AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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Towards AI

AUGUST 16, 2023

Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). There is no central store to manage models (versions and stage transitions).

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Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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AWS Machine Learning Blog

DECEMBER 24, 2024

aws sagemaker create-cluster --cli-input-json file://cluster-config.json --region $AWS_REGION You should be able to see your cluster by navigating to SageMaker Hyperpod in the AWS Management Console and see a cluster named ml-cluster listed. After a few minutes, its status should change from Creating to InService.

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AWS Machine Learning Blog

MARCH 29, 2023

there is enormous potential to use machine learning (ML) for quality prediction. ML-based predictive quality in HAYAT HOLDING HAYAT is the world’s fourth-largest branded baby diapers manufacturer and the largest paper tissue manufacturer of the EMEA. After the data preparation phase, a two-stage approach is used to build the ML models.

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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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ODSC - Open Data Science

APRIL 13, 2023

Hands-on Data-Centric AI: Data Preparation Tuning — Why and How? Going into developing machine learning models with a hands-on, data-centric AI approach has its benefits and requires a few extra steps to achieve. Learn more here.

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Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

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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

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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AWS Machine Learning Blog

AUGUST 22, 2023

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed.

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AWS Machine Learning Blog

AUGUST 16, 2023

MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. MLOps tooling helps you repeatably and reliably build and simplify these processes into a workflow that is tailored for ML. This capability leads to significant time and computational resource savings.

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AWS Machine Learning Blog

MAY 29, 2024

Figure 1: LLaVA architecture Prepare data When it comes to fine-tuning the LLaVA model for specific tasks or domains, data preparation is of paramount importance because having high-quality, comprehensive annotations enables the model to learn rich representations and achieve human-level performance on complex visual reasoning challenges.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

input_ids return batch #apply the data preparation function to all of our fine-tuning dataset samples using dataset's.map method. His current areas of focus are AI/ML infrastructure and applications. Dr. Changsha Ma is an AI/ML Specialist at AWS.

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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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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

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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Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Pickl AI

DECEMBER 3, 2024

Established in 1987 at the University of California, Irvine, it has become a global go-to resource for ML practitioners and researchers. The UCI Machine Learning Repository is a well-known online resource that houses vast Machine Learning (ML) research and applications datasets. The global Machine Learning market continues to expand.

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Pickl AI

NOVEMBER 5, 2024

It combines various techniques from statistics, mathematics, computer science, and domain expertise to interpret complex data sets. AI encompasses various subfields, including Machine Learning (ML), Natural Language Processing (NLP), robotics, and computer vision.

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Tableau

FEBRUARY 24, 2021

However, another motivation was a personal reflection on a field that did not yet exist a little over a decade ago when I first began my advanced studies in computer science. Moreover, the work carried out by data scientists is distinct from other types of data analysis, because it requires a wider breadth of multidisciplinary skills.

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Tableau

FEBRUARY 24, 2021

However, another motivation was a personal reflection on a field that did not yet exist a little over a decade ago when I first began my advanced studies in computer science. Moreover, the work carried out by data scientists is distinct from other types of data analysis, because it requires a wider breadth of multidisciplinary skills.

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AWS Machine Learning Blog

DECEMBER 5, 2024

About the Authors Raghu Ramesha is an ML Solutions Architect with the Amazon SageMaker Service team. He focuses on helping customers build, deploy, and migrate ML production workloads to SageMaker at scale. Ram Vegiraju is an ML Architect with the Amazon SageMaker Service team. In his spare time, he loves traveling and writing.

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ODSC - Open Data Science

MARCH 18, 2025

Allen Downey, PhD, Principal Data Scientist at PyMCLabs Allen is the author of several booksincluding Think Python, Think Bayes, and Probably Overthinking Itand a blog about data science and Bayesian statistics. in computer science from the University of California, Berkeley; and Bachelors and Masters degrees fromMIT.

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