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Succeeding with generative AI requires the expertise to design new use cases and the ability to develop and operationalize genAI models. These are major challenges.
TensorFlow is a Python-friendly open source library for developing machine learning applications and neural networks. Here's what you need to know about TensorFlow.
Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications.
SQL is a convenient way to manage and query your data, but badly written queries can tie up your database. Here are seven common SQL traps and how to avoid them.
There is no universal ‘best’ vector database—the choice depends on your needs. Evaluating scalability, functionality, performance, and compatibility with your use cases is vital.
Event-driven architectures are wonderful. But Kafka was never intended to be a database, and using it as a database won’t solve your problem.
Pandas makes it easy to quickly load, manipulate, align, merge, and even visualize data tables directly in Python.
Survey of enterprise users of generative AI finds rapid adoption but also hurdles, with difficulty finding business use cases, legal uncertainties, and high infrastructure costs top concerns.
The Apache Arrow in-memory columnar format has become a critical component of many analytical database systems and tools. It brings a number of advantages to InfluxDB.
By making cryptic machine data human readable, generative AI will dramatically reduce the time and energy IT teams spend on managing and interpreting data generated by operational systems.
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