AWS Machine Learning Blog

FEBRUARY 26, 2024

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

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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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Chatbots Life

MAY 16, 2023

10Clouds is a software consultancy, development, ML, and design house based in Warsaw, Poland. Services : AI Solution Development, ML Engineering, Data Science Consulting, NLP, AI Model Development, AI Strategic Consulting, Computer Vision. Elite Service Delivery partner of NVIDIA.

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Snorkel AI

MAY 4, 2023

Large Language Models (LLMs) such as GPT-4 and LLaMA have revolutionized natural language processing and understanding, enabling a wide range of applications, from conversational AI to advanced text generation. AI development stack: AutoML, ML frameworks, no-code/low-code development.

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Snorkel AI

MAY 4, 2023

Large Language Models (LLMs) such as GPT-4 and LLaMA have revolutionized natural language processing and understanding, enabling a wide range of applications, from conversational AI to advanced text generation. AI development stack: AutoML, ML frameworks, no-code/low-code development.

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. GitHub serves as a centralized location to store, version, and manage your ML code base.

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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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Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. Their mission?

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Mlearning.ai

FEBRUARY 9, 2023

11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. While pursuing education in the field, the nature of the problems you solve as a part of a course project might be different compared to the real world.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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Pickl AI

JULY 14, 2023

Expert systems were further refined, and new programming languages like Prolog emerged. 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. The average salary of a ML Engineer per annum is $125,087.

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Snorkel AI

JUNE 9, 2023

These models will help automate manual processes and improve insurance companies’ abilities to find the right buyers for the right products. 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

These models will help automate manual processes and improve insurance companies’ abilities to find the right buyers for the right products. 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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AWS Machine Learning Blog

AUGUST 1, 2023

Summarized patient information can be useful to a number of downstream processes like data aggregation, effectively coding patients, or grouping patients with similar diagnoses for review. Artificial intelligence (AI) and machine learning (ML) models have shown great promise in addressing these challenges. It’s as easy as that!

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AWS Machine Learning Blog

JUNE 8, 2023

This enables you to begin machine learning (ML) quickly. It performs well on various natural language processing (NLP) tasks, including text generation. A SageMaker real-time inference endpoint enables fast, scalable deployment of ML models for predicting events. He leads the NYC machine learning and AI meetup.

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AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. It can be cumbersome to manage the process, but with the right tool, you can significantly reduce the required effort. FastAPI is a modern, high-performance web framework for building APIs with Python.

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BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. Currently, I am working on Large Language Model (LLM) based autonomous agents.

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AWS Machine Learning Blog

APRIL 29, 2024

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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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

DECEMBER 14, 2023

Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository. This can significantly shorten the time needed to deploy the Machine Learning (ML) pipeline to production. And, it does not require the code to be ported into PySpark.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL.

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AWS Machine Learning Blog

MARCH 14, 2024

Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents. Grace Lang is an Associate Data & ML engineer with AWS Professional Services. Mike Amjadi is a Data & ML Engineer with AWS ProServe focused on enabling customers to maximize value from data.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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AWS Machine Learning Blog

AUGUST 16, 2023

Thomson Reuters (TR), a global content and technology-driven company, has been using artificial intelligence (AI) and machine learning (ML) in its professional information products for decades. Thomson Reuters Labs, the company’s dedicated innovation team, has been integral to its pioneering work in AI and natural language processing (NLP).

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AWS Machine Learning Blog

MAY 13, 2024

We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization. Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline. Embeddings are numerical representations of real-world objects that ML systems use to understand complex knowledge domains like humans do.

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Generative AI Prompt Engineer Prompt Engineering LLM 105

BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. Currently, I am working on Large Language Model (LLM) based autonomous agents.

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Robotics Natural Language Processing Machine Learning Computer Vision 100

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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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Reusability & reproducibility: Building ML models is time-consuming by nature. These 3 operations work in harmony to simplify the whole model management process.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Given this mission, Talent.com and AWS joined forces to create a job recommendation engine using state-of-the-art natural language processing (NLP) and deep learning model training techniques with Amazon SageMaker to provide an unrivaled experience for job seekers. The recommendation system has driven an 8.6%

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Snorkel AI

OCTOBER 12, 2023

Discover the strategies used to drive data-driven decisions within the complex governmental landscape and gain valuable perspectives on the future of AI/ML, the ethical considerations in data science, and the transformative potential of leveraging data to better society. Current specialized ML libraries (e.g.,

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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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DataRobot Blog

SEPTEMBER 13, 2022

They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. These might include—but are not limited to—deep learning, image recognition and natural language processing. Data scientists drive business outcomes.

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Snorkel AI

OCTOBER 12, 2023

Discover the strategies used to drive data-driven decisions within the complex governmental landscape and gain valuable perspectives on the future of AI/ML, the ethical considerations in data science, and the transformative potential of leveraging data to better society. Current specialized ML libraries (e.g.,

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AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

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The MLOps Blog

JULY 18, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production.

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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? this means that language models are just a higher level of abstraction for the developers. Why is ChatGPT so effective?

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models.

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