AWS Machine Learning Blog

JUNE 3, 2024

Solution overview SageMaker Canvas brings together a broad set of capabilities to help data professionals prepare, build, train, and deploy ML models without writing any code. In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing.

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Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. AWS published Guidance for Optimizing MLOps for Sustainability on AWS to help customers maximize utilization and minimize waste in their ML workloads.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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Towards AI

APRIL 27, 2023

Nothing in the world motivates a team of ML engineers and scientists to spend the required amount of time in data annotation and labeling. Now if you see, it's a complete workflow optimization challenge centered around the ability to execute data-related operations 10x faster. It's a new need now.

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Pickl AI

JANUARY 23, 2023

Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. Theano Theano is one of the fastest and simplest ML libraries, and it was built on top of NumPy. When used in GPU architectures, this framework can complete tasks 140 times faster.

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AWS Machine Learning Blog

MARCH 1, 2023

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.

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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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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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AWS Machine Learning Blog

APRIL 27, 2023

Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by extending the functionality of existing machine learning (ML) frameworks such as PyTorch. Regular CPU systems are completely memory bound for these calculations, and performance is limited by the time required to move the data into the CPU.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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TheSequence

MAY 24, 2023

I originally did a master's degree in physics focusing on astrophysics, but around that time, I noticed the breakthroughs happening in ML so I decided to switch the focus of my studies towards ML. data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ).

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Heartbeat

SEPTEMBER 6, 2023

When working on an ML project, we must compare various machine learning models with different hyperparameters and want to understand which models and hyperparameters are most effective for our use case. Image by Author If you want to end the experiment, you can use the end method of the Experiment object to mark the experiment as complete. #

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DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. DataRobot Notebooks is a fully hosted and managed notebooks platform with auto-scaling compute capabilities so you can focus more on the data science and less on low-level infrastructure management.

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The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. I don’t see what special role ML and MLOps engineers would play here. –

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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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The MLOps Blog

DECEMBER 29, 2022

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 Kyle Morris from Banana about deploying models on GPU. Kyle: Yes.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Amazon SageMaker is a fully managed service that allows developers and data scientists to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get real-time inference results.

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AWS Machine Learning Blog

DECEMBER 2, 2024

Challenges in deploying LLMs for inference As LLMs and their respective hosting containers continue to grow in size and complexity, AI and ML engineers face increasing challenges in deploying and scaling these models efficiently for inference. Marc Karp is an ML Architect with the Amazon SageMaker Service team.

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AWS Machine Learning Blog

JULY 23, 2024

is an auto-regressive language model that uses an optimized transformer architecture. SageMaker Studio is a comprehensive integrated development environment (IDE) that offers a unified, web-based interface for performing all aspects of the machine learning (ML) development lifecycle. The Llama 3.1 At its core, Llama 3.1 Deploy Llama 3.1

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Mlearning.ai

MAY 3, 2023

autogpt : Auto-GPT is an “Autonomous AI agent” that given a goal in natural language, will allow Large Language Models (LLMs) to think, plan, and execute actions for us autonomously. ML/AI Enthusiasts, and Learners Citizen Data Scientists who prefer a low code solution for quick testing. The complete code of the APP can be found here.

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