AWS Machine Learning Blog

APRIL 5, 2023

Amazon Rekognition makes it easy to add this capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example. client('rekognition') s3 = boto3.client('s3')

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Ref S3BucketName, '/*']] - PolicyName: SNSPublish PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - 'sns:Publish' Resource: !Ref Ref S3BucketName, '/*']] - PolicyName: SNSPublish PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - 'sns:Publish' Resource: !Ref Join ['', ['arn:aws:s3:::',

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It’s attached to a ML compute instance whenever a Space is run.

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AWS Machine Learning Blog

MAY 12, 2023

Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio.

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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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AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. The following steps assume that you already have a valid Python 3 and JupyterLab environment (this extension works with JupyterLab v3.0 or higher).

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AWS Machine Learning Blog

AUGUST 22, 2023

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. What is federated learning?

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AWS Machine Learning Blog

NOVEMBER 15, 2024

jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).

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Flipboard

NOVEMBER 15, 2024

The following sections describe the implementation details of each approach using the Python programming language. Python and boto3. The following code snippet uses the AWS Glue API through the AWS SDK for Python (Boto3) to retrieve tables for a chosen database and then prints them on the screen for validation.

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AWS Machine Learning Blog

MAY 8, 2023

With Amazon SageMaker , you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. The changes to the MLflow Python SDK are available for everyone since MLflow version 1.30.0.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Create a role named sm-build-role with the following trust policy, and add the policy sm-build-policy that you created earlier: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "codebuild.amazonaws.com" }, "Action": "sts:AssumeRole" } ] } Now, let’s review the steps in CloudShell. base-ubuntu18.04

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ODSC - Open Data Science

FEBRUARY 28, 2023

To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making. Narrowing the communications gap between humans and machines is one of SAS’s leading projects in their work with NLP.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1) Happy learning.

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AWS Machine Learning Blog

MARCH 30, 2023

Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. mkdir $data_dir !cd

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. or later image versions.

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AWS Machine Learning Blog

APRIL 22, 2024

Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists, and cluster administrators. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. They edit their own files, run their own jobs, and want to avoid impacting each other’s work.

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Towards AI

JULY 21, 2023

But who knows… 3301’s Cicada project started with a random 4chan post in 2012 leading many thrill seekers, with a cult-like following, on a puzzle hunt that encompassed everything from steganography to cryptography. It uses the 2 model architecture: sparse search via Elasticsearch and then a ranker ML model.

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AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker JumpStart helps you get started with machine learning (ML) by providing fully customizable solutions and one-click deployment and fine-tuning of more than 400 popular open-weight and proprietary generative AI models. Let me know if you'd like me to elaborate on any of these action items!

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AWS Machine Learning Blog

MARCH 15, 2024

You can load the data to the DynamoDB table using Python code in a SageMaker notebook. Return to the SageMaker notebook and write a Python code to set up a connection to DynamoDB using the Boto3 library in Python. There are many FMs available via the Amazon Bedrock Python SDK. Copy your prompt files to the python folder.

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ODSC - Open Data Science

JUNE 8, 2023

Taipy The inspiration for this open-source software for Python developers was the frustration felt by those who were trying, and struggling, to bring AI algorithms to end-users. To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making.

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AWS Machine Learning Blog

MAY 5, 2023

In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Amazon Kendra is an intelligent search service powered by ML, and Amazon Rekognition is an ML service that can identify objects, people, text, scenes, and activities from images or videos.

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AWS Machine Learning Blog

DECEMBER 29, 2022

revolution has shown the value and importance of machine learning (ML) across verticals and environments, with more impact on manufacturing than possibly any other application. These are the real-time datasets that will be used for inferencing with the ML model. For Runtime , choose Python 3.9. The last decade of the Industry 4.0

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AWS Machine Learning Blog

JULY 2, 2024

The following code snippet demonstrates how to call the retrieve_and_generate API using the Boto3 library in Python. Streamlit sample app To showcase the interaction between doctors and the knowledge base, we developed a user-friendly web application using Streamlit , a popular open source Python library for building interactive data apps.

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Pickl AI

APRIL 27, 2023

AI and machine learning (ML) technologies enable businesses to analyze unstructured data. AI and ML technologies work cohesively with data analytics and business intelligence (BI) tools. Python Might Go Viral Yes, you read it right. Add to this, Python has a friendly learning curve for beginners.

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AWS Machine Learning Blog

JULY 10, 2024

Sovik Kumar Nath is an AI/ML and Generative AI Senior Solutions Architect with AWS. Fang Liu is a principal machine learning engineer at Amazon Web Services, where he has extensive experience in building AI/ML products using cutting-edge technologies. Yanyan graduated from Texas A&M University with a PhD in Electrical Engineering.

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Heartbeat

JULY 27, 2023

If you’ve only been programming in Python land your whole life, and have no clue what I mean when I say map, you can think of it as no different than a Python dictionary. For example, “Features” would have to become: Again, if you’ve only ever programmed in Python, or something of the sort, this might seem strange to you.

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The MLOps Blog

JANUARY 11, 2023

Figure 1: The high-level overview of the MLOps environment architectural design built with AWS services listed in the left side | Source: Author I went through several rounds of interviews and discussions with different teams to hear what they expect to achieve, mainly the ML team, and shared with them the expectations of other teams as well.

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Pickl AI

APRIL 28, 2023

With more than 650% growth since 2012, Data Science has emerged as one of the most sought-after technologies. With the new developments in this domain, Data Science presents a picture of futuristic technology. At the same time, it has also emerged as one of the highest-paying job profiles.

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Mlearning.ai

JUNE 18, 2023

While Google Colab’s main focus is running Python code using Jupyter notebooks, with magic commands non-Python code can also be executed. This opens up plenty of opportunities, as many AI models are not written in Python. We will use Python to help us do this. which is accessible from Google Colab.

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Mlearning.ai

MARCH 9, 2023

2012; Otsu, 1979; Long et al., Methodology In this study, we used the publicly available PASCAL VOC 2012 dataset (Everingham et al., We implemented the MBD approach using the Python programming language, with the scikit-learn and NetworkX libraries for feature selection and structure learning, respectively. Goodfellow, I.

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O'Reilly Media

JULY 25, 2023

For instance, problems like “write a Python function that takes a list of names, splits them by first and last name, and sorts by last name.” It’s well-known that current AI tools can solve these kinds of problems even better than many students can. This choice also inspired me to call my project Swift Papers.

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AWS Machine Learning Blog

APRIL 23, 2024

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. ML model development often involves slow iteration cycles as developers switch between coding, training, and deployment. Python 3.10

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AWS Machine Learning Blog

SEPTEMBER 7, 2023

Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, and deployment.

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AWS Machine Learning Blog

JULY 18, 2024

Create a Lambda function to invoke the Amazon Bedrock model On the Lambda console, create a function (for example, invoke_bedrock_claude3 ), choose Python 3.12 Create a Lambda function to insert records into the DynamoDB table On the Lambda console, create a Lambda function (for example, insert_into_dynamodb ), choose Python 3.12

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AWS Machine Learning Blog

OCTOBER 11, 2024

data # Assing local directory path to a python variable local_data_path = "./data/" data/" # Assign S3 bucket name to a python variable. This was created in Step-2 above. This bucket will be used as source for vector databases and uploading source files.

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AWS Machine Learning Blog

OCTOBER 24, 2024

These functions will be used inside a Spark Python user-defined function (UDF) in later cells. He is passionate about distributed computing and using ML/AI for designing and building end-to-end solutions to address customers’ Data Integration needs. It requires IAM permission for OpenSearch Service Serverless.

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