Our main use case for AI and ML Development involves a major task in the processing of data that comes in every month. To automate that task, we use AI, ML, and data science techniques to make the query and the models, enabling a non-technical person to run the query and model with just the input file and output file name. The task involves a huge amount of data that comes in every month to process. To automate that task, we use AI, ML, and data science techniques to make the query, to make the models, and to automate that process so that a non-technical person can run the query and get the update with just the input file and output file name. I come up with this approach by first looking into the new data and the columns present in it, then making the approach that can be solved and implementing the code or script, trying it with demo data first, and when it works fine, using the real data to make the query. After I got access to Databricks in the company, the heavy tasks became easier to run and do by using AI, Gemini, and other tools. Tasks that previously took one to three hours can now be completed in only half an hour or 30 minutes.
My main use case for AI and ML Development has been building a RAG pipeline to reduce hallucinations in LLM responses. We used FAISS for vector storage, LangChain for orchestration, and integrated it with a FastAPI backend. The result was a 30% reduction in the hallucination rate, which directly improved the reliability of AI-generated responses in production. This use case perfectly demonstrates the power of modern AI and ML Development tools, combining multiple frameworks seamlessly to solve a real-world problem.
Our main use case for AI and ML Development involves a major task in the processing of data that comes in every month. To automate that task, we use AI, ML, and data science techniques to make the query and the models, enabling a non-technical person to run the query and model with just the input file and output file name. The task involves a huge amount of data that comes in every month to process. To automate that task, we use AI, ML, and data science techniques to make the query, to make the models, and to automate that process so that a non-technical person can run the query and get the update with just the input file and output file name. I come up with this approach by first looking into the new data and the columns present in it, then making the approach that can be solved and implementing the code or script, trying it with demo data first, and when it works fine, using the real data to make the query. After I got access to Databricks in the company, the heavy tasks became easier to run and do by using AI, Gemini, and other tools. Tasks that previously took one to three hours can now be completed in only half an hour or 30 minutes.
My main use case for AI and ML Development has been building a RAG pipeline to reduce hallucinations in LLM responses. We used FAISS for vector storage, LangChain for orchestration, and integrated it with a FastAPI backend. The result was a 30% reduction in the hallucination rate, which directly improved the reliability of AI-generated responses in production. This use case perfectly demonstrates the power of modern AI and ML Development tools, combining multiple frameworks seamlessly to solve a real-world problem.