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

MAY 30, 2024

The broad range of topics covered with easy to understand examples will help any readers, and developers be in the know of the theory behind LLMs, prompt engineering, RAG, orchestration platforms and more. The defacto manual for AI Engineering. I highly recommend this book.” Seriously, pick it up.” Ahmed Moubtahij, ing.,

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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AWS Machine Learning Blog

MARCH 13, 2024

The audio moderation workflow uses Amazon Transcribe Toxicity Detection, which is a machine learning (ML)-powered capability that uses audio and text-based cues to identify and classify voice-based toxic content across seven categories, including sexual harassment, hate speech, threats, abuse, profanity, insults, and graphic language.

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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. These factors led to the selection of Amazon Aurora PostgreSQL as the store for vector embeddings.

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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

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

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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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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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TheSequence

NOVEMBER 12, 2023

📌 ML Engineering Event: Join Meta, PepsiCo, RiotGames, Uber & more at apply(ops) apply(ops) is in two days! Databricks’ CEO Ali Ghodsi will also be joining Tecton CEO Mike Del Balso for a fireside chat about LLMs, real-time ML, and other trends in ML. Register today—it’s free!

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OpenAI Generative AI ML Prompt Engineer 64

Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment. The focus shifts towards prompt engineering and fine-tuning.

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ODSC - Open Data Science

JANUARY 10, 2024

Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short. Participants will walk away with a solid grasp of feature stores, equipped with the knowledge to drive meaningful insights and enhancements in their real-world ML platforms and projects.

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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. John helps customers design and optimize AI/ML workloads on AWS to help them achieve their business goals.

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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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Computer Vision Large Language Models AI AI 52

AWS Machine Learning Blog

APRIL 25, 2023

Data scientists typically carry out several iterations of experimentation in data processing and training models while working on any ML problem. They want to run this ML code and carry out the experimentation with ease of use and minimal code change.

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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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Metadata Machine Learning Data Scientist Data Quality 134

Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. We’re committed to supporting and inspiring developers and engineers from all walks of life. What comes out is amazing AI-generated art! We pay our contributors, and we don’t sell ads.

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Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

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Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

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Large Language Models Data Scientist Machine Learning Computer Vision 52

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

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

Amazon SageMaker MLOps lifecycle As the post “ MLOps foundation roadmap for enterprises with Amazon SageMaker ” describes, MLOps is the combination of processes, people, and technology to productionise ML use cases efficiently. input prompts comprising input data and query) and define metrics like similarity and toxicity.

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Marktechpost

NOVEMBER 14, 2024

Prior he was an ML product leader at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on ML.

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