Marktechpost

NOVEMBER 16, 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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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

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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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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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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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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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

AUGUST 4, 2023

SageMaker integration SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. To learn more about the Salesforce BYOM integration, refer to Bring your own AI models with Einstein Studio.

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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. Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.

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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. AI/ML Specialist Solutions Architect working on Amazon Web Services.

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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. Conclusion FM evaluations allows customers to trust that the LLM they select is the right one for their use case and that it will perform responsibly. What is FMEval?

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AWS Machine Learning Blog

OCTOBER 15, 2024

ML practitioners can deploy FMs to dedicated SageMaker instances from a network-isolated environment and customize models using SageMaker for model training and deployment. About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria. Rodrigo Merino is a Generative AI Solutions Architect Manager at AWS.

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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. Shreeya holds a master’s degree from Duke University.

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ODSC - Open Data Science

JANUARY 10, 2024

Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short.

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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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Pickl AI

JULY 14, 2023

However, symbolic AI faced limitations in handling uncertainty and dealing with large-scale data. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Artificial Intelligence and the Future of Humans 1.

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Viso.ai

FEBRUARY 12, 2024

Error Detection and Debugging: A major challenge ML engineers face is debugging complex models with millions of parameters. Explainable AI helps identify the particular segments of an issue and errors in the system’s logic or training data. Explainable AI is the pillar for responsible AI development and monitoring.

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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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. The following figure shows the end-to-end MLOps lifecycle: A typical journey starts with a data scientist creating a proof-of-concept (PoC) notebook to prove that ML can solve a business problem.

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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 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. Marc Karp is an ML Architect with the Amazon SageMaker Service team. 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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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. Connect with Hin Yee on LinkedIn.

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AWS Machine Learning Blog

OCTOBER 11, 2024

This makes it a popular option for a vector database when using Amazon Bedrock Knowledge Bases, because it makes it straightforward to build modern machine learning (ML) augmented search experiences and generative AI applications without having to manage the underlying vector database infrastructure. Satish Sarapuri is a Sr.

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