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 15, 2023

A Definitive Guide to Advanced Use of MLflowContinue reading on Towards Data Science »

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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

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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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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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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IBM Data Science in Practice

MARCH 8, 2023

The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years. Hidden Technical Debt in Machine Learning Systems More money, more problems — Rise of too many ML tools 2012 vs 2023 — Source: Matt Turck People often believe that money is the solution to a problem.

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AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. This entire workflow is shown in the following solution diagram.

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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. This significantly reduces training time and cost for product planogram models.

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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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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. Machine learning(ML) is evolving at a very fast pace. Photo by Andrea De Santis on Unsplash So, What is Machine Learning?

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phData

JANUARY 30, 2024

Snowpark ML is transforming the way that organizations implement AI solutions. Snowpark allows ML models and code to run on Snowflake warehouses. By “bringing the code to the data,” we’ve seen ML applications run anywhere from 4-100x faster than other architectures. Sign up today for unbiased AI/ML advice!

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Data Science Dojo

SEPTEMBER 8, 2023

Machine Learning and Deep Learning: The Power Duo Machine Learning (ML) and Deep Learning (DL) are two critical branches of AI that bring exceptional capabilities to predictive analytics. ML encompasses a range of algorithms that enable computers to learn from data without explicit programming. Streamline operations. Mitigate risks.

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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

AUGUST 3, 2023

As machine learning (ML) becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of ML models to meet the diverse needs of their customers. Here, the checkpoints need to be saved in a pre-specified location, with the default being /opt/ml/checkpoints.

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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. Firstly, we need to define the serving container.

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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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Data Science Dojo

APRIL 25, 2023

Keep in mind that CREATE PROCEDURE must be invoked using EXEC in order to be executed, exactly like the function definition. Tom Hamilton Stubber The emergence of Quantum ML With the use of quantum computing, more advanced artificial intelligence and machine learning models might be created.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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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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phData

DECEMBER 12, 2024

The Snowflake AI Data Cloud has been on a roll, building predictive ML capabilities into their platform. Predictive ML in Snowflake Snowflakes predictive ML platform covers the lifecycle of a typical machine learning project. Watch Webinar The post Snowflake ML Objects Cheatsheet appeared first on phData.

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AWS Machine Learning Blog

FEBRUARY 27, 2023

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models.

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Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. This type of data is often used in ML and artificial intelligence applications.

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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. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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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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Towards AI

NOVEMBER 16, 2024

Figure 1 : Non-Convex function Visualisation (Source: [link] Therefore, the classical notion of local Nash equilibrium from simultaneous games may not be a proper definition of local optima for sequential games since minimax is in general not equal to maximin.

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Towards AI

MARCH 14, 2024

I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer? Well, there are three main reasons for this confusing overlap between the role of a data scientist and the role of an ML engineer.

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The MLOps Blog

JUNE 13, 2024

As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. Using the following three principles helps you build a mature ML development process: Establish a standard repository structure you can use as a scaffold for your projects. What is a mature ML development process?

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Towards AI

AUGUST 16, 2023

To receive deep insights just like this and more, including top ML papers of the week, job postings, ML tips from real-world experience, and ML stories from researchers and builders, join my newsletter here. We can definitely do better. Upgrade to access all of Medium. Join thousands of data leaders on the AI newsletter.

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IBM Data Science in Practice

MAY 26, 2023

The Common Definition of AI Fairness In Fairness Explained: Definitions and Metrics , fairness definitions and fairness metrics are presented in the context of a real-world example that predicts a criminal defendant’s likelihood of reoffending. A definition of asset fairness may be useful here.

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Towards AI

SEPTEMBER 10, 2023

I’ve passed many ML courses before, so that I can compare. This one is definitely one of the most practical and inspiring. So you definitely can trust his expertise in Machine Learning and Deep Learning. You start with the working ML model. So, I would like to share my main takeaways from it with you.

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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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

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. The SageMaker Processing job operates with the /opt/ml local path, and you can specify your ProcessingInputs and their local path in the configuration. Create an S3 bucket.

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AWS Machine Learning Blog

AUGUST 22, 2024

You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment.

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Data Science Dojo

MARCH 15, 2024

Its robots are powered by a technology called the Covariant Brain, a machine-learning (ML) model to train and improve robots’ functionality in real-world applications. It spun off in 2017 from OpenAI by its ex-research scientists, Peter Chen and Pieter Abbeel.

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