ODSC - Open Data Science

OCTOBER 3, 2023

Who hasn’t seen the news surrounding one of the latest jobs created by AI, that of prompt engineering ? If you’re unfamiliar, a prompt engineer is a specialist who can do everything from designing to fine-tuning prompts for AI models, thus making them more efficient and accurate in generating human-like text.

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AI News

MARCH 7, 2024

Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. The company says it has also achieved ‘near human’ proficiency in various tasks.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

It enables you to privately customize the FM of your choice with your data using techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG) and build agents that run tasks using your enterprise systems and data sources while adhering to security and privacy requirements.

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Unite.AI

APRIL 26, 2024

Parameter Count : The number of parameters in a decoder-based LLM is primarily determined by the embedding dimension (d_model), the number of attention heads (n_heads), the number of layers (n_layers), and the vocabulary size (vocab_size).

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ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. are harnessed to channel LLMs output.

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IBM Journey to AI blog

JULY 23, 2024

In the accompanying launch announcement, Meta stated that “[their] goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across LLM capabilities such as reasoning and coding.” ” Today’s launch of Llama 3.1 Likewise, Llama 3.1

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AWS Machine Learning Blog

JANUARY 4, 2024

Specifically, we discuss the following: Why do we need Text2SQL Key components for Text to SQL Prompt engineering considerations for natural language or Text to SQL Optimizations and best practices Architecture patterns Why do we need Text2SQL? Effective prompt engineering is key to developing natural language to SQL systems.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. They’re illustrated in the following figure.

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AWS Machine Learning Blog

MARCH 6, 2025

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

JANUARY 28, 2025

In interactive AI applications, delayed responses can break the natural flow of conversation, diminish user engagement, and ultimately affect the adoption of AI-powered solutions. This feature is especially helpful for time-sensitive workloads where rapid response is business critical.

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ODSC - Open Data Science

JANUARY 29, 2024

The role of prompt engineer has attracted massive interest ever since Business Insider released an article last spring titled “ AI ‘Prompt Engineer Jobs: $375k Salary, No Tech Backgrund Required.” It turns out that the role of a Prompt Engineer is not simply typing questions into a prompt window.

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ODSC - Open Data Science

SEPTEMBER 28, 2023

5 Must-Have Skills to Get Into Prompt Engineering From having a profound understanding of AI models to creative problem-solving, here are 5 must-have skills for any aspiring prompt engineer. The Implications of Scaling Airflow Wondering why you’re spending days just deploying code and ML models?

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ODSC - Open Data Science

FEBRUARY 16, 2024

Recently, we posted an in-depth article about the skills needed to get a job in prompt engineering. Now, what do prompt engineering job descriptions actually want you to do? Here are some common prompt engineering use cases that employers are looking for.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

For a demonstration on how you can use a RAG evaluation framework in Amazon Bedrock to compute RAG quality metrics, refer to New RAG evaluation and LLM-as-a-judge capabilities in Amazon Bedrock. Responsible AI Implementing responsible AI practices is crucial for maintaining ethical and safe deployment of RAG systems.

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Towards AI

JULY 27, 2024

We will also discuss how it differs from the most popular generative AI tool ChatGPT. Claude AI Claude AI is developed by Anthropic, an AI startup company backed by Google and Amazon, and is dedicated to developing safe and beneficial AI. ChatGPT vs. Claude AI: How do they differ? Let’s compare.

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AWS Machine Learning Blog

MARCH 14, 2025

Finally, metrics such as ROUGE and F1 can be fooled by shallow linguistic similarities (word overlap) between the ground truth and the LLM response, even when the actual meaning is very different.

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. Amazon Bedrock also comes with a broad set of capabilities required to build generative AI applications with security, privacy, and responsible AI.

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Marktechpost

MAY 30, 2024

Introduction to Responsible AI This course explains what responsible AI is, its importance, and how Google implements it in its products. It also introduces Google’s 7 AI principles. Introduction to Vertex AI Studio This course introduces Vertex AI Studio for prototyping and customizing generative AI models.

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AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale. The Step Functions workflow starts.

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Marktechpost

APRIL 11, 2024

These courses are crafted to provide learners with the right knowledge, tools, and techniques required to excel in AI. Here’s a look at the most relevant short courses available: Red Teaming LLM Applications This course offers an essential guide to enhancing the safety of LLM applications through red teaming.

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Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries. Prompt engineering is crucial to steering LLMs effectively.

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AWS Machine Learning Blog

DECEMBER 11, 2024

EBSCOlearning experts and GenAIIC scientists worked together to develop a sophisticated prompt engineering approach using Anthropics Claude 3.5 The evaluation process includes three phases: LLM-based guideline evaluation, rule-based checks, and a final evaluation. Sonnet model in Amazon Bedrock.

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Marktechpost

AUGUST 31, 2024

This creates a significant obstacle for real-time applications that require quick response times. Researchers from Microsoft Responsible AI present a robust workflow to address the challenges of hallucination detection in LLMs.

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AWS Machine Learning Blog

MARCH 4, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, such as 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

JULY 29, 2024

Requests and responses between Salesforce and Amazon Bedrock pass through the Einstein Trust Layer , which promotes responsible AI use across Salesforce. These prompts can be integrated with Salesforce capabilities such as Flows and Invocable Actions and Apex. For this post, we use the Anthropic Claude 3 Sonnet model.

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Unite.AI

FEBRUARY 21, 2025

How Reinforcement Learning Enhances Reasoning in LLMs How Reinforcement Learning Works in LLMs Reinforcement Learning is a machine learning paradigm in which an agent (in this case, an LLM) interacts with an environment (for instance, a complex problem) to maximize a cumulative reward.

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Mlearning.ai

JULY 8, 2023

Introduction Create ML Ops for LLM’s Build end to end development and deployment cycle. Add Responsible AI to LLM’s Add Abuse detection to LLM’s. High level process and flow LLM Ops is people, process and technology. LLM Ops flow — Architecture Architecture explained. This is an iterative pattern.

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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. Content ingestion into vector db Select the optimal LLM for your use case Selecting the right LLM for any use case is essential.

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AWS Machine Learning Blog

JULY 31, 2024

As generative artificial intelligence (AI) applications become more prevalent, maintaining responsible AI principles becomes essential. Without proper safeguards, large language models (LLMs) can potentially generate harmful, biased, or inappropriate content, posing risks to individuals and organizations.

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AWS Machine Learning Blog

APRIL 24, 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 using 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

MAY 21, 2024

This blog post outlines various use cases where we’re using generative AI to address digital publishing challenges. To do so, journalists first invoke a rewrite of the article by an LLM using Amazon Bedrock. We then parse the response, store the sentiment, and make it publicly available for each article to be accessed by ad servers.

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Flipboard

NOVEMBER 27, 2024

It provides a broad set of capabilities needed to build generative AI applications with security, privacy, and responsible AI. Sonnet large language model (LLM) on Amazon Bedrock. For naturalization applications, LLMs offer key advantages. prompt = f''' You are an expert citizenship application analyst.

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ODSC - Open Data Science

OCTOBER 24, 2024

Time is running out to get your pass to the can’t-miss technical AI conference of the year. Our incredible lineup of speakers includes world-class experts in AI engineering, AI for robotics, LLMs, machine learning, and much more. Register here before we sell out!

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AWS Machine Learning Blog

APRIL 22, 2024

Figure 5 offers an overview on generative AI modalities and optimization strategies, including prompt engineering , Retrieval Augmented Generation , and fine-tuning or continued pre-training. This enhances transparency and promotes trust in your commitment to sustainability.

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AWS Machine Learning Blog

MARCH 15, 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

AUGUST 9, 2024

RAG enables LLMs to generate more relevant, accurate, and contextual responses by cross-referencing an organization’s internal knowledge base or specific domains, without the need to retrain the model. The question and context are combined and fed as a prompt to the LLM.

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AssemblyAI

MAY 23, 2023

Large Language Models In recent years, LLM development has seen a significant increase in size, as measured by the number of parameters. To put it differently, this means that in the span of the last 4 years only, the size of LLMs has repeatedly doubled every 3.5 Determining the necessary data for training an LLM is challenging.

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