AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Only involving necessary people to do case validation or augmentation tasks reduces the risk of document mishandling and human error when dealing with sensitive data. About the Authors Sherry Ding is a senior artificial intelligence (AI) and machine learning (ML) specialist solutions architect at Amazon Web Services (AWS).

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Snorkel AI

NOVEMBER 1, 2023

Enhanced user experience in Snorkel Flow Studio We’ve made significant improvements to Snorkel Flow Studio, making it easier for you to export training datasets in the UI, improving default display settings, adding per-class filtering and analysis, and several other great enhancements for easier integration with larger ML pipelines.

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AWS Machine Learning Blog

AUGUST 14, 2023

Some of the models offer capabilities for you to fine-tune them with your own data. SageMaker JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for machine learning (ML) with SageMaker. Mia Chang is an ML Specialist Solutions Architect for Amazon Web Services.

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Snorkel AI

NOVEMBER 1, 2023

Enhanced user experience in Snorkel Flow Studio We’ve made significant improvements to Snorkel Flow Studio, making it easier for you to export training datasets in the UI, improving default display settings, adding per-class filtering and analysis, and several other great enhancements for easier integration with larger ML pipelines.

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Iguazio

FEBRUARY 8, 2024

To read more about LLMOps and MLOps, checkout the O’Reilly book “Implementing MLOps in the Enterprise” , authored by Iguazio ’s CTO and co-founder Yaron Haviv and by Noah Gift. They are characterized by their enormous size, complexity, and the vast amount of data they process. What is LLMOps? Collecting feedback for further tuning.

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AssemblyAI

JUNE 24, 2024

An updated ML from scratch course will be released. - Do Kaggle's intro and intermediate ML courses to learn more data preparation with Pandas. Useful books referenced: Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, Machine Learning Yearning by Andrew Ng. Helps build portfolio and CV.

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AssemblyAI

JUNE 24, 2024

An updated ML from scratch course will be released. - Do Kaggle's intro and intermediate ML courses to learn more data preparation with Pandas. Useful books referenced: Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, Machine Learning Yearning by Andrew Ng. Helps build portfolio and CV.

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The MLOps Blog

FEBRUARY 22, 2024

At the beginning of my machine learning journey, I was convinced that creating an ML model always looks similar. You start with a business problem, prepare a dataset, and finally train the model, which is evaluated and deployed. But most real-world machine learning (ML) projects are not like that. What is continual learning?

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AWS Machine Learning Blog

MARCH 28, 2024

It provides a collection of pre-trained models that you can deploy quickly and with ease, accelerating the development and deployment of machine learning (ML) applications. Data preparation In this post, we use several years of Amazon’s Letters to Shareholders as a text corpus to perform QnA on.

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Snorkel AI

MARCH 19, 2024

Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. Book a demo today. Dr. Ali Arsanjani is the director of AI/ML partner engineering at Google Cloud. See what Snorkel option is right for you.

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Snorkel AI

MARCH 19, 2024

Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

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Mlearning.ai

FEBRUARY 27, 2023

Recently as I focused more on how to make proper data science projects that would be better fit for production, I started to pick up various tools and practices and figured out which were the best for me. However, for the intent of this article, I will strictly follow this principle as demonstrated by the book mentioned above.

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DagsHub

MAY 27, 2024

Source: Author Introduction Just like having a massive pile of books won't make you a genius unless you read and understand them, a mountain of data won't make a powerful AI if it's not properly labeled. Now you might be wondering, why exactly we need these annotation tools when we can label the ML data on our own.

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AWS Machine Learning Blog

AUGUST 15, 2024

Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

AWS innovates to offer the most advanced infrastructure for ML. For ML specifically, we started with AWS Inferentia, our purpose-built inference chip. Neuron plugs into popular ML frameworks like PyTorch and TensorFlow, and support for JAX is coming early next year. Customers like Adobe, Deutsche Telekom, and Leonardo.ai

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AWS Machine Learning Blog

SEPTEMBER 12, 2023

The machine learning (ML) model classifies new incoming customer requests as soon as they arrive and redirects them to predefined queues, which allows our dedicated client success agents to focus on the contents of the emails according to their skills and provide appropriate responses.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

The next step is to provide them with a more intuitive and conversational interface to interact with their data, empowering them to generate meaningful visualizations and reports through natural language interactions. Outside of work, he enjoys playing lawn tennis and reading books. powered by Amazon Bedrock Domo.AI

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DagsHub

JULY 15, 2024

They can engage users in natural dialogue, provide customer support, answer FAQs, and assist with booking or shopping decisions. Data Management Costs Data Collection : Involves sourcing diverse datasets, including multilingual and domain-specific corpora, from various digital sources, essential for developing a robust LLM.

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How to Learn Machine Learning

MARCH 25, 2025

Let us now look at the key differences starting with their definitions and the type of data they use. Definition of Supervised Learning and Unsupervised Learning Supervised learning is a process where an ML model is trained using labeled data. In this case, every data point has both input and output values already defined.

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AWS Machine Learning Blog

DECEMBER 18, 2024

With a vision to build a large language model (LLM) trained on Italian data, Fastweb embarked on a journey to make this powerful AI capability available to third parties. To further enrich the dataset, Fastweb generated synthetic Italian data using LLMs. The team opted for fine-tuning on AWS.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

Solution overview SageMaker JumpStart is a robust feature within the SageMaker machine learning (ML) environment, offering practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs). Choose Submit to start the training job on a SageMaker ML instance. Accept the Llama 3.2

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ODSC - Open Data Science

MARCH 18, 2025

Amber Roberts, Staff Technical Marketing Manager at Databricks Prior to her time at Databricks, Amber was the ML Growth Lead at Arize AI, where she leaned on her years of experience building models as a data scientist and machine learning engineer. Session 2: Machine Learning withCatBoost This workshop will show how to use CatBoost.

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