AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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Flipboard

FEBRUARY 10, 2025

In this post, we dive into how organizations can use Amazon SageMaker AI , a fully managed service that allows you to build, train, and deploy ML models at scale, and can build AI agents using CrewAI, a popular agentic framework and open source models like DeepSeek-R1. This agent is equipped with a tool called BlocksCounterTool.

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Dataconomy

MAY 16, 2023

Let’s explore the specific role and responsibilities of a machine learning engineer: Definition and scope of a machine learning engineer A machine learning engineer is a professional who focuses on designing, developing, and implementing machine learning models and systems.

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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Dataconomy

MARCH 12, 2025

Baseline distribution plays a pivotal role in the realm of machine learning (ML), serving as the cornerstone for assessing how well models perform against a foundational standard. Importance of baseline distribution The significance of baseline distribution in ML lies in its role as a reference point.

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ODSC - Open Data Science

MARCH 12, 2025

As a machine learning (ML) practitioner, youve probably encountered the inevitable request: Can we do something with AI? Stephanie Kirmer, Senior Machine Learning Engineer at DataGrail, addresses this challenge in her talk, Just Do Something with AI: Bridging the Business Communication Gap for ML Practitioners.

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Google Research AI blog

MAY 19, 2023

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. Therefore, protecting the privacy of the training data is critical to practical, applied ML.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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DrivenData Labs

MAY 12, 2025

competition, winning solutions used deep learning approaches from facial recognition tasks (particularly ArcFace and EfficientNet) to help the Bureau of Ocean and Energy Management and NOAA Fisheries monitor endangered populations of beluga whales by matching overhead photos with known individuals.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. Our AI/ML research team focuses on identifying the best computer vision (CV) models for our system. Inferentia has been shown to reduce inference costs significantly.

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AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. In this post, we show you how to run your ML training jobs in a container using Amazon ECS to deploy, manage, and scale your ML workload.

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. TensorRT is an SDK developed by NVIDIA that provides a high-performance deep learning inference library.

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DagsHub

JULY 25, 2024

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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AWS Machine Learning Blog

JUNE 11, 2024

release , you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. AWS DLCs provide a set of Docker images that are pre-installed with deep learning frameworks.

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AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. For that use case, SageMaker provides SageMaker single model endpoints (SMEs), which allow you to deploy a single ML model against a logical endpoint.

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Heartbeat

NOVEMBER 29, 2023

Two names stand out prominently in the wide realm of deep learning: TensorFlow and PyTorch. These strong frameworks have changed the field, allowing researchers and practitioners to create and deploy cutting-edge machine learning models. TensorFlow and PyTorch present distinct routes to traverse.

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AWS Machine Learning Blog

MAY 6, 2025

Human-based model evaluation has supported custom metric definition since its launch in November 2023. At the time of writing, we dont accept custom AWS Lambda functions or endpoints for code-based custom metric evaluators.

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Towards AI

NOVEMBER 16, 2024

In particular, min-max optimisation is curcial for GANs [2], statistics, online learning [6], deep learning, and distributed computing [7]. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXivpreprint arXiv:1706.06083, 2017.[5] Arjovsky, S. Chintala, and L. 214–223, 2017.[4] Makelov, L.

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AWS Machine Learning Blog

FEBRUARY 6, 2025

By doing this, you can benefit from the higher performance and cost-efficiency offered by these specialized AI chips while taking advantage of the seamless integration with popular deep learning frameworks such as TensorFlow and PyTorch. To learn more, visit our Neuron documentation.

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AWS Machine Learning Blog

JANUARY 27, 2025

Amazon SageMake r provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. Examples for this could include use cases like geospatial analysis, bioinformatics research, or quantum machine learning. Write a Python model definition using the SageMaker inference.py

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Data Science 101

MAY 20, 2019

The exam can be broken down into 4 components: Machine Learning, Azure ML Studio, Azure Products, and Python. Machine Learning. These are topics which would be covered in a traditional machine learning course. Azure ML Studio. Azure ML Studio is a major focus of the exam, so you need to be fluent in how to use it.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

What Zeta has accomplished in AI/ML In the fast-evolving landscape of digital marketing, Zeta Global stands out with its groundbreaking advancements in artificial intelligence. Zeta’s AI innovation is powered by a proprietary machine learning operations (MLOps) system, developed in-house.

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AWS Machine Learning Blog

JANUARY 22, 2025

Daniel Pienica is a Data Scientist at Cato Networks with a strong passion for large language models (LLMs) and machine learning (ML). With six years of experience in ML and cybersecurity, he brings a wealth of knowledge to his work. You are only allowed to output text in JSON format. He completed an M.Sc.

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Dataversity

NOVEMBER 26, 2021

With uses spanning personalized medicine to the creation of social media clickbait, the use of artificial intelligence (AI) and machine learning (ML) is expected to transform industries from health care to manufacturing. The post A Beginner’s Guide to AI and Machine Learning in Web Scraping appeared first on DATAVERSITY.

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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Dataconomy

MARCH 5, 2025

Definition of smart machines Smart machines integrate AI, ML, and deep learning for cognitive functionalities such as reasoning, decision-making, and autonomous actions.

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How to Learn Machine Learning

MAY 2, 2025

This is where Azure Machine Learning shines by democratizing access to advanced AI capabilities. Azure Machine Learning is Microsoft’s enterprise-grade service that provides a comprehensive environment for data scientists and ML engineers to build, train, deploy, and manage machine learning models at scale.

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AWS Machine Learning Blog

APRIL 12, 2023

They’ve built a deep-learning model ScarceGAN, which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak labels. This work has been published in CIKM’21 and is open source for rare class identification for any longitudinal telemetry data.

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Towards AI

AUGUST 25, 2023

How to get started with an AI project Vackground on Unsplash Background Here I am assuming that you have read my previous article on How to Learn AI. Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

For this post, we use a dataset called sql-create-context , which contains samples of natural language instructions, schema definitions and the corresponding SQL query. We encourage you to read this post while running the code in the notebook.

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Becoming Human

JANUARY 23, 2023

- a beginner question Let’s start with the basic thing if I talk about the formal definition of Data Science so it’s like “Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis” , is the definition enough explanation of data science?

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Heartbeat

JULY 25, 2023

Introduction When it comes to practicing deep learning at home vs. industry, there’s a huge disconnect. If so, how much effort does it take to go from that system to a deep learning framework-ready system? It’s easy for ML teams to rack-up their monthly cloud training bills. Learn how Comet can help you do this.

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AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. These models range from lightweight tree-based models to deep learning computer vision models, which need to run on GPUs to achieve low latency and improve the user experience.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. photo from [link] As new algorithms are added to robotics, computer vision will definitely get better.

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