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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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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AWS Machine Learning Blog

APRIL 29, 2024

In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows. Metaflow’s coherent APIs simplify the process of building real-world ML/AI systems in teams.

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AWS Machine Learning Blog

MAY 16, 2024

For this reason, we built the MLOps architecture to manage the created models and provide real-time services. Solution architecture The following diagram illustrates the solution architecture for serving Neural Collaborative Filtering (NCF) algorithm-based recommendation models as MLOps.

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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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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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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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AWS Machine Learning Blog

NOVEMBER 14, 2023

At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data. However, these models are not without their challenges. These can be barriers for many organizations looking to build their own foundation models.

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AWS Machine Learning Blog

JANUARY 12, 2024

In contrast, text embeddings use machine learning (ML) capabilities to capture the meaning of unstructured data. Embeddings are generated by representational language models that translate text into numerical vectors and encode contextual information in a document.

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AWS Machine Learning Blog

MAY 30, 2024

The solution is available on the GitHub repository and can be deployed to your AWS account using an AWS Cloud Development Kit (AWS CDK) package. It also generates text embedding and multimodal embedding on the frame level using Amazon Titan models. Detect generic objects and labels using the Amazon Rekognition label detection API.

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AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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The MLOps Blog

JUNE 27, 2023

As you delve into the landscape of MLOps in 2023, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. Assess the pricing models, including any hidden costs, and ensure they fit your budget and scaling requirements. For example, neptune.ai

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AWS Machine Learning Blog

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. In Part 2 , we showed how to automate the labeling and model training parts of the pipeline. If you haven’t read it yet, we recommend checking out Part 1.

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Towards AI

SEPTEMBER 28, 2023

Deployment & Serving: Exploring 6 Key MLOps Questions using AWS SageMaker This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. A/B testing, canary, shadow, etc.)▢ [Reproducibility] How do you package your ML models for serving in the cloud or at the edge?▢

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well.

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AWS Machine Learning Blog

MAY 8, 2024

To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This strategic use of AWS services delivers efficiency and scalability of their operations, as well as the implementation of advanced AI/ML applications.

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AWS Machine Learning Blog

DECEMBER 12, 2023

This new workforce requires rapid reskilling and understanding of disruptive services such as artificial intelligence (AI) and machine learning (ML) to drive meaningful outcomes. In this post, we share how Vodafone is advancing its ML skills using AWS DeepRacer and Accenture. Why is machine learning important to Vodafone?

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Mlearning.ai

APRIL 10, 2023

Hey guys in this very short blog we will see how we can train and test multiple models using Lazypredict which is an amazing open-source python package. With LazyPredict, data scientists can quickly build and compare several models on their datasets with just a few lines of code. The package requires Python 3.6

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Iguazio

APRIL 24, 2024

Generative AI is by no means a replacement for the previous wave of AI/ML (now sometimes referred to as ‘traditional AI/ML’), which continues to deliver significant value, and represents a distinct approach with its own advantages. In the end, we explain how MLOps can help accelerate the process and bring these models to production.

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AWS Machine Learning Blog

JUNE 27, 2023

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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AWS Machine Learning Blog

MAY 10, 2023

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry , enabling you to operationalize ML models in production. If it performs to your requirements, you can update the approval status of the model version to approved.

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AWS Machine Learning Blog

DECEMBER 13, 2023

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We then use a large model inference container powered by Deep Java Library (DJLServing) as our model serving solution.

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DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. DataRobot AI Platform release we’ve broken down the barriers that exist across the ML lifecycle.

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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. The SageMaker Model Registry centralizes model tracking, simplifying model deployment.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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AWS Machine Learning Blog

APRIL 25, 2024

However, for non-supported languages, you can use other models (in our case, PyAnnote) that will be deployed in SageMaker for inference. Hugging Face is a popular open source hub for machine learning (ML) models. This model, trained on the sample audio dataset, enables effective speaker partitioning in audio files.

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AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Endpoints had to be deployed manually as well.

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phData

JANUARY 31, 2024

In this blog, we’ll show you how to build a robust energy price forecasting solution within the Snowflake Data Cloud ecosystem. We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. On the Deployable models tab, choose Create.

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AWS Machine Learning Blog

MAY 1, 2024

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. Llama2 by Meta is an example of an LLM offered by AWS.

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DagsHub

SEPTEMBER 5, 2023

Most Machine Learning models are dynamic. This requires us to constantly update the deployed model. However, doing it for ML models is not as intuitive as you imagine. ML models are fundamentally different from code-based applications (size, type, framework, etc.), What will this blog cover?

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AWS Machine Learning Blog

SEPTEMBER 26, 2023

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. predictor).

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Iguazio

MARCH 14, 2024

GPU as a Service (GPUaaS) serves as a cost-effective solution for organizations who need more GPUs for their ML and gen AI operations. In this blog post, we explain how GPUaaS as a service works, how it can close the GPU shortage gap, when to use GPUaaS and how it fits with gen AI. How Does GPUaaS Work? MLRun is also a GPUaaS platform.

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AWS Machine Learning Blog

JUNE 29, 2023

Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models.

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AWS Machine Learning Blog

MAY 23, 2023

This is a guest blog post co-written with Vik Pant and Kyle Bassett from PwC. With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. Many organizations start their journey into the world of ML with a model-centric viewpoint.

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