection The collected data is a massive stream of events. It employs sophisticated algorithms to identify patterns, trends, and anomalies that deviate from established baselines.

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Alation

NOVEMBER 16, 2021

Data growth, shrinking talent pool, data silos – legacy & modern, hybrid & cloud, and multiple tools – add to their challenges. According to Gartner, “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.”.

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Pickl AI

JUNE 13, 2024

Rules Engine This is the brain of the CDSS, employing complex algorithms to analyze patient data against the knowledge base. The rules engine acts like a tireless medical consultant, constantly evaluating patient data and recommending the most suitable course of action based on the latest medical knowledge.

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Ocean Protocol

FEBRUARY 1, 2023

Participants are encouraged to use Ocean Protocol’s technology to decentralize access to their algorithms, and share their solutions with privacy-preserving technology. Catalonia) over the past three decades and develop algorithms to predict air pollutant concentrations. The submission deadline is February 14 @ 23:59 PM UTC.

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