phData

JULY 17, 2023

We live in a world where vast amounts of data are being collected, and unprecedented compute power is available to extract value. The advancement of technology in large language models (LLMs), machine learning (ML), and data science can truly transform industries through insights and predictions.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2

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AWS Machine Learning Blog

NOVEMBER 21, 2024

Complete the following steps for manual deployment: Download these assets directly from the GitHub repository. Make sure you’re updating the data model ( updateTrackListData function) to handle your custom fields. The assets (JavaScript and CSS files) are available in our GitHub repository. Host them in your own S3 bucket.

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AWS Machine Learning Blog

NOVEMBER 10, 2023

Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. It provides key functionality that allows you to focus on the ML problem at hand while automatically keeping track of the trials and results. We use a Random Forest from SkLearn.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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Mlearning.ai

AUGUST 18, 2023

As an ML engineer you’re in charge of some code/model. MLOps cover all of the rest, how to track your experiments, how to share your work, how to version your models etc (Full list in the previous post. ). Not having a local model is not an excuse to throw organization, versioning and just good ol’ clean code patterns for.

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DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value.

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AWS Machine Learning Blog

MAY 4, 2023

Simple methods for time series forecasting use historical values of the same variable whose future values need to be predicted, whereas more complex, machine learning (ML)-based methods use additional information, such as the time series data of related variables. You should see the data imports in progress.

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AWS Machine Learning Blog

JUNE 5, 2023

In this post, we show you how to train the 7-billion-parameter BloomZ model using just a single graphics processing unit (GPU) on Amazon SageMaker , Amazon’s machine learning (ML) platform for preparing, building, training, and deploying high-quality ML models. Then, it starts the training job.

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DagsHub

DECEMBER 11, 2023

In addition to versioning code, teams can also version data, models, experiments and more. Released in 2022, DagsHub’s Direct Data Access (DDA for short) allows Data Scientists and Machine Learning engineers to stream files from DagsHub repository without needing to download them to their local environment ahead of time.

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Heartbeat

NOVEMBER 28, 2023

Introducing MLOps Machine learning (ML) is an essential tool for businesses of all sizes. However, deploying ML models in production can be complex and challenging. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. Second, ML models are constantly evolving.

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AWS Machine Learning Blog

MAY 31, 2024

This begins the process of converting the data stored in the S3 bucket into vector embeddings in your OpenSearch Serverless vector collection. Note: The syncing operation can take minutes to hours to complete, based on the size of the dataset stored in your S3 bucket.

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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. And since you are reading this article, the data scientists you support have probably reached out for help.

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

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Mlearning.ai

DECEMBER 22, 2023

Now, to download Mixtral, you must login into your account using an access token: huggingface-cli login --token YOUR_TOKEN We then need access to an IAM Role with the required permissions for Sagemaker. After finishing it, we can acces the model using from_pretrained method from transformers library. You can find here more about it.

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The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. You can learn more about the benefits of having a data pipeline in place here.

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Heartbeat

DECEMBER 27, 2023

The machine learning (ML) lifecycle defines steps to derive values to meet business objectives using ML and artificial intelligence (AI). Here are some details about these packages: jupyterlab is for model building and data exploration. catboost is the machine learning algorithm for model building. Flask==2.1.2

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phData

JULY 18, 2023

Why Migrate to a Modern Data Stack? Data teams can focus on delivering higher-value data tasks with better organizational visibility. Move Beyond One-off Analytics: The Modern Data Stack empowers you to elevate your data for advanced analytics and integration of AI/ML, enabling faster generation of actionable business insights.

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Mlearning.ai

JUNE 7, 2023

Then can download the neuron model and tokenizer config files from the above step and store them in the model directory, e.g script by overwriting the model_fn to load our neuron model and the predict_fn to create a text-classification pipeline. ! into the code/ directory of the model directory. ! copy inference.py

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AWS Machine Learning Blog

JANUARY 27, 2025

Amazon SageMake r provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. def predict_fn(data, model): normalized = preprocess_image(data) with torch.no_grad(): mask_ratio = 0.5 _, pred, mask = model(normalized, mask_ratio=mask_ratio) mask_img = model.unpatchify(mask.unsqueeze(-1).repeat(1,

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AWS Machine Learning Blog

MARCH 18, 2025

Embedding is usually performed by a machine learning (ML) model. The language model then generates a SQL query that incorporates the enterprise knowledge. Streamlit This open source Python library makes it straightforward to create and share beautiful, custom web apps for ML and data science. streamlit run app.py

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA reduces computational requirements and memory footprint, FSDP, a data/model parallelism technique, will help shard the model across all eight GPUs (one ml.p4d.24xlarge 24xlarge ), enabling training the model even more efficiently. Nishant Karve is a Sr.

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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. . This enables you to use Aurora for generative AI RAG-based use cases by storing vectors with the rest of the data.

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