AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. Create a SageMaker Model Monitor schedule Next, you use the Amazon SageMaker Python SDK to create a model monitoring schedule. About the Authors Joe King is a Sr.

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Towards AI

JANUARY 6, 2025

Artificial Intelligence graduate certificate by STANFORD SCHOOL OF ENGINEERING Artificial Intelligence graduate certificate; taught by Andrew Ng, and other eminent AI prodigies; is a popular course that dives deep into the principles and methodologies of AI and related fields. Generative AI with LLMs course by AWS AND DEEPLEARNING.AI

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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

DECEMBER 10, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. Discover Pixtral 12B in SageMaker JumpStart You can access Pixtral 12B through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK.

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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

APRIL 25, 2023

With this new capability of the SageMaker Python SDK, data scientists can onboard their ML code to the SageMaker Training platform in a few minutes. In this release, you can run your local machine learning (ML) Python code as a single-node Amazon SageMaker training job or multiple parallel jobs.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the SageMaker Python SDK. Discover Llama 3.2

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Towards AI

FEBRUARY 20, 2025

But who exactly is an LLM developer, and how are they different from software developers and ML engineers? Laufeyson5190 is learning ML basics and is inviting other beginners to create a study group. If you are skilled in Python or computer vision, diffusion models, or GANS, you might be a great fit.

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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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AWS Machine Learning Blog

FEBRUARY 12, 2025

medium instance with a Python 3 (ipykernel) kernel. About the authors Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. Moran Beladev is a Senior ML Manager at Booking.com. Manos Stergiadis is a Senior ML Scientist at Booking.com. For details, refer to Creating an AWS account.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

This container image has all the most popular ML frameworks supported by SageMaker, along with SageMaker Python SDK , boto3 , and other AWS and data science specific libraries installed. In this example, Code Editor can be used by an ML engineering team who needs advanced IDE features to debug their code and deploy the endpoint.

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AWS Machine Learning Blog

JANUARY 25, 2023

Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models. We use the SageMaker Python SDK for this purpose. He focuses on Deep learning including NLP and Computer Vision domains.

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AWS Machine Learning Blog

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

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AWS Machine Learning Blog

OCTOBER 4, 2024

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. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

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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. In this example, you will use a Python function. Download the complete Python file , including the function and all imported libraries.

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AWS Machine Learning Blog

OCTOBER 2, 2023

In this series, we walk you through the process of architecting and building an integrated end-to-end MLOps pipeline for a computer vision use case at the edge using SageMaker, AWS IoT Greengrass, and the AWS Cloud Development Kit (AWS CDK). The following diagram illustrates what this could look like for our computer vision pipeline.

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Pickl AI

APRIL 19, 2023

Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Job market will experience a rise of 13% by 2026 for ML Engineers Why is Machine Learning Important? It includes learning Python, R, Java, C++, SQL, etc. Consequently.

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AWS Machine Learning Blog

NOVEMBER 20, 2023

KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker' amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'

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Viso.ai

FEBRUARY 27, 2024

Since its inception in 2015, the YOLO (You Only Look Once) object-detection algorithm has been closely followed by tech enthusiasts, data scientists, ML engineers, and more, gaining a massive following due to its open-source nature and community contributions. YOLOv5 uses YAML files instead of CFG files in the model configurations.

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AWS Machine Learning Blog

MAY 5, 2023

Voxel51 is the company behind FiftyOne, the open-source toolkit for building high-quality datasets and computer vision models. 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.

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Metadata Computer Vision Machine Learning Data Scientist 101

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence.

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Pickl AI

JANUARY 23, 2023

It supports languages like Python and R and processes the data with the help of data flow graphs. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs. Keras supports a high-level neural network API written in Python. Cons Low level computation cannot be handled by keras.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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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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Viso.ai

JANUARY 30, 2024

” We will cover the most important model training errors, such as: Overfitting and Underfitting Data Imbalance Data Leakage Outliers and Minima Data and Labeling Problems Data Drift Lack of Model Experimentation About us: At viso.ai, we offer the Viso Suite, the first end-to-end computer vision platform.

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

ODSC - Open Data Science

JANUARY 10, 2024

You will also become familiar with the concept of LLM as a reasoning engine that can power your applications, paving the way to a new landscape of software development in the era of Generative AI. Stable Diffusion: A New Frontier for Text-to-Image Paradigm Sandeep Singh | Head of Applied AI/Computer Vision | Beans.ai

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Large Language Models Data Science Deep Learning LLM 52

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.

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Data Scientist ML Computer Vision AI 64

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.

Data Scientist 59

Data Scientist ML Computer Vision AI 59

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.

Data Scientist 52

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The MLOps Blog

OCTOBER 3, 2023

How did you manage to jump from a more analytical, scientific type of role to a more engineering one? Mikiko Bazeley: Most people are really surprised to hear that my background in college was not computer science. I actually did not pick up Python until about a year before I made the transition to a data scientist role.

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AWS Machine Learning Blog

APRIL 19, 2023

Most publicly available fraud detection datasets don’t provide this information, so we use the Python Faker library to generate a set of transactions covering a 5-month period. About the Authors Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions. This dataset contains 5.4

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AWS Machine Learning Blog

JULY 23, 2024

You can now use state-of-the-art model architectures, such as language models, computer vision models, and more, without having to build them from scratch. models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: Amazon SageMaker Studio and the SageMaker Python SDK. Discover Llama 3.1

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

OCTOBER 17, 2024

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. The storage resources for SageMaker Studio spaces are Amazon Elastic Block Store (Amazon EBS) volumes, which offer low-latency access to user data like notebooks, sample data, or Python/Conda virtual environments.

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