Marktechpost

JUNE 19, 2024

Introduction to Generative AI Learning Path Specialization This course offers a comprehensive introduction to generative AI, covering large language models (LLMs), their applications, and ethical considerations. The learning path comprises three courses: Generative AI, Large Language Models, and Responsible AI.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI.

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AWS Machine Learning Blog

JUNE 19, 2024

About the Authors Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. He helps customers implement big data, machine learning, analytics solutions, and generative AI solutions. Outside of work, he enjoys spending time with family, reading, running, and playing golf.

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Towards AI

JANUARY 6, 2025

AI engineering professional certificate by IBM AI engineering professional certificate from IBM targets fundamentals of machine learning, deep learning, programming, computer vision, NLP, etc. You shall build competence in NLP solutions, programming languages, responsible AI principles, etc.

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AWS Machine Learning Blog

OCTOBER 2, 2024

It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral 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.

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AWS Machine Learning Blog

MARCH 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, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

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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.

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AWS Machine Learning Blog

AUGUST 4, 2023

SageMaker Projects provides a straightforward way to set up and standardize the development environment for data scientists and ML engineers to build and deploy ML models on SageMaker. With her customer-first approach, Mani helps strategic customers shape their AI/ML strategy, fuel innovation, and accelerate their AI/ML journey.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. His experience extends across different areas, including natural language processing, generative AI and machine learning operations. Laurens van der Maas is a Machine Learning Engineer at AWS Professional Services.

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AWS Machine Learning Blog

OCTOBER 15, 2024

Bria’s commitment to responsible AI and the robust security framework of SageMaker provide enterprises with the full package for data privacy, regulatory compliance, and responsible AI models for commercial use. About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria.

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AWS Machine Learning Blog

MAY 13, 2024

This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. He has two graduate degrees in physics and a doctorate in engineering.

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The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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AWS Machine Learning Blog

AUGUST 1, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API.

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ODSC - Open Data Science

JANUARY 10, 2024

In this talk, you’ll explore the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, and provide a roadmap for thinking about responsible AI for generative AI in practice through real-world LLM use cases.

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Large Language Models LLM Deep Learning Data Science 52

Pickl AI

JULY 14, 2023

Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable. Ethical considerations, such as bias mitigation, privacy protection, and responsible AI deployment, gained prominence. The average salary of a ML Engineer per annum is $125,087.

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Viso.ai

FEBRUARY 12, 2024

By understanding what goes under the hood with Explainable AI, data teams are better equipped to improve and maintain model performance, and reliability. Error Detection and Debugging: A major challenge ML engineers face is debugging complex models with millions of parameters.

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Flipboard

NOVEMBER 28, 2023

An important next step of the AI system risk assessment is to identify potentially harmful events associated with the use case. In considering these events, it can be helpful to reflect on different dimensions of responsible AI, such as fairness and robustness, for example.

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AWS Machine Learning Blog

MAY 9, 2024

Governance Establish governance that enables the organization to scale value delivery from AI/ML initiatives while managing risk, compliance, and security. Additionally, pay special attention to the changing nature of the risk and cost that is associated with the development as well as the scaling of AI.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. FMEval provides the ability to perform evaluations for both LLM model endpoints or the endpoint for a generative AI service as a whole. What is FMEval? In his spare time, he loves traveling and writing.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Use case and model governance plays a crucial role in implementing responsible AI and helps with the reliability, fairness, compliance, and risk management of ML models across use cases in the organization. It helps prevent biases, manage risks, protect against misuse, and maintain transparency.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com learned that migrating to the cloud and SageMaker has proved beneficial, and that adapting machine learning operations (MLOps) practices allows their ML engineers and scientists to focus on their craft and increase development velocity. Daniel Zagyva is a Data Scientist at AWS Professional Services.

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AWS Machine Learning Blog

JANUARY 26, 2024

Being aware of risks fosters transparency and trust in generative AI applications, encourages increased observability, helps to meet compliance requirements, and facilitates informed decision-making by leaders. Learn more about our commitment to Responsible AI and additional responsible AI resources to help our customers.

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Unite.AI

FEBRUARY 22, 2024

In the rapidly evolving realm of modern technology, the concept of ‘ Responsible AI ’ has surfaced to address and mitigate the issues arising from AI hallucinations , misuse and malicious human intent. Bias and Fairness : Ensuring Ethicality in AI Responsible AI demands fairness and impartiality.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

We all need to be able to unlock generative AI’s full potential while mitigating its risks. It should be easy to implement safeguards for your generative AI applications, customized to your requirements and responsible AI policies. Guardrails can help block specific words or topics.

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AWS Machine Learning Blog

DECEMBER 17, 2024

Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) along with a broad set of capabilities to build generative AI applications, simplifying development with security, privacy, and responsible AI.

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Metadata Generative AI Categorization Data Scientist 117

AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Furthermore, evaluating LLMs can also help mitigating security risks, particularly in the context of prompt data tampering.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Isaac Privitera is a Principal Data Scientist with the AWS Generative AI Innovation Center, where he develops bespoke generative AI-based solutions to address customers’ business problems. His primary focus lies in building responsible AI systems, using techniques such as RAG, multi-agent systems, and model fine-tuning.

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Metadata Generative AI LLM Data Ingestion 104

AWS Machine Learning Blog

DECEMBER 2, 2024

This dramatic improvement in loading speed opens up new possibilities for responsive AI systems, potentially enabling faster scaling and more dynamic applications that can adapt quickly to changing demands. During our performance testing we were able to load the llama-3.1-70B 70B model on an ml.p4d.24xlarge

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AWS Machine Learning Blog

NOVEMBER 18, 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 AI, 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.

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