AWS Machine Learning Blog

MAY 7, 2024

This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain. Prompt engineering Prompt engineering enables you to instruct LLMs to generate suggestions, explanations, or completions of text in an interactive way.

Prompt Engineer 115

Prompt Engineer Prompt Engineering Large Language Models LLM 115

Flipboard

DECEMBER 6, 2023

Considering the nature of the time series dataset, Q4 also realized that it would have to continuously perform incremental pre-training as new data came in. This would have required a dedicated cross-disciplinary team with expertise in data science, machine learning, and domain knowledge.

Chatbots 168

Chatbots LLM Prompt Engineer Prompt Engineering 168

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Generative AI 122

Generative AI Prompt Engineer Prompt Engineering ML 122

ODSC - Open Data Science

JULY 26, 2024

You’ll also be introduced to prompt engineering, a crucial skill for optimizing AI interactions. You’ll explore data ingestion from multiple sources, preprocessing unstructured data into a normalized format that facilitates uniform chunking across various file types, and metadata extraction.

Data Scientist 52

Data Scientist Robotics Metadata Data Science 52

AWS Machine Learning Blog

JULY 3, 2024

We use prompt engineering to send our summarization instructions to the LLM. Importantly, when performed, summarization should preserve as much article’s metadata as possible, such as the title, authors, date, etc. Data Scientist with 8+ years of experience in Data Science and Machine Learning.

Generative AI 122

Generative AI LLM Large Language Models Machine Learning 122

ODSC - Open Data Science

OCTOBER 6, 2023

Be sure to check out his talk, “ Prompt Optimization with GPT-4 and Langchain ,” there! The difference between the average person using AI and a Prompt Engineer is testing. Most people run a prompt 2–3 times and find something that works well enough.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Python Data Science 52

John Snow Labs

OCTOBER 16, 2023

.” – Zain Hasan, Senior ML Developer Advocate at Weaviate “An under-discussed yet crucial question is how to ensure that LLMs can be trained in a way that respects user privacy and does not rely on exploiting vast amounts of personal data.” solves this problem by extracting metadata during the data preparation process.

Large Language Models 52

Large Language Models NLP Metadata Data Scarcity 52

ODSC - Open Data Science

JULY 12, 2024

The fields of AI and data science are changing rapidly and ODSC West 2024 is evolving to ensure we keep you at the forefront of the industry with our all-new tracks, AI Agents , What’s Next in AI, and AI in Robotics , and our updated tracks NLP, NLU, and NLG , and Multimodal and Deep Learning , and LLMs and RAG.

NLP 40

NLP Robotics Deep Learning Large Language Models 40

AWS Machine Learning Blog

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

Auto-classification 128

Auto-classification LLM Prompt Engineer Prompt Engineering 128

Heartbeat

SEPTEMBER 19, 2023

Comet’s LLMOps tool provides an intuitive and responsive view of our prompt history. Prompt Playground: With the LLMOps tool comes the new Prompt Playground, which allows Prompt Engineers to iterate quickly with different Prompt Templates and understand the impact on different contexts.

Large Language Models 52

Large Language Models Metadata LLM Data Scientist 52

deepsense.ai

JULY 30, 2023

Preceded by data analysis and feature engineering, a model is trained and ready to be productionized. We may observe a growing awareness among machine learning and data science practitioners of the crucial role played by pre- and post-training activities. But what happens next? What is LLMOps?

Large Language Models 98

Large Language Models LLM Machine Learning Automation 98

Heartbeat

FEBRUARY 8, 2024

LangChain loads training data in the form of ‘Documents’, and these contain text from various sources and their respective metadata. The `Document` class provides various document loader methods to load data from sources, including PDF, HTML, JSON, and CSV. The rest are optional like prompt template, metadata, tags, etc.

Conversational AI 52

Conversational AI Chatbots LLM Prompt Engineer 52

Heartbeat

OCTOBER 19, 2023

Users can gain deep insights into the evolution of their prompts and responses. Prompt Playground Adventure: Within the LLMOps toolkit lies the innovative Prompt Playground, a dynamic space where Prompt Engineers can embark on expedited explorations. comet_llm.log_prompt( prompt="Hey,What's your name?

Large Language Models 52

Large Language Models Metadata LLM Deep Learning 52

Heartbeat

NOVEMBER 24, 2023

Document Loaders Document loaders in LangChain are used to load data from various sources as Document objects. A Document is a piece of text with associated metadata. Document loaders provide a convenient way to fetch data from different sources, such as text files, web pages, or even transcripts of videos.

Deep Learning 52

Deep Learning Metadata Data Scientist OpenAI 52

Heartbeat

NOVEMBER 22, 2023

metadata : (Optional) Additional metadata to associate with the evaluation. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. kwargs : Additional keyword arguments.

LLM 52

LLM Explainability Metadata Deep Learning 52

AWS Machine Learning Blog

APRIL 16, 2024

Text to SQL: Using natural language to enhance query authoring SQL is a complex language that requires an understanding of databases, tables, syntaxes, and metadata. The way you craft a prompt can profoundly influence the nature and usefulness of the AI’s response. Bosco Albuquerque is a Sr.

Data Scientist 109

Data Scientist Generative AI Machine Learning ML 109

Heartbeat

DECEMBER 13, 2023

', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, handle_tool_error=False, api_wrapper=WikipediaAPIWrapper(wiki_client=<module 'wikipedia' from '/usr/local/lib/python3.10/dist-packages/wikipedia/__init__.py'>, Input should be a search query.',

LLM 52

LLM Metadata Deep Learning Prompt Engineer 52

Heartbeat

DECEMBER 15, 2023

Even if the text is cleaner, other aspects, like how the loader interfaces with the language model, the handling of metadata, and the overall synergy with the RAG process, are crucial. We’re committed to supporting and inspiring developers and engineers from all walks of life.

AI Chatbots 52

AI Chatbots LLM Chatbots NLP 52

Heartbeat

NOVEMBER 8, 2023

This gives you access to metadata like the number of tokens used. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

LLM 52

LLM OpenAI Data Scientist Prompt Engineer 52

The MLOps Blog

AUGUST 3, 2023

As you’ve been running the ML data platform team, how do you do that? How do you know whether the platform we are building, the tools we are providing to data science teams, or data teams are bringing value? If you can be data-driven, that is the best. Depending on your size, you might have a data catalog.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

deepsense.ai

MAY 26, 2023

We used Streamlit , a library for quickly building graphical interfaces for data science projects – it does not allow a high level of customization, but it allows you to quickly present visual results which greatly simplifies communication, especially with less technically-oriented people.

OpenAI 52

OpenAI Neural Network Prompt Engineer Prompt Engineering 52

Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows.

ETL 157

ETL Prompt Engineer Prompt Engineering Generative AI 157

AWS Machine Learning Blog

FEBRUARY 3, 2025

By using a combination of transcript preprocessing, prompt engineering, and structured LLM output, we enable the user experience shown in the following screenshot, which demonstrates the conversion of LLM-generated timestamp citations into clickable buttons (shown underlined in red) that navigate to the correct portion of the source video.

UX Design 90

UX Design Auto-complete LLM Prompt Engineer 90