AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. Download the template.yml file to your computer.

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The MLOps Blog

MAY 10, 2023

How to save a trained model in Python? In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. The first way to save an ML model is by using the pickle file. Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Download the notebook file to use in this post. data # Assing local directory path to a python variable local_data_path = "./data/" data/" # Assign S3 bucket name to a python variable. Ginni Malik is a Senior Data & ML Engineer with AWS Professional Services. Run the SageMaker Studio application.

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AWS Machine Learning Blog

APRIL 29, 2024

Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner. It provides an approachable, robust Python API for the full infrastructure stack of ML/AI, from data and compute to workflows and observability. Choose Create new stack.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017.

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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

NOVEMBER 9, 2023

Download and save the publicly available UCI Mammography Mass dataset to the S3 bucket you created earlier in the dev account. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Set up an S3 bucket to maintain the Terraform state in the prod account.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data. This example uses the Python client to identify and download imagery needed for the analysis.

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Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

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Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. This is suitable for making a variety of Python applications with other dependencies being added to it at the user’s convenience.

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The MLOps Blog

MARCH 15, 2023

AWS Glue consists of a metadata repository known as Glue catalog, an engine to generate the Scala or Python code for the ETL Job, and also does job monitoring, scheduling, and so on. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem. Redshift, S3, and so on.

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AWS Machine Learning Blog

JUNE 23, 2023

FastAPI is a modern, high-performance web framework for building APIs with Python. It stands out when it comes to developing serverless applications with RESTful microservices and use cases requiring ML inference at scale across multiple industries. The download time can take around 3–5 minutes.

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Mlearning.ai

AUGUST 18, 2023

TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). As an ML engineer you’re in charge of some code/model. Python has different flavors, and some freedom about the location of scripts and components. Replace MLOps with program .Source

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AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle. You can use SageMaker Data Wrangler to simplify and streamline dataset preprocessing and feature engineering by either using built-in, no-code transformations or customizing with your own Python scripts.

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Flipboard

OCTOBER 9, 2023

Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more. the image).

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Set up the SDK for Python (Boto3). medium instance with the Python 3 (Data Science) kernel. You can download the datasets and store them in Amazon Simple Storage Service (Amazon S3). About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. Install the AWS Command Line Interface (AWS CLI).

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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

SEPTEMBER 25, 2024

models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the SageMaker Python SDK. SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the ML development lifecycle. Discover Llama 3.2 Deploy Llama 3.2

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AWS Machine Learning Blog

OCTOBER 4, 2023

In this post, we walk you through deploying a Falcon large language model (LLM) using Amazon SageMaker JumpStart and using the model to summarize long documents with LangChain and Python. SageMaker is a HIPAA-eligible managed service that provides tools that enable data scientists, ML engineers, and business analysts to innovate with ML.

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AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. You can apply tags to models and import jobs to keep track of different projects and versions.

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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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DataRobot Blog

JANUARY 10, 2023

We will be writing code in Python, but DataRobot Notebooks also supports R if that’s your preferred language. For this experiment, we are going to ingest the hospital readmissions data from a CSV file downloaded to the notebook’s working directory using a shell command. Seamless DataRobot API Integration for Hassle-free Workflows.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

By directly integrating with Amazon Managed Service for Prometheus and Amazon Managed Grafana and abstracting the management of hardware failures and job resumption, SageMaker HyperPod allows data scientists and ML engineers to focus on model development rather than infrastructure management. test_cases/10.FSDP create_conda_env.sh

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AWS Machine Learning Blog

APRIL 27, 2023

Note all necessary software, drivers, and tools have already been installed on the DLAMIs, and only the activation of the Python environment is needed to start working with the tutorial. Download the sample code from the GitHub repository. We reference the CustomOps functionality available in Neuron as “Neuron CustomOps.”

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Dlabs.ai

OCTOBER 23, 2019

There’s a misconception in the world of machine learning (ML). Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. But times are changing — as are the dynamics of ML engineering. And in Node.js

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc.,

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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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

JUNE 14, 2023

You can integrate a Data Wrangler data preparation flow into your ML workflows to simplify and streamline data preprocessing and feature engineering using little to no coding. You can also add your own Python scripts and transformations to customize workflows. Python code file. Choose the file browser icon view the path.

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