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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...

 Here are a few screens shared from the AWSome Day online conference.

  Photo by cottonbro studio Generative AI and large language models (LLMs) can be used at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. Generative AI and LLMs in the Financial Sector Generative AI and LLMs have the potential to revolutionize the financial sector by automating many of the manual tasks that are currently performed by analysts, freeing up their time to focus on more strategic work. Additionally, these technologies can provide new insights into financial data that can help companies make better decisions. Examples of Generative AI and LLM Applications in Finance Automated financial reporting: Generative AI can be used to generate financial reports automatically, saving analysts time and effort. LLMs can be used to analyze financial data and generate insights that can be used to improve decision-making. Fraud detection: Generative AI can be used to identify patterns in fin...