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).

Machine Learning 153

Machine Learning Computer Vision Deep Learning Python 153

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.

Machine Learning 136

Machine Learning Data Scientist ML Python 136

AWS Machine Learning Blog

JUNE 3, 2024

Upload the dataset you downloaded in the prerequisites section. Choose Batch prediction and upload the downloaded file. Deploy the model The final (optional) step of the SageMaker Canvas workflow for ML models is deploying the model. Ryan Gomes is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

Generative AI 127

Generative AI Categorization Auto-complete Auto-classification 127

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.

Generative AI 137

Generative AI Prompt Engineer Prompt Engineering LLM 137

AWS Machine Learning Blog

OCTOBER 4, 2023

Solution overview Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices. You can also download the completed notebook here. For this post, we choose the Data Science 3.0

LLM 121

LLM Large Language Models ML Generative AI 121

AWS Machine Learning Blog

FEBRUARY 28, 2024

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. Outside of his work as a Data and ML engineer at Amazon, Sanjeeb Panda is an avid foodie and music enthusiast. format('parquet').option('path',

Metadata 142

Metadata Generative AI LLM NLP 142

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.

Generative AI 128

Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 128

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.

Data Scientist 103

Data Scientist Generative AI Machine Learning ML 103

AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. These geospatial capabilities open up a new world of possibilities for environmental monitoring.

Machine Learning 120

Machine Learning ML Data Scientist Robotics 120

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?

Machine Learning 72

Machine Learning Metadata ML Python 72

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.

Automation 88

Automation LLM DevOps Python 88

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.

Machine Learning 58

Machine Learning ML Deep Learning Robotics 58

AWS Machine Learning Blog

JULY 21, 2023

Amazon SageMaker makes it easier for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. The tool makes it easier to access geospatial data sources, run purpose-built processing operations, apply pre-trained ML models, and use built-in visualization tools faster and at scale.

Machine Learning 94

Machine Learning ML ML Engineer Data Scientist 94

AWS Machine Learning Blog

AUGUST 22, 2024

Use Python to preprocess, train, and test an LLM in Amazon Bedrock To begin, we need to download data and prepare an LLM in Amazon Bedrock. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. We use Python to do this.

Generative AI 131

Generative AI Metadata Python ML 131

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.

DevOps 52

DevOps Python ML ML Engineer 52

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.

ML 125

ML Automation Metadata Software Development 125

AWS Machine Learning Blog

SEPTEMBER 25, 2024

models with SageMaker JumpStart as follows: import requests import base64 def url_to_base64(image_url): # Download the image response = requests.get(image_url) if response.status_code != Single-image input You can set up vision-based reasoning tasks with Llama 3.2 200: return None # Encode the image content to base64 image_base64 = base64.b64encode(response.content).decode('utf-8')

Software Engineer 131

Software Engineer Software Development ML Python 131

Flipboard

MARCH 7, 2023

There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role. Depending on how many packages are installed and how large they are, the lifecycle script might even timeout.

Python 123

Python Data Science Data Scientist Automation 123

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.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

AWS Machine Learning Blog

APRIL 27, 2023

Download the sample code from the GitHub repository. Conclusion Modern state-of-the-art model architectures require an increasing number of resources from engineering staff (data scientists, ML engineers, MLOps engineers, and others) to actual infrastructure including storage, compute, memory, and accelerators.

Auto-complete 98

Auto-complete Deep Learning Machine Learning ML 98

AWS Machine Learning Blog

FEBRUARY 21, 2025

The concept of a compound AI system enables data scientists and ML engineers to design sophisticated generative AI systems consisting of multiple models and components. The synthetic data generation notebook automatically downloads the CUAD_v1 ZIP file and places it in the required folder named cuad_data.

LLM 99

LLM AI AI Data Scientist 99

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. Could you please tell us about the vision and inspiration behind this project?

ML 97

ML Python Machine Learning Metadata 97

DataRobot Blog

SEPTEMBER 13, 2022

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. At the same time, automated ML tools can augment your existing data professionals’ expertise without sacrificing their time. Download Now.

Data Scientist 52

Data Scientist Automation ML Machine Learning 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.

ML 91

ML Computer Vision Data Scientist Machine Learning 91

AWS Machine Learning Blog

FEBRUARY 11, 2025

download_file(s3_bucket, f"{key_prefix}/{key_filename}", key_filename) truck_jpg = "truck.jpg" #Download images. download_from_s3(key_filenames=[truck_jpg]) display(Image(filename=truck_jpg)) After you have your image and it is encoded, you can create masks for objects in the image. On the endpoint details page, choose Delete.

Computer Vision 83

Computer Vision ML Python Automation 83

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.

Data Science 52

Data Science AI Engineer Prompt Engineer Prompt Engineering 52

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.

Metadata 101

Metadata Computer Vision Machine Learning Data Scientist 101

The MLOps Blog

MAY 10, 2023

To save the model using ONNX, you need to have onnx and onnxruntime packages downloaded in your system. Here is an example of how you can convert the existing ML model to ONNX format. You can download this library with the help of the Python package installer. $ In this example, I’ll use the Neptune.

Python 105

Python Metadata ML Machine Learning 105

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 104

Generative AI AI AI Auto-classification 104

Dlabs.ai

OCTOBER 23, 2019

But times are changing — as are the dynamics of ML engineering. Single-threaded In the JavaScript library, single threads download synchronously, which might throttle performance. Even in the context of machine learning, most assumed JavaScript only had applications in data visualization: take the library D3.js, And in Node.js

Machine Learning 52

Machine Learning Convolutional Neural Networks Python ML 52

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

Auto-complete 119

Auto-complete ML Generative AI Deep Learning 119

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.

LLM 80

LLM ML Natural Language Processing AI 80

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.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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.

Data Drift 123

Data Drift Auto-complete ML Automation 123

DataRobot Blog

JANUARY 10, 2023

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. Built-in, Intuitive Cell Functions Promote Better Usability for Exploratory Analysis. Almost 90% of machine learning models never make it into production.

Auto-classification 52

Auto-classification Auto-complete Data Scientist Data Science 52

Viso.ai

FEBRUARY 11, 2024

One of the first steps is registering and downloading training data, and getting it into the system. Detectron2 Training Techniques In developing projects with Detectron2, it’s useful to look at how developers typically work. Another key task is addressing dependencies related to Detectron2.

Computer Vision 52

Computer Vision Robotics Deep Learning Large Language Models 52

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.

ETL 59

ETL ML Machine Learning Data Scientist 59