ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: MLOps Fundamentals LLM Deployment & Monitoring Cloud Infrastructure forLLMs Observability & Cost Management Operationalizing Local LLMs Responsibly Who Should Attend: MLOps Engineers, Data Scientists, and AI Developers responsible for deploying AIsystems.

Data Scientist 52

Data Scientist Machine Learning Large Language Models ML Engineer 52

AWS Machine Learning Blog

AUGUST 8, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral, Stability AI, and Amazon within a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Metadata 118

Metadata LLM Prompt Engineering Prompt Engineer 118

ODSC - Open Data Science

MARCH 24, 2023

6 Characteristics of Companies That are Successfully Building AI In this article, we touch on the six most common characteristics of companies that are successfully building AI, and what we can learn from them. Register by Friday for 50% off.

Data Science 52

Data Science Machine Learning Data Ingestion NLP 52

ODSC - Open Data Science

JANUARY 10, 2024

These concerns include lack of interpretability, bias, and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. Sign me up!

Large Language Models 52

Large Language Models Deep Learning LLM Data Science 52

ODSC - Open Data Science

SEPTEMBER 17, 2024

This talk will also cover the implementation of the RAISE framework, which stands for Responsible AI Security Engineering, designed to provide a step-by-step approach to building secure and resilient AI systems.

LLM 52

LLM Large Language Models Metadata 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

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow. These include: 1.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

Bugra Akyildiz

FEBRUARY 24, 2024

Responsible AI Development: Phi-2 highlights the importance of considering responsible development practices when building large language models. Increased Democratization: Smaller models like Phi-2 reduce barriers to entry, allowing more developers and researchers to explore the power of large language models.

Large Language Models 52

Large Language Models LLM Data Ingestion Neural Network 52

Flipboard

NOVEMBER 27, 2024

To demonstrate, we create a generative AI-enabled Slack assistant with an integration to Amazon Bedrock Knowledge Bases that can expose the combined knowledge of the AWS Well-Architected Framework while implementing safeguards and responsible AI using Amazon Bedrock Guardrails.

Generative AI 142

Generative AI Data Ingestion AI AI 142

Flipboard

NOVEMBER 26, 2024

Implement the solution The following illustrates the solution architecture: Architecture Diagram for Custom Hallucination Detection and Mitigation The overall workflow involves the following steps: Data ingestion involving raw PDFs stored in an Amazon Simple Storage Service (Amazon S3) bucket synced as a data source with.

Large Language Models 153

Large Language Models LLM Natural Language Processing Responsible AI 153

The MLOps Blog

MARCH 21, 2023

Responsible AI and explainability. ML metadata and artifact repository Your data scientists can manually build and test models that you deploy to the production environment. Responsible AI and explainability component To fully trust ML systems, it’s important to interpret these predictions. Model serving.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

NOVEMBER 14, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Metadata 88

Metadata Data Ingestion Generative AI Computer Vision 88

AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

Metadata 101

Metadata Generative AI LLM Data Ingestion 101