Unite.AI

AUGUST 28, 2024

This blog will explore the five challenges in implementing AI in healthcare, their solutions, and their benefits. Challenges of Using AI in Healthcare Physicians, doctors, nurses, and other healthcare providers face many challenges integrating AI into their workflows, from displacement of human labor to data quality issues.

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IBM Journey to AI blog

JANUARY 15, 2024

This partnership establishes a benchmark for digital transformation in the insurance industry, promoting innovation and achieving cost efficiency through AI-powered business automation. The office sought a practical solution for process optimization and automation to address the aforementioned issues.

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IBM Journey to AI blog

MAY 9, 2024

At the same time, implementing a data governance framework poses some challenges, such as data quality issues, data silos security and privacy concerns. Data quality issues Positive business decisions and outcomes rely on trustworthy, high-quality data.

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ETL Data Quality Automation Metadata 213

Uber AI

FEBRUARY 23, 2023

Data quality is of paramount importance at Uber, powering critical decisions and features. In this blog learn how we automated column-level drift detection in batch datasets at Uber scale, reducing the median time to detect issues in critical datasets by 5X.

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AWS Machine Learning Blog

JULY 11, 2024

This allows customers to further pre-train selected models using their own proprietary data to tailor model responses to their business context. The quality of the custom model depends on multiple factors including the training data quality and hyperparameters used to customize the model. AWS Step Functions User Guide.

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IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

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IBM Journey to AI blog

OCTOBER 21, 2024

million annually due to poor data quality. This is where data intelligence solutions prove their worth, by helping to ensure that data is not only abundant but also precise, accessible and governed by quality standards. However, diving into analytics without clear direction can lead to confusion and inefficiency.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

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IBM Journey to AI blog

APRIL 20, 2023

Before a bank can start the process of certifying a risk model, they first need to understand what data is being used and how it changes as it moves from a database to a model. With an accurate view of the entire system, banks can more easily track down issues like missing or inconsistent data.

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IBM Journey to AI blog

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

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IBM Journey to AI blog

JANUARY 8, 2024

Event triggers are used to automate workflows or decisions, allowing businesses to generate notifications so appropriate actions can be taken as swiftly as situations are detected. Flexible and customizable Kafka configurations can be automated by using a simple user interface. Ready to take the next step?

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IBM Journey to AI blog

NOVEMBER 24, 2023

Many enterprises are realizing that moving to cloud is not giving them the desired value nor agility/speed beyond basic platform-level automation. Generative AI-based Solution Approach : The Mule API to Java Spring boot modernization was significantly automated via a Generative AI-based accelerator we built.

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IBM Journey to AI blog

JULY 25, 2023

In reviewing IBM’s capabilities in ESG Reporting and Data Management, the Verdantix report notes that IBM has strengths in: Data quality control and enhancement, including “easy-to-understand reports to better analyze data quality.

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Unite.AI

SEPTEMBER 16, 2024

Data integration tools can simplify this process, allowing businesses to streamline workflows and gain more actionable insights faster. This blog will explore the benefits of data integration tools, the different types available, and the best tools to consider in 2024. Fivetran also provides robust data security and governance.

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IBM Journey to AI blog

APRIL 25, 2024

.” Shantha Farris, Global Digital Commerce Strategy and Offering Leader at IBM iX The vast amounts of data businesses collect, combined with external data sources, can be used to present cross-selling and upselling opportunities that genuinely appeal to customers.

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IBM Journey to AI blog

JANUARY 25, 2024

Generative AI has the potential to deliver powerful support in key data areas: Master data cleansing to reduce duplications and flag outliers. Master data enrichment to enhance categorization and materials attributes. Master data quality to improve scoring, prioritization and automated validation of data.

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Unite.AI

SEPTEMBER 16, 2024

Data integration tools can simplify this process, allowing businesses to streamline workflows and gain more actionable insights faster. This blog will explore the benefits of data integration tools, the different types available, and the best tools to consider in 2024. Fivetran also provides robust data security and governance.

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IBM Journey to AI blog

SEPTEMBER 15, 2023

Key challenges in meeting Sustainability disclosure requirements For most companies today, meeting this wide range of requirements will force accelerating the maturity level of ESG data quality, data management, and governance controls, as well as streamlining the ESG reporting processes to ensure auditability.

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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Chatbots Life

MAY 19, 2023

Automation rules today’s world. A chatbot is a technological genie that uses intelligent automation, ML, and NLP to automate tasks. It adds a digital flavor by automating your day-to-day IT tasks to help businesses work smarter. Modern service desks offer an automated ticketing system for staff.

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Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

This improvement will lead to the automation of low-level tasks and the augmentation of human abilities, enabling workers to accomplish more with greater proficiency. These include so-called small language models and non-generative models, such as forecasting models , which require a narrower data set.

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IBM Journey to AI blog

OCTOBER 14, 2024

Internally, we’ve implemented a marketing-automation use case where IBM’s brand guidelines and examples were ingested to generate new marketing content and curate it for consistent quality and tone. Similarly, the proliferation of agents will infuse data into an exploding volume and variety of automated workflows.

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IBM Journey to AI blog

AUGUST 12, 2024

The integration of generative AI, particularly LLMs, offers transformative potential to automate compliance processes, detect anomalies, and provide comprehensive insights into regulatory requirements. RAG implementations involve combining LLMs with external data sources to enhance their knowledge and decision-making capabilities.

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DataRobot Blog

MAY 10, 2022

In this post, we will dive deeper into the first component of managing model risk, and look at opportunities at how automation provided by DataRobot brings about efficiencies in the development and implementation of models. . Developing Robust Machine Learning Models within a MRM Framework. To reference SR 11-7: .

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IBM Journey to AI blog

JUNE 20, 2024

It provides seamless workflows that automate risk assessments and model approval processes, simplifying regulatory compliance. It automates compliance checks and maintains audit trails, enhancing regulatory adherence. Data security and privacy Ensuring the security and privacy of data used in AI models is crucial.

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IBM Journey to AI blog

DECEMBER 20, 2023

The automation of tasks that traditionally relied on human intelligence has far-reaching implications, creating new opportunities for innovation and enabling businesses to reinvent their operations. Explore AI consulting services Explore watsonx The post How to build a successful AI strategy appeared first on IBM Blog.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution.

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IBM Journey to AI blog

DECEMBER 7, 2023

Automated data preparation and cleansing : AI-powered data preparation tools will automate data cleaning, transformation and normalization, reducing the time and effort required for manual data preparation and improving data quality.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

To quickly explore the loan data, choose Get data insights and select the loan_status target column and Classification problem type. The generated Data Quality and Insight report provides key statistics, visualizations, and feature importance analyses.

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IBM Journey to AI blog

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture. Data governance. The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. Start a trial.

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IBM Journey to AI blog

AUGUST 23, 2023

If you add in IBM data governance solutions, the top left will look a bit more like this: The data governance solution powered by IBM Knowledge Catalog offers several capabilities to help facilitate advanced data discovery, automated data quality and data protection.

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IBM Journey to AI blog

JULY 3, 2024

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

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IBM Journey to AI blog

OCTOBER 12, 2023

Worldwide IDC Global DataSphere Forecast, 2022–2026: Enterprise Organizations Driving Most of the Data Growth, May 2022 The post How data stores and governance impact your AI initiatives appeared first on IBM Blog.

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