Remove Artificial Intelligence Remove Data Pipeline Remove SQL
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Securing the data pipeline, from blockchain to AI

Dataconomy

Generative artificial intelligence is the talk of the town in the technology world today. These challenges are primarily due to how data is collected, stored, moved and analyzed. With most AI models, their training data will come from hundreds of different sources, any one of which could present problems.

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The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

Data engineers build data pipelines, which are called data integration tasks or jobs, as incremental steps to perform data operations and orchestrate these data pipelines in an overall workflow. With a multicloud data strategy, organizations need to optimize for data gravity and data locality.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning Blog

OMRONs data strategyrepresented on ODAPalso allowed the organization to unlock generative AI use cases focused on tangible business outcomes and enhanced productivity. This tool democratizes data access across the organization, enabling even nontechnical users to gain valuable insights.

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The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning Blog

Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems. Given the data sources, LLMs provided tools that would allow us to build a Q&A chatbot in weeks, rather than what may have taken years previously, and likely with worse performance.

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

With all this packaged into a well-governed platform, Snowflake continues to set the standard for data warehousing and beyond. Snowflake supports data sharing and collaboration across organizations without the need for complex data pipelines.