Smart Data Collective

AUGUST 29, 2022

This is why businesses are looking to leverage machine learning (ML). In this article, we will share some best practices for improving your analytics with ML. Top ML approaches to improve your analytics. Clustering. ?lustering They need a more comprehensive analytics strategy to achieve these business goals.

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AWS Machine Learning Blog

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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Towards AI

SEPTEMBER 3, 2024

Let’s discuss two popular ML algorithms, KNNs and K-Means. We will discuss KNNs, also known as K-Nearest Neighbours and K-Means Clustering. They are both ML Algorithms, and we’ll explore them more in detail in a bit. They are both ML Algorithms, and we’ll explore them more in detail in a bit.

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AWS Machine Learning Blog

APRIL 17, 2024

Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. In such distributed environments, observability of both instances and ML chips becomes key to model performance fine-tuning and cost optimization.

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AWS Machine Learning Blog

JULY 25, 2024

By accelerating the speed of issue detection and remediation, it increases the reliability of your ML training and reduces the wasted time and cost due to hardware failure. Choose Clusters in the navigation pane, open the trainium-inferentia cluster, choose Node groups, and locate your node group. # install.sh

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We use the Time Series Clustering using TSFresh + KMeans notebook, which is available on our GitHub repo.

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Data Science Dojo

MARCH 8, 2024

IVF or Inverted File Index divides the vector space into clusters and creates an inverted file for each cluster. A file records vectors that belong to each cluster. It enables comparison and detailed data search within clusters. Both methods aim to enhance the similarity search in vector databases.

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DataRobot Blog

DECEMBER 6, 2022

With a goal to help data science teams learn about the application of AI and ML, DataRobot shares helpful, educational blogs based on work with the world’s most strategic companies. Time Series Clustering empowers you to automatically detect new ways to segment your series as economic conditions change quickly around the world.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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Towards AI

FEBRUARY 21, 2024

Image generated with DALL-E 3 In the fast-paced world of Machine Learning (ML) research, keeping up with the latest findings is crucial and exciting, but let’s be honest — it’s also a challenge. Enter ML Conference Paper Explorer: your sidekick in navigating the ML paper maze with ease. What’s the next big thing in ML?

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Dataconomy

MAY 22, 2023

TensorFlow provides high-level APIs, such as tf.distribute, to distribute training across multiple devices, machines, or clusters. It offers a comprehensive ecosystem that supports distributed training and inference, allowing developers to scale their machine learning workflows seamlessly.

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AWS Machine Learning Blog

JUNE 25, 2024

This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

At the time, I knew little about AI or machine learning (ML). But AWS DeepRacer instantly captured my interest with its promise that even inexperienced developers could get involved in AI and ML. Panic set in as we realized we would be competing on stage in front of thousands of people while knowing little about ML.

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Google Research AI blog

MAY 25, 2023

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. When clustering is applied to personal data (e.g.,

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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Hacker News

JANUARY 25, 2024

He writes about ML/AI/crypto/data, leadership, and building tech teams. Adam Drake is an advisor to scale-up tech companies.

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Smart Data Collective

NOVEMBER 1, 2020

Unsupervised ML: The Basics. Unlike supervised ML, we do not manage the unsupervised model. Unsupervised ML uses algorithms that draw conclusions on unlabeled datasets. As a result, unsupervised ML algorithms are more elaborate than supervised ones, since we have little to no information or the predicted outcomes.

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Data Science Dojo

DECEMBER 28, 2023

That world is not science fiction—it’s the reality of machine learning (ML). In this blog post, we’ll break down the end-to-end ML process in business, guiding you through each stage with examples and insights that make it easy to grasp. Formatting the data in a way that ML algorithms can understand.

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ODSC - Open Data Science

FEBRUARY 23, 2023

Architectures for Running ML at the Edge Tue, Feb 28, 2023, 12:00 PM — 1:00 PM EST In this webinar, we will explore different paradigms for edge deployment of ML models, including federated learning, cloud-edge hybrid architectures, and standalone edge models.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It provides a large cluster of clusters on a single machine. It also has ML algorithms built into the platform.

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Hacker News

MARCH 12, 2024

Marking a major investment in Meta’s AI future, we are announcing two 24k GPU clusters. We use this cluster design for Llama 3 training. We built these clusters on top of Grand Teton , OpenRack , and PyTorch and continue to push open innovation across the industry. The other cluster features an NVIDIA Quantum2 InfiniBand fabric.

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Towards AI

JULY 17, 2023

Pyspark MLlib | Classification using Pyspark ML In the previous sections, we discussed about RDD, Dataframes, and Pyspark concepts. In this article, we will discuss about Pyspark MLlib and Spark ML. using PySpark we can run applications parallelly on the distributed cluster… blog.devgenius.io

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster.

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ML @ CMU

NOVEMBER 7, 2024

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. RELand consistently outperforms the benchmark models on all relevant metrics.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. For this post we’ll use a provisioned Amazon Redshift cluster.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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AWS Machine Learning Blog

APRIL 1, 2024

Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. Distributed model training requires a cluster of worker nodes that can scale. The following figure shows how FSDP works for two data parallel processes.

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Towards AI

MAY 3, 2023

This code can cover a diverse array of tasks, such as creating a KMeans cluster, in which users input their data and ask ChatGPT to generate the relevant code. This is where ML CoPilot enters the scene. In this paper, the authors suggest the use of LLMs to make use of past ML experiences to suggest solutions for new ML tasks.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. There are a few different ways in which authentication across AWS accounts can be achieved when data in the SaaS platform is accessed from SageMaker and when the ML model is invoked from the SaaS platform.

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Dataconomy

MAY 16, 2023

Machine learning techniques: Familiarity with a wide range of machine learning algorithms and techniques allows data scientists to apply appropriate models for predictive analysis, clustering, classification, and recommendation systems.

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AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. kubectl for working with Kubernetes clusters.

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AWS Machine Learning Blog

APRIL 19, 2024

The architecture deploys a simple service in a Kubernetes pod within an EKS cluster. Karpenter monitors for any pending pods that can’t run due to lack of sufficient resources in the cluster. If such pods are detected, Karpenter adds more nodes to the cluster to provide the necessary resources. A managed node group with two c5.xlarge

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

This allows SageMaker Studio users to perform petabyte-scale interactive data preparation, exploration, and machine learning (ML) directly within their familiar Studio notebooks, without the need to manage the underlying compute infrastructure. This same interface is also used for provisioning EMR clusters.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Solution overview The steps to implement the solution are as follows: Create the EKS cluster. Create the EKS cluster If you don’t have an existing EKS cluster, you can create one using eksctl. Adjust the following configuration to suit your needs, such as the Amazon EKS version, cluster name, and AWS Region.

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Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster.

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