AWS Machine Learning Blog

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. In this post, we expand on this topic to demonstrate how to use Einstein Studio for product recommendations. You can use this integration for traditional models as well as large language models (LLMs).

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Mlearning.ai

FEBRUARY 25, 2023

In this part, I will tell you the other components of MLflow including models, model registry, and project. from the MLflow official website) The model format is called “flavors” that can be understood by different downstream tools. This function will log a trained model in the currently active run.

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AWS Machine Learning Blog

DECEMBER 19, 2023

However, putting an ML model into production at scale is challenging and requires a set of best practices. Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge.

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AWS Machine Learning Blog

APRIL 21, 2023

Batch inference is a common pattern where prediction requests are batched together on input, a job runs to process those requests against a trained model, and the output includes batch prediction responses that can then be consumed by other applications or business functions. In this post, we focus on the second scenario.

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Heartbeat

MARCH 27, 2023

Model versioning and tracking with Comet ML Photo by Maxim Hopman on Unsplash In the first part of this article , we made a point to go through the steps that are necessary for you to log a model into the registry. The next step that I will address in this article involves the development of different models.

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Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Live logs make debugging seamless.

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Towards AI

MARCH 28, 2024

This week, I’m super excited to announce that we are finally releasing our book, ‘Building AI for Production; Enhancing LLM Abilities and Reliability with Fine-Tuning and RAG,’ where we gathered all our learnings. It offers free API service to access AI models like Gemma, GPT-4, GPT-4–1106-PREVIEW, GPT-3.5-turbo,

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Snorkel AI

JANUARY 9, 2024

Databricks MLflow Deployment Integration: new streamlined deployment of machine learning models to the Databricks MLflow Model Registry Performance and usability improvements: Snorkel Flow Studio and dataset loads 2x faster. Advanced PDF Annotation: Simplifies labeling and boosts efficiency for large documents. As of the 2023.R4

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Snorkel AI

JANUARY 9, 2024

Databricks MLflow Deployment Integration: new streamlined deployment of machine learning models to the Databricks MLflow Model Registry Performance and usability improvements: Snorkel Flow Studio and dataset loads 2x faster. Advanced PDF Annotation: Simplifies labeling and boosts efficiency for large documents. As of the 2023.R4

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AWS Machine Learning Blog

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models.

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NVIDIA

AUGUST 29, 2023

As generative AI and large language models (LLMs) continue to drive innovations, compute requirements for training and inference have grown at an astonishing pace. This Jax-based machine learning framework is purpose-built to train large-scale models, allowing advanced and fully configurable experimentation and parallelization.

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Towards AI

NOVEMBER 5, 2023

Source: author’s screenshot on DataRobot AWS Bedrock is a managed service platform for foundation models on AWS. It is an orchestration of a bunch of foundation models e.g., Anthropic Claude, Stable Diffusion etc. You can also create a custom model by fine-tuning the foundation models and creating your own model endpoint.

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deepsense.ai

JULY 30, 2023

The recent strides made in the field of machine learning have given us an array of powerful language models and algorithms. These models offer tremendous potential but also bring a unique set of challenges when it comes to building large-scale ML projects. But what happens next?

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. Our approach applies to both open-source and proprietary models equally.

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Snorkel AI

MAY 1, 2024

phData Senior ML Engineer Ryan Gooch recently evaluated options to accelerate ML model deployment with Snorkel Flow and AWS SageMaker. This involves developing and tuning a model customized to extract rich insights from invoices using Snorkel’s data development platform and deploying with AWS Sagemaker.

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Snorkel AI

MAY 1, 2024

Whether it’s creating real-time reporting on manufacturing output, constructing sophisticated data engineering pipelines, architecting robust MLOps solutions, or building models to help predict when to perform maintenance, we empower leading manufacturers to bring ambitious data and analytics projects to life.

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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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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. High level Solution Design This ML platform was envisioned and designed to be consumed by different models across various code repositories.

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AWS Machine Learning Blog

MAY 25, 2023

This post was co-authored by Brian Curry (Founder and Head of Products at OCX Cognition) and Sandhya MN (Data Science Lead at InfoGain) OCX Cognition is a San Francisco Bay Area-based startup, offering a commercial B2B software as a service (SaaS) product called Spectrum AI.

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Heartbeat

SEPTEMBER 18, 2023

It offers a robust suite of features and tools that facilitate ML workflow productivity, collaboration, and reproducibility. With its user-friendly interface and extensive capabilities, Comet empowers users to extract meaningful insights from their data, accelerate model development, and achieve exceptional results.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

Boomi funded this solution using the AWS PE ML FastStart program, a customer enablement program meant to take ML-enabled solutions from idea to production in a matter of weeks. Boomi built this solution using Amazon SageMaker Studio , an end-to-end browser-based IDE for AI/ML workloads, and Amazon Elastic Container Registry (Amazon ECR).

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AWS Machine Learning Blog

JULY 6, 2023

A number of AWS independent software vendor (ISV) partners have already built integrations for users of their software as a service (SaaS) platforms to utilize SageMaker and its various features, including training, deployment, and the model registry.

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Flipboard

NOVEMBER 21, 2023

In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS). For more details about MathWorks cloud products, see MATLAB and Simulink in the Cloud or email Mathworks. We start by training a classifier model on our desktop with MATLAB.

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AWS Machine Learning Blog

APRIL 19, 2023

In this post, the Sportradar team discusses the challenges they encountered and the solutions they created to build their model inference platform using the DJL. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. We recently developed four more new models.

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AWS Machine Learning Blog

MAY 10, 2023

Sleepme is an industry leader in sleep temperature management and monitoring products, including an Internet of Things (IoT) enabled sleep tracking sensor suite equipped with heart rate, respiration rate, bed and ambient temperature, humidity, and pressure sensors. This use case demanded an ML model that served real-time inference.

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AWS Machine Learning Blog

FEBRUARY 15, 2024

This dilemma hampers the creation of efficient models that use data to generate business-relevant insights. Amazon SageMaker Canvas addresses this dilemma by providing domain experts a no-code interface to create powerful analytics and ML models, such as forecasts, classification, or regression models.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency. Once deployed, model performance is continuously monitored.

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ODSC - Open Data Science

AUGUST 10, 2023

You should be comfortable using Jupyter Notebooks and Numpy, and training simple models in PyTorch. In particular, we’ll learn about key technologies available on AWS that help you pretrain the foundation models of the future. Read a recap of our recent interview with Emily here.

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Unite.AI

DECEMBER 19, 2023

With increasing cyberattacks, like ransomware, zero-day exploitations, and other never-before-seen threats, the status quo reactionary security model is not working. Most cybersecurity tools leverage machine learning (ML) models that present several shortcomings to security teams when it comes to preventing threats.

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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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AWS Machine Learning Blog

MAY 3, 2023

times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. bfloat16 support – The bfloat16 support in Graviton3 allows for efficient deployment of models trained using bfloat16, fp32, and AMP (Automatic Mixed Precision). is up to 3.5 cpu-py310-ubuntu20.04-ec2

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Mlearning.ai

JULY 29, 2023

Schedule Batch Inference of Machine Learning Model on Azure Cloud with Container Services and Logic App Photo by Victoire Joncheray on Unsplash I. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications II. Tag and push this image to Azure Container Registry.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

Another example is independent software vendors (ISVs) that make their products and services available in different cloud platforms to benefit their end customers. AWS provides support for scenarios where organizations want to bring their own model to Amazon SageMaker or into Amazon SageMaker Canvas for predictions.

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AWS Machine Learning Blog

JULY 24, 2023

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. Inefficient architecture can lead to suboptimal utilization of the accelerators and unnecessarily high production cost.

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AWS Machine Learning Blog

DECEMBER 5, 2023

New and powerful large language models (LLMs) are changing businesses rapidly, improving efficiency and effectiveness for a variety of enterprise use cases. Enterprises need a responsible and safer way to send sensitive information to the models without needing to take on the often prohibitively high overheads of on-premises DevOps.

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Mlearning.ai

FEBRUARY 17, 2023

Have you ever felt bored when working on a machine learning project and it is struggling to manage the model version? Which is the best-performing model? MLflow is an open-source platform to manage the ML lifecycle , including experimentation, reproducibility, deployment, and a central model registry. Why use MLflow?

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Pragnakalp

JANUARY 12, 2023

Introduction Welcome to our tutorial on deploying a machine learning (ML) model on Amazon Web Services (AWS) Lambda using Docker. In this tutorial, we will walk you through the process of packaging an ML model as a Docker container and deploying it on AWS Lambda, a serverless computing service. So, let’s get started!

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