AWS Machine Learning Blog

AUGUST 8, 2024

Refine your existing application using strategic methods such as prompt engineering , optimizing inference parameters and other LookML content. You can gain granular control over the reasoning capabilities using several prompt engineering techniques. Create a simple web application using LangChain and Streamlit.

Metadata 127

Metadata LLM Prompt Engineering Prompt Engineer 127

The MLOps Blog

DECEMBER 12, 2024

If you are an enterprise or a team operating a model, focus on three key areas: fine-tune your prompts to get the most effective outputs (prompt engineering), ensure that your model behaves safely and predictably, and implement robust monitoring and logging to track performance, detecting issues early.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

deepsense.ai

JULY 30, 2023

Other steps include: data ingestion, validation and preprocessing, model deployment and versioning of model artifacts, live monitoring of large language models in a production environment, monitoring the quality of deployed models and potentially retraining them.

Large Language Models 98

Large Language Models LLM Machine Learning Automation 98

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

The MLOps Blog

MARCH 12, 2024

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. The quality and structure of prompts significantly influence LLMs’ output. using techniques like RLHF.)

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

ODSC - Open Data Science

JANUARY 10, 2024

Over the course of this session, you will develop an understanding of no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and for building, training, and deploying ML models. Sign me up!

Large Language Models 52

Large Language Models Deep Learning LLM Data Science 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

OCTOBER 23, 2024

It facilitates the seamless customization of FMs with enterprise-specific data using advanced techniques like prompt engineering and RAG so outputs are relevant and accurate. SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS).

Generative AI 88

Generative AI IDP LLM Automation 88

AWS Machine Learning Blog

DECEMBER 4, 2024

Amazon Kendra GenAI Index addresses common challenges in building retrievers for generative AI assistants, including data ingestion, model selection, and integration with various generative AI tools. Organizations can select their preferred language models, customize prompts, and manage costs through pay-per-token pricing.

Metadata 111

Metadata Generative AI Data Ingestion Software Engineer 111