AWS Machine Learning Blog

AUGUST 26, 2024

You can download and install Docker from Docker’s official website. This instance will be used for various tasks such as video processing and data preparation. For instructions to install FFmpeg on the Windows EC2 instance, refer to Download FFmpeg. Generate avatar videos: python create_pose_videos.py

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AssemblyAI

JUNE 24, 2024

youtube-dl-exec wraps the yt-dlp CLI tool which lets you retrieve information about YouTube videos and download them. tsx lets you execute TypeScript code without additional setup npm install --save assemblyai youtube-dl-exec tsx You must also install Python 3.7 Python skills are essential. Come back later to fill gaps.

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Mlearning.ai

APRIL 6, 2023

Tweets inference data pipeline architecture Tweets Inference Data Pipeline Architecture (Screenshot by Author) The workflow performs the following tasks: Download Tweets Dataset: Download the tweets dataset from the S3 bucket. In this blog post, we will install Apache-Airflow using the Python package.

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AssemblyAI

JUNE 24, 2024

youtube-dl-exec wraps the yt-dlp CLI tool which lets you retrieve information about YouTube videos and download them. tsx lets you execute TypeScript code without additional setup npm install --save assemblyai youtube-dl-exec tsx You must also install Python 3.7 Python skills are essential. Come back later to fill gaps.

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AWS Machine Learning Blog

AUGUST 21, 2024

In the following sections, we provide a detailed, step-by-step guide on implementing these new capabilities, covering everything from data preparation to job submission and output analysis. This use case serves to illustrate the broader potential of the feature for handling diverse data processing tasks.

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AWS Machine Learning Blog

JULY 24, 2024

This is where MLflow can help streamline the ML lifecycle, from data preparation to model deployment. 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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AWS Machine Learning Blog

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

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phData

OCTOBER 11, 2023

In this blog, we will focus on integrating Power BI within KNIME for enhanced data analytics. KNIME and Power BI: The Power of Integration The data analytics process invariably involves a crucial phase: data preparation. This phase demands meticulous customization to optimize data for analysis.

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Heartbeat

MARCH 20, 2023

A guide to train YoloV7 model on custom dataset using Python Source:Author Introduction Deep Learning (DL) technologies are now being widely adopted by different organizations that want to improve their services in no time along with great accuracy. For the image annotation, you can use the LabelImg tool , while Python 3.9

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Towards AI

JUNE 27, 2023

Its seamless integration capabilities make it highly compatible with numerous other Python libraries, which is why Scikit Learn is favored by many in the field for tackling sophisticated machine learning problems. PyTorch PyTorch, a Python-based machine learning library, stands out among its peers in the machine learning tools ecosystem.

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DrivenData Labs

NOVEMBER 18, 2024

Competition runtime limits ¶ Before preparing your submission, please ensure you understand the runtime environment and its constraints: Your submission must run using Python 3.12 The official runtime replaces this with the actual test data. Prepare your submission : Save all submission files (e.g.,

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AWS Machine Learning Blog

NOVEMBER 19, 2024

Solution overview This solution uses multiple features of SageMaker and Amazon Bedrock, and can be divided into four main steps: Data analysis and preparation – In this step, we assess the available data, understand how it can be used to develop solution, select data for fine-tuning, and identify required data preparation steps.

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DataRobot Blog

JUNE 16, 2022

The DataRobot provider for Apache Airflow is a Python package built from source code available in a public GitHub repository and published in PyPi (The Python Package Index). The integration uses the DataRobot Python API Client , which communicates with DataRobot instances via REST API. DataRobot Python API Client >= 2.27.1.

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Towards AI

JANUARY 31, 2023

I downloaded 50 samples from each, but something unfortunate happened — all the images I collected got deleted! I also had to make sure that I downloaded only good photos of the different textiles I was interested in. I also had to make sure that I downloaded only good photos of the different textiles I was interested in.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

We cover two approaches: using the Amazon SageMaker Studio UI for a no-code solution, and using the SageMaker Python SDK. It’s essential to review and adhere to the applicable license terms before downloading or using these models to make sure they’re suitable for your intended use case. Vision models. You can access the Meta Llama 3.2

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AWS Machine Learning Blog

AUGUST 1, 2024

Meta Llama3 8B is a gated model on Hugging Face, which means that users must be granted access before they’re allowed to download and customize the model. QLoRA quantizes a pretrained language model to 4 bits and attaches smaller low-rank adapters (LoRA), which are fine-tuned with our training data.

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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.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Prepare the dataset for fine-tuning We use the low-resource language Marathi for the fine-tuning task. Using the Hugging Face datasets library, you can download and split the Common Voice dataset into training and testing datasets. The source code associated with this implementation can be found on GitHub.

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Flipboard

AUGUST 17, 2023

For Prepare template , select Template is ready. Choose Choose File and navigate to the location on your computer where the CloudFormation template was downloaded and choose the file. If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select.

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AWS Machine Learning Blog

DECEMBER 13, 2023

We create an automated model build pipeline that includes steps for data preparation, model training, model evaluation, and registration of the trained model in the SageMaker Model Registry. Download the template.yml file to your computer. Upload the template you downloaded. Choose Create a new portfolio. Choose Review.

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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AWS Machine Learning Blog

MAY 29, 2024

Figure 1: LLaVA architecture Prepare data When it comes to fine-tuning the LLaVA model for specific tasks or domains, data preparation is of paramount importance because having high-quality, comprehensive annotations enables the model to learn rich representations and achieve human-level performance on complex visual reasoning challenges.

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Towards AI

JULY 19, 2023

Installation Installation is quite simple* Clone the library* Run installation script Support available for▹ Python — 3.6▹ Data Preparation The Training dataset is labeled as per Pascal VOC format (XML files) PASCAL-VOC Format. With Monk, it is easier to do the same using simple pythonic syntax. Cuda — 9.0,

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AWS Machine Learning Blog

FEBRUARY 2, 2023

You can download the endzone and sideline videos , and also the ground truth labels. We use OpenCV for reading, writing, and manipulating image data in Python. In particular, we focus on deduplicating and visualizing videos with the ID 57583_000082 in endzone and sideline views. astype('str') + '_' + df['playID'].astype('str').str.zfill(6)

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Prerequisites The following are prerequisites for completing the walkthrough in this post: An AWS account Familiarity with SageMaker concepts, such as an Estimator, training job, and HPO job Familiarity with the Amazon SageMaker Python SDK Python programming knowledge Implement the solution The full code is available in the GitHub repo.

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Mlearning.ai

MARCH 31, 2023

Check one of my previous stories if you want to learn how to use YOLOv5 with Python or C++. In this story, we will not use one of those high performing off-the-shelf object detectors but develop a new one ourselves, from scratch, using plain python, OpenCV, and Tensorflow. This is basically the path in which we are going to walk here.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

Solution overview In this solution, we start with data preparation, where the raw datasets can be stored in an Amazon Simple Storage Service (Amazon S3) bucket. We provide a Jupyter notebook to preprocess the raw data and use the Amazon Titan Multimodal Embeddings model to convert the image and text into embedding vectors.

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AWS Machine Learning Blog

MAY 25, 2023

Dockerfile requirements.txt Create an Amazon Elastic Container Registry (Amazon ECR) repository in us-east-1 and push the container image created by the downloaded Dockerfile. For more information, refer to Granting Data Catalog permissions using the named resource method. We have completed the data preparation step.

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AWS Machine Learning Blog

MARCH 6, 2023

Data preparation LLM developers train their models on large datasets of naturally occurring text. Popular examples of such data sources include Common Crawl and The Pile. An LLM’s eventual quality significantly depends on the selection and curation of the training data.

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Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

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phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. Download a free PDF by filling out the form.

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PyImageSearch

NOVEMBER 6, 2023

Jump Right To The Downloads Section Image Segmentation with U-Net in PyTorch: The Grand Finale of the Autoencoder Series Introduction Image segmentation is a pivotal task in computer vision where each pixel in an image is assigned a specific label, effectively dividing the image into distinct regions. Looking for the source code to this post?

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The MLOps Blog

JUNE 27, 2023

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., The entire model can be downloaded to your source code’s runtime with a single line of code. and programmatically via the Kolena Python client.

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AWS Machine Learning Blog

JULY 16, 2024

The NVIDIA NeMo Framework provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment. To get around this, you can put the launcher scripts in the head node and the results and data folder in the file system that the compute nodes have access to.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

We then also cover how to fine-tune the model using SageMaker Python SDK. It’s essential to review and adhere to the applicable license terms before downloading or using these models to make sure they’re suitable for your intended use case. FMs through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK.

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