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5 Pitfalls to Avoid When Developing AI Tools Developing a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of. |
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Benefits of Using Columnar Storage in Relational Database Management Systems Relational database management systems (RDBMS) store data in rows and columns. Most relational databases store data row-wise by default, but a few RDBMS provide the option to store data column-wise, which is a useful feature. Let’s look at the benefits of being able to use columnar storage for data and when you'd want to. |
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Choosing the Right Threat Modeling Methodology Threat modeling has transitioned from a theoretical concept into an IT security best practice. Choosing the right methodology is a combination of finding what works for your SDLC maturity and ensuring it results in the desired outputs. Let’s look at four different methodologies and assess their strengths and weaknesses. |