Towards AI

JUNE 27, 2023

Let’s get started with the best machine learning (ML) developer tools: TensorFlow TensorFlow, developed by the Google Brain team, is one of the most utilized machine learning tools in the industry. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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AWS Machine Learning Blog

MAY 1, 2024

Using the Neuron Distributed library with SageMaker SageMaker is a fully managed service that provides developers, data scientists, and practitioners the ability to build, train, and deploy machine learning (ML) models at scale. Cluster update is currently enabled for the TRN1 instance family as well as P and G GPU-based instance types.

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AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). Typical unsupervised learning tasks include clustering (e.g., It’s a fascinating field that can seem daunting at first, but I assure you, with the right mindset and resources, anyone can master it.

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AWS Machine Learning Blog

OCTOBER 18, 2024

Business challenge Today, many developers use AI and machine learning (ML) models to tackle a variety of business cases, from smart identification and natural language processing (NLP) to AI assistants. You can train foundation models (FMs) for weeks and months without disruption by automatically monitoring and repairing training clusters.

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. When an On-Demand job is launched, it goes through five phases: Starting, Downloading, Training, Uploading, and Completed.

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Give this technique a try to take your team’s ML modelling to the next level.

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Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. As part of a single cluster run, you can spin up a cluster of Trn1 instances with Trainium accelerators. Trn1 UltraClusters can host up to 30,000 Trainium devices and deliver up to 6 exaflops of compute in a single cluster.

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Mlearning.ai

MAY 19, 2023

Here is HuggingFace Link: [link] From the Mosaic ML paper. Here is the HuggingFace Link: [link] From the Mosaic ML paper. This approach offers several benefits, including the elimination of the need to download the entire dataset before commencing training. This model was trained with 9.6M This model was trained with 86M tokens.

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AWS Machine Learning Blog

JUNE 11, 2024

In these cases, the model sizes are smaller, which means the communication overhead with GPUs or ML accelerator instances outweighs their compute performance benefits. As early adopters of Graviton for ML workloads, it was initially challenging to identify the right software versions and the runtime tunings.

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster.

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AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

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Mlearning.ai

NOVEMBER 6, 2023

Downloading Requirements I recommend installing Hadoop on using the terminal it provides a easy way to check if your installation progressed successfully. To open the terminal on most Ubuntu systems the command is Ctrl+Alt+T once the terminal is opened we can start downloading the requirements using the command. tar -xvzf hadoop-3.3.6.tar.gz

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AWS Machine Learning Blog

JUNE 7, 2023

Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. The distributed training workshop illustrates the steps to set up the distributed training cluster.

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AWS Machine Learning Blog

MAY 23, 2024

By distributing experts across workers, expert parallelism addresses the high memory requirements of loading all experts on a single device and enables MoE training on a larger cluster. The following figure offers a simplified look at how expert parallelism works on a multi-GPU cluster.

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AWS Machine Learning Blog

APRIL 29, 2024

Trainium is the second-generation machine learning (ML) accelerator that AWS purpose built to help developers access high-performance model training accelerators to help lower training costs by up to 50% over comparable Amazon Elastic Compute Cloud (Amazon EC2) instances. Our cluster consisted of 16 nodes, each equipped with a trn1n.32xlarge

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AWS Machine Learning Blog

APRIL 7, 2023

As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations.

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AWS Machine Learning Blog

MAY 31, 2024

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. With SageMaker Training, a managed batch ML compute service, users can efficiently train models without having to manage the underlying infrastructure.

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Mlearning.ai

SEPTEMBER 16, 2023

Provides performant, standardized inference protocol across ML frameworks ( Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX ) Support modern serverless inference workload with Autoscaling including Scale to Zero on GPU. Let’s start the minikube cluster once our local minikube installation is completed. Extract the istio-1.17.2-win.zip

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. SageMaker-managed clusters of ml.p4d.24xlarge

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AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Hugging Face is a popular open source hub for machine learning (ML) models. You use the same script for downloading the model file when creating the SageMaker endpoint.

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AWS Machine Learning Blog

JUNE 7, 2023

The model weights are available to download, inspect and deploy anywhere. Starting June 7th, both Falcon LLMs will also be available in Amazon SageMaker JumpStart, SageMaker’s machine learning (ML) hub that offers pre-trained models, built-in algorithms, and pre-built solution templates to help you quickly get started with ML.

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. Infrastructure and development challenges Veriff’s backend architecture is based on a microservices pattern, with services running on different Kubernetes clusters hosted on AWS infrastructure.

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Mlearning.ai

FEBRUARY 18, 2023

", ) my_training_data_test = Input( type=AssetTypes.MLTABLE, path=" /test/", description="Dataset for NASA Turbofan Testing", tags={"source_type": "web", "source": "Kaggle ML Repo"}, version="1.0.0", v1", image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",

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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. Training steps To run the training, we use SLURM managed multi-node Amazon Elastic Compute Cloud ( Amazon EC2 ) Trn1 cluster, with each node containing a trn1.32xl instance.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

To upload the dataset Download the dataset : Go to the Shoe Dataset page on Kaggle.com and download the dataset file (350.79MB) that contains the images. With Amazon OpenSearch Serverless, you don’t need to provision, configure, and tune the instance clusters that store and index your data. b64encode(image_file.read()).decode('utf-8')

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Snorkel AI

FEBRUARY 6, 2023

Let’s say when we started, it turns out that downloading data from NASA is work. All you do is you go on a command line and essentially write one line telling the starting and the end date, the coordinates, and it just downloads it on your machine. So we built a simple tool to access any of the satellite imagery. Thanks Google.

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Snorkel AI

FEBRUARY 6, 2023

Let’s say when we started, it turns out that downloading data from NASA is work. All you do is you go on a command line and essentially write one line telling the starting and the end date, the coordinates, and it just downloads it on your machine. So we built a simple tool to access any of the satellite imagery. Thanks Google.

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AWS Machine Learning Blog

JULY 11, 2023

As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. When it comes to ML, this restricts data scientists from downloading any package from public repositories like PyPI , Anaconda , or Conda-Forge.

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Pickl AI

JUNE 7, 2023

The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented. The first part requires you to focus on understanding, analysing and processing datasets; the second part is about designing the model using ML Library. or greater for operations and download.

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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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AWS Machine Learning Blog

JULY 31, 2023

Machine learning (ML) methods can help identify suitable compounds at each stage in the drug discovery process, resulting in more streamlined drug prioritization and testing, saving billions in drug development costs (for more information, refer to AI in biopharma research: A time to focus and scale ). that runs run_alphafold.py

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AWS Machine Learning Blog

JANUARY 24, 2024

Text representation with Embed – Developers can access endpoints that capture the semantic meaning of text, enabling applications such as vector search engines, text classification and clustering, and more. Next, you set up a Weaviate cluster. Subscribe to the Weaviate Kubernetes Cluster on AWS Marketplace.

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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. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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