AWS Machine Learning Blog

AUGUST 22, 2024

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. The SageMaker Pipelines decorator feature helps convert local ML code written as a Python program into one or more pipeline steps. SageMaker Pipelines can handle model versioning and lineage tracking.

Generative AI 128

Generative AI Metadata Python ML 128

Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes lessons on vector search and text embeddings, practical demos, and a hands-on lab.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence BERT Computer Vision 131

Marktechpost

JULY 25, 2024

Large Language Models (LLMs) Concepts Difficulty Level: Beginner This course explores Large Language Models (LLMs), their impact on AI, and real-world applications. It helps learn about LLM building blocks, training methodologies, and ethical considerations.

Large Language Models 113

Large Language Models Prompt Engineer Prompt Engineering Chatbots 113

The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

ML 130

ML Machine Learning Data Scientist ETL 130

AWS Machine Learning Blog

MAY 30, 2024

Instead, Vitech opted for Retrieval Augmented Generation (RAG), in which the LLM can use vector embeddings to perform a semantic search and provide a more relevant answer to users when interacting with the chatbot. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

Chatbots 132

Chatbots Prompt Engineering Prompt Engineer Large Language Models 132

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. MLOps engineers are responsible for providing a secure environment for data scientists and ML engineers to productionize the ML use cases.

Generative AI 130

Generative AI Prompt Engineering Prompt Engineer ML 130

AWS Machine Learning Blog

OCTOBER 2, 2024

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Before you can enable invocation logging, you need to set up an Amazon Simple Storage Service (Amazon S3) or CloudWatch Logs destination.

Generative AI 127

Generative AI Data Ingestion AI AI 127

AWS Machine Learning Blog

JUNE 8, 2023

We have included a sample project to quickly deploy an Amazon Lex bot that consumes a pre-trained open-source LLM. This mechanism allows an LLM to recall previous interactions to keep the conversation’s context and pace. We also use LangChain, a popular framework that simplifies LLM-powered applications.

Generative AI 93

Generative AI LLM Machine Learning Large Language Models 93

AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker Clarify now provides AWS customers with foundation model (FM) evaluations, a set of capabilities designed to evaluate and compare model quality and responsibility metrics for any LLM, in minutes. FMEval helps in measuring evaluation dimensions such as accuracy, robustness, bias, toxicity, and factual knowledge for any LLM.

Large Language Models 115

Large Language Models Algorithm LLM Responsible AI 115

Towards AI

APRIL 12, 2025

Traditional implementations often bloat prompts with extensive function metadata, consuming valuable token space and potentially overwhelming smaller language models. The crucial difference in this function calling approach lies in avoiding embedded function descriptions within the context window.

Explainability 66

Explainability Metadata ML Engineer Machine Learning 66

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

Data Scientist 52

Data Scientist Machine Learning Large Language Models ML Engineer 52

TheSequence

JULY 12, 2023

At Cruise, we noticed a wide gap between the complexity of cloud infrastructure, and the needs of the ML workforce. ML Engineers want to focus on writing Python logic, and visualizing the impact of their changes quickly. However, what is going to emerge are tools around LLM orchestration.

ML 97

ML Python Machine Learning Metadata 97

AWS Machine Learning Blog

NOVEMBER 29, 2023

However, harnessing this potential while ensuring the responsible and effective use of these models hinges on the critical process of LLM evaluation. An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Who needs to perform LLM evaluation?

LLM 115

LLM Data Scientist Algorithm Generative AI 115

Flipboard

NOVEMBER 27, 2024

The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. The following diagram illustrates the end-to-end architecture, consisting of the metadata API layer, ingestion pipeline, embedding generation workflow, and frontend UI.

Metadata 149

Metadata Auto-complete Data Discovery ML Engineer 149

AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

Metadata 106

Metadata Generative AI LLM Data Ingestion 106

ODSC - Open Data Science

MARCH 11, 2025

The second script shows how to query those embeddings with an LLM for RAG-based Q&A. This quick workflow lets you maintain a powerful, scalable knowledge base for any LLM-powered application. The first script ingests text data, chunks it, creates embeddings, and writes them to Neo4j. This is for demonstration purposesonly! . """

LLM 52

LLM Metadata Software Engineer Python 52

Marktechpost

APRIL 10, 2025

Bio: Hamza Tahir is a software developer turned ML engineer. Based on his learnings from deploying ML in production for predictive maintenance use-cases in his previous startup, he co-created ZenML , an open-source MLOps framework for creating production grade ML pipelines on any infrastructure stack.

Software Engineer 48

Software Engineer LLM ML Prompt Engineering 48