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How to create a proper prompt for your query is very important. You can use this tool very easily to create proper #prompts to generate much better results for your query to #llms say #chatgpt or #bardai Say as an example I am asking chatgpt "Details machine learning engineer job post to hire a machine learning engineer" see the response below. ------------------------------------------------------------------------------------------------------------------------ **Job Title: Machine Learning Engineer** **Company: [Your Company Name]** **Location: [City, State] | Employment Type: [Full-time/Contract]** **About Us:** [Your Company Name] is a leading [industry/domain] company dedicated to [brief company mission or values]. With a focus on innovation and technology, we are committed to transforming [industry/domain] through cutting-edge solutions. As we continue to grow, we are seeking a talented and motivated Machine Learning Engineer to join our dynamic team. **Responsibilitie...

Power of 3-Pipeline Design in ML: Building a Financial Assistant In the realm of Machine Learning (ML), the 3-Pipeline Design has emerged as a game-changer, revolutionizing the approach to building robust ML systems. This design philosophy, also known as the Feature/Training/Inference (FTI) architecture, offers a structured way to dissect and optimize your ML pipeline. In this article, we'll delve into how this approach can be employed to craft a formidable financial assistant using Large Language Models (LLMs) and explore each pipeline's significance. What is 3-Pipeline Design? 3-Pipeline Design is a new approach to machine learning that can be used to build high-performance financial assistants. This design is based on the idea of using three separate pipelines to process and analyze financial data. These pipelines are: The data pipeline: This pipeline is responsible for collecting, cleaning, and preparing financial data for analysis. The feature engineering pipeline: This pi...