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Build a strong data foundation for AI-driven business growth

CIO

In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.

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What is data architecture? A framework to manage data

CIO

Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. It includes data collection, refinement, storage, analysis, and delivery. Cloud storage. AI and machine learning models.

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Are enterprises ready to adopt AI at scale?

CIO

This requires greater flexibility in systems to better manage data storage and ensure quality is maintained as data is fed into new AI models. As data is moved between environments, fed into ML models, or leveraged in advanced analytics, considerations around things like security and compliance are top of mind for many.

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9 IT skills where expertise pays the most

CIO

AI skills broadly include programming languages, database modeling, data analysis and visualization, machine learning (ML), statistics, natural language processing (NLP), generative AI, and AI ethics. As one of the most sought-after skills on the market right now, organizations everywhere are eager to embrace AI as a business tool.

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Simplifying machine learning lifecycle management

O'Reilly Media - Data

In this episode of the Data Show , I spoke with Harish Doddi , co-founder and CEO of Datatron , a startup focused on helping companies deploy and manage machine learning models. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies.

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Unlocking the full potential of enterprise AI

CIO

These narrow approaches also exacerbate data quality issues, as discrepancies in data format, consistency, and storage arise across disconnected teams, reducing the accuracy and reliability of AI outputs. Reliability and security is paramount. Without the necessary guardrails and governance, AI can be harmful.

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How to take machine learning from exploration to implementation

O'Reilly Media - Data

Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Scalable Machine Learning for Data Cleaning.