A Practical Guide on Leveraging Ollama in AI Pipelines

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Since I started working in data engineering 10 years ago, implementing data transformations has always looked to be the most emphasized and significant stage in the development cycle. However, well-formatted code for data transformations is ineffective without proper validation of its outputs to ensure reliability in data pipelines.

When it comes to ensuring reliability in data pipelines, two key topics typically emerge: unit tests and data quality checks. I’m not suggesting that these are the only considerations, but I believe that if you have a solid set of unit tests and data quality checks, your data pipelines will be significantly more reliable than those lacking them.

At the same time, I must admit that working on unit tests and data quality checks is a time-consuming task. Sometimes, we do not have the capacity needed to ensure them due to various reasons, such as budget limitations, high demand for quick market entry, and others.

Thus, I want to present how you can improve the reliability of your data pipeline with AI, and even better, at a low cost. As you know, AI is becoming more integrated into code development, automating various tasks and accelerating the software development life cycle.