The MLOps Blog

MAY 10, 2023

In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models. Now let’s see how we can save our model.

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AWS Machine Learning Blog

OCTOBER 4, 2024

You can download the generated images directly from the UI or check the image in your S3 bucket. About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based solutions.

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AWS Machine Learning Blog

APRIL 27, 2023

Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. 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.

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AWS Machine Learning Blog

MAY 13, 2024

Because we used only the radiology report text data, we downloaded just one compressed report file (mimic-cxr-reports.zip) from the MIMIC-CXR website. He has two graduate degrees in physics and a doctorate in engineering. Srushti Kotak is an Associate Data and ML Engineer at AWS Professional Services.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

SageMaker AI starts and manages all the necessary Amazon Elastic Compute Cloud (Amazon EC2) instances for us, supplies the appropriate containers, downloads data from our S3 bucket to the container and uploads and runs the specified training script, in our case fine_tune_llm.py. Manos Stergiadis is a Senior ML Scientist at Booking.com.

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The MLOps Blog

JANUARY 26, 2024

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline?

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. The journey of providers FM providers need to train FMs, such as deep learning models.

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The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders.

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AWS Machine Learning Blog

FEBRUARY 9, 2023

Rather than downloading the data to a local machine for inferences, SageMaker does all the heavy lifting for you. SageMaker automatically downloads and preprocesses the satellite image data for the EOJ, making it ready for inference. This land cover segmentation model can be run with a simple API call.

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Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. To download it, you will use the Kaggle package. Create your API keys on your Account’s Settings page and it will download a JSON file. We pay our contributors, and we don’t sell ads.

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AWS Machine Learning Blog

OCTOBER 22, 2024

It accelerates your generative AI journey from prototype to production because you don’t need to learn about specialized workflow frameworks to automate model development or notebook execution at scale. Download the pipeline definition as a JSON file to your local environment by choosing Export at the bottom of the visual editor.

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TheSequence

JULY 12, 2023

At Cruise, we noticed a wide gap between the complexity of cloud infrastructure, and the needs of the ML workforce. ML Engineers want to focus on writing Python logic, and visualizing the impact of their changes quickly. I can see how every Fortune 500 company in 5 years will do some amount of deep learning (e.g.

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Mlearning.ai

AUGUST 18, 2023

As an ML engineer you’re in charge of some code/model. Also same expertise rule applies for an ML engineer, the more versed you are in MLOps the better you can foresee issues, fix data/model bugs and be a valued team member. Running invoke from cmd: $ inv download-best-model We’re decoupling MLOps from actual ML code.

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DataRobot Blog

SEPTEMBER 13, 2022

These might include—but are not limited to—deep learning, image recognition and natural language processing. Platforms like DataRobot AI Cloud support business analysts and data scientists by simplifying data prep, automating model creation, and easing ML operations ( MLOps ). Download Now. Download Now.

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Dlabs.ai

OCTOBER 23, 2019

Even in the context of machine learning, most assumed JavaScript only had applications in data visualization: take the library D3.js, But times are changing — as are the dynamics of ML engineering. And it’s become common practice for developers to write machine learning functions using common web-scripting languages.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

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AWS Machine Learning Blog

MARCH 1, 2023

Machine learning (ML) engineers can fine-tune and deploy text-to-semantic-segmentation and in-painting models based on pre-trained CLIPSeq and Stable Diffusion with Amazon SageMaker. We began by having the user upload a fashion image, followed by downloading and extracting the pre-trained model from CLIPSeq.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. test_cases/10.FSDP create_conda_env.sh

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Viso.ai

FEBRUARY 11, 2024

Detectron2 is a deep learning model built on the Pytorch framework, which is said to be one of the most promising modular object detection libraries being pioneered. One of the first steps is registering and downloading training data, and getting it into the system.

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AWS Machine Learning Blog

DECEMBER 2, 2024

These improvements are available across a wide range of SageMaker’s Deep Learning Containers (DLCs), including Large Model Inference (LMI, powered by vLLM and multiple other frameworks), Hugging Face Text Generation Inference (TGI), PyTorch (Powered by TorchServe), and NVIDIA Triton.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Download the notebook file to use in this post. He helps internal teams and customers in scaling generative AI, machine learning, and analytics solutions. Ginni Malik is a Senior Data & ML Engineer with AWS Professional Services. This will open a new browser tab for SageMaker Studio Classic.

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AWS Machine Learning Blog

DECEMBER 2, 2024

SageMaker Large Model Inference (LMI) is deep learning container to help customers quickly get started with LLM deployments on SageMaker Inference. One of the primary bottlenecks in the deployment process is the time required to download and load containers when scaling up endpoints or launching new instances.

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