AWS Machine Learning Blog

MAY 13, 2024

In this part of the blog series, we review techniques of prompt engineering and Retrieval Augmented Generation (RAG) that can be employed to accomplish the task of clinical report summarization by using Amazon Bedrock. It can be achieved through the use of proper guided prompts. There are many prompt engineering techniques.

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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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Towards AI

JUNE 18, 2024

The principles of CNNs and early vision transformers are still important as a good background for ML engineers, even though they are much less popular nowadays. The book focuses on adapting large language models (LLMs) to specific use cases by leveraging Prompt Engineering, Fine-Tuning, and Retrieval Augmented Generation (RAG).

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Large Language Models Prompt Engineer Prompt Engineering LLM 60

AWS Machine Learning Blog

MAY 30, 2024

Prompt engineering Prompt engineering is crucial for the knowledge retrieval system. The prompt guides the LLM on how to respond and interact based on the user question. Prompts also help ground the model.

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Chatbots Life

MAY 12, 2023

We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. Will ChatGPT replace software engineers? Will ChatGPT replace ML Engineers? Will ChatGPT replace ML Engineers? We will answer the question “ Will you lose your job?” And, as mentioned before.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. These users need strong end-to-end ML and data science expertise and knowledge of model deployment and inference.

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AWS Machine Learning Blog

MARCH 13, 2024

Use LLM prompt engineering to accommodate customized policies The pre-trained Toxicity Detection models from Amazon Transcribe and Amazon Comprehend provide a broad toxicity taxonomy, commonly used by social platforms for moderating user-generated content in audio and text formats. LLMs, in contrast, offer a high degree of flexibility.

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TheSequence

NOVEMBER 12, 2023

📌 ML Engineering Event: Join Meta, PepsiCo, RiotGames, Uber & more at apply(ops) apply(ops) is in two days! PromptIDE Elon Musk’s xAI announced PromptIDE, a development environment for prompt engineering —> Read more. It’s what makes this market so fascinating.

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Heartbeat

OCTOBER 5, 2023

Feature Engineering and Model Experimentation MLOps: Involves improving ML performance through experiments and feature engineering. LLMOps: LLMs excel at learning from raw data, making feature engineering less relevant. The focus shifts towards prompt engineering and fine-tuning.

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Snorkel AI

MAY 4, 2023

AI development stack: AutoML, ML frameworks, no-code/low-code development. Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The free virtual conference is the largest annual gathering of the data-centric AI community.

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Snorkel AI

MAY 4, 2023

AI development stack: AutoML, ML frameworks, no-code/low-code development. Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The free virtual conference is the largest annual gathering of the data-centric AI community.

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AWS Machine Learning Blog

JULY 24, 2024

You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning. Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. Each iteration can be considered a run within an experiment.

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ODSC - Open Data Science

JANUARY 10, 2024

Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution.

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AWS Machine Learning Blog

OCTOBER 4, 2023

Solution overview Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. In SageMaker Studio, choose the upload icon and upload the file to your SageMaker Studio instance.

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Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. Evaluation Metrics Choosing the right evaluation metrics for a classification task is critical to accurately benchmark the performance of computer vision models. What comes out is amazing AI-generated art!

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Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.

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Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.

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Large Language Models Data Scientist Machine Learning Computer Vision 52

AWS Machine Learning Blog

APRIL 25, 2023

This allows ML engineers and admins to configure these environment variables so data scientists can focus on ML model building and iterate faster. AI/ML Specialist Solutions Architect at AWS, based in Virginia, US. SageMaker uses training jobs to launch this function as a managed job. Vikram Elango is a Sr.

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AWS Machine Learning Blog

MARCH 1, 2023

This blog post details the implementation of generative AI-assisted fashion online styling using text prompts. Machine learning (ML) engineers can fine-tune and deploy text-to-semantic-segmentation and in-painting models based on pre-trained CLIPSeq and Stable Diffusion with Amazon SageMaker.

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AWS Machine Learning Blog

MAY 9, 2024

AI/ML Specialist Solutions Architect at AWS, based in Virginia, US. He is currently focused on generative AI, LLMs, prompt engineering, large model inference optimization, and scaling ML across enterprises. Vikram Elango is a Sr.

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The MLOps Blog

JUNE 27, 2023

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. Valohai Valohai provides a collaborative environment for managing and automating machine learning projects.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data.

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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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The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. We have someone precisely using it more for feature engineering, but using it within a Flask app.

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Marktechpost

NOVEMBER 14, 2024

That’s why we provide an end-to-end platform backed by a dedicated team of ML engineers to help you every step of the way. Right now, what I hear from organizations is that when they move to production, they can often get a model to 70% accuracy with prompt engineering alone.

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