AWS Machine Learning Blog

FEBRUARY 11, 2025

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 machine learning (ML) development lifecycle. Discover Meta SAM 2.1

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Computer Vision ML Python Automation 80

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.

Machine Learning 104

Machine Learning Python ML Automation 104

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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Machine Learning Data Scientist ML Python 128

AWS Machine Learning Blog

APRIL 7, 2025

Discover Llama 4 models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the Amazon SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and use SageMaker JumpStart models. b64encode(img).decode('utf-8')

Machine Learning 62

Machine Learning Large Language Models Python Automation 62

AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. We use Python to do this.

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Generative AI Metadata Python ML 114

AWS Machine Learning Blog

JULY 24, 2024

Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. You can create workflows with SageMaker Pipelines that enable you to prepare data, fine-tune models, and evaluate model performance with simple Python code for each step.

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LLM ML Generative AI Machine Learning 121

AWS Machine Learning Blog

FEBRUARY 21, 2025

We use DSPy (Declarative Self-improving Python) to demonstrate the workflow of Retrieval Augmented Generation (RAG) optimization, LLM fine-tuning and evaluation, and human preference alignment for performance improvement. Examples are similar to Python dictionaries but with added utilities such as the dspy.Prediction as a return value.

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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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Large Language Models ML Python Machine Learning 96

AWS Machine Learning Blog

APRIL 11, 2023

Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit , developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. It also stores the source files (.tar.gz

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ML Engineer ML Data Scientist Machine Learning 83

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

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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LLM Large Language Models ML Data Scientist 106

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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Software Engineer Software Development ML Python 121

AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. 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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Automation LLM DevOps Python 79

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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Metadata Generative AI LLM NLP 125

AWS Machine Learning Blog

FEBRUARY 12, 2025

medium instance with a Python 3 (ipykernel) kernel. 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.

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LLM ML Natural Language Processing Machine Learning 75

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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DevOps Python ML ML Engineer 52

TheSequence

JULY 12, 2023

. 🛠 ML Work Your most recent project is Sematic, which focuses on enabling Python-based orchestration of ML pipelines. At Cruise, we noticed a wide gap between the complexity of cloud infrastructure, and the needs of the ML workforce. Could you please tell us about the vision and inspiration behind this project?

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ML Python Machine Learning Metadata 97

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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Generative AI Prompt Engineering Prompt Engineer LLM 123

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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Data Scientist Generative AI Machine Learning ML 98

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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ML Automation Metadata Software Development 117

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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Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 115

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

Machine Learning 52

Machine Learning Convolutional Neural Networks Python ML 52

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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ML Computer Vision Data Scientist Machine Learning 78

ODSC - Open Data Science

NOVEMBER 25, 2024

We also have plenty of slides from the virtual side of ODSC West that you can see and download here. You can check out the top session recordings here if you have a subscription to the Ai+ Training platform.

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Data Science AI Engineer Machine Learning Prompt Engineering 52

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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Machine Learning Metadata Data Scientist Data Quality 134

Heartbeat

JULY 17, 2023

Prerequisites To follow along with this tutorial, make sure you: Use a Google Colab Notebook to follow along Install these Python packages using pip: CometML , PyTorch, TorchVision, Torchmetrics and Numpy, Kaggle %pip install - upgrade comet_ml>=3.10.0 !pip To download it, you will use the Kaggle package.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

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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Auto-complete Deep Learning Machine Learning ML 83

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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Metadata Computer Vision Machine Learning Data Scientist 87

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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Data Drift Auto-complete ML Automation 116

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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Auto-complete ML Generative AI Deep Learning 103

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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Auto-classification Auto-complete Data Scientist Data Science 52

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.

Generative AI 93

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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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ETL ML Machine Learning Data Scientist 59

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 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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ML Data Scientist Automation DevOps 91

AWS Machine Learning Blog

DECEMBER 2, 2024

Container Caching addresses this scaling challenge by pre-caching the container image, eliminating the need to download it when scaling up. We discuss how this innovation significantly reduces container download and load times during scaling events, a major bottleneck in LLM and generative AI inference.

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Generative AI Machine Learning Large Language Models ML Engineer 91

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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Metadata Generative AI LLM Data Ingestion 98