AWS Machine Learning Blog

NOVEMBER 30, 2023

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

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ODSC - Open Data Science

MAY 24, 2023

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

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Data Science Dojo

JULY 17, 2023

Scikit-learn can be used for a variety of data analysis tasks, including: Classification Regression Clustering Dimensionality reduction Feature selection Leveraging Scikit-learn in data analysis projects Scikit-learn can be used in a variety of data analysis projects. It is open-source, so it is free to use and modify.

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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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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. How do I develop my body of work?

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Pickl AI

APRIL 5, 2023

ML forms the underlying platform for several new developments. Hence, it has also triggered the demand for ML experts. However, if you are new to the tech domain and want to learn Machine Learning for free, then in this blog, we will take you through the 3 best options to start your ML learning journey. Lakhs to ₹ 28.4

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DataRobot

DECEMBER 16, 2021

Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Simply fire up DataRobot’s unsupervised mode and use clustering or anomaly detection to help you discover patterns and insights with your data. Request a Demo. Do More with Text AI.

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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. Choose Manage.

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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

MAY 23, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are rapidly adopting and using ML technologies to transform their businesses.

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DataRobot Blog

OCTOBER 4, 2022

This has prompted AI/ML model owners to retrain their legacy models using data from the post-COVID era, while adapting to continually fluctuating market trends and thinking creatively about forecasting. Time Series Clustering takes it a step further, allowing you to automatically detect new ways to segment your series. The Dataset.

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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.

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Mlearning.ai

JULY 9, 2023

I realized that the algorithm assumes that we like a particular genre and artist and groups us into these clusters, not letting us discover and experience new music. You can check a live demo of the app using the link below: Spotify Reccomendation BECOME a WRITER at MLearning.ai // invisible ML // 800+ AI tools Mlearning.ai

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Snorkel AI

MARCH 30, 2023

Rapid, model-guided iteration with New Studio for all core ML tasks. Enhanced studio experience for all core ML tasks. If you want to see Snorkel Flow in action, sign up for a demo. Enhanced new studio experience Snorkel Flow now supports all ML tasks through a single interface via our new Snorkel Flow Studio experience.

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AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. For demo purposes, we use approximately 1,600 products. We use the first metadata file in this demo. We use a pretrained ResNet-50 (RN50) model in this demo.

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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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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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Snorkel AI

FEBRUARY 6, 2023

But then, well, I’m presenting here, so I probably will have a demo ready, right, to show you. It just happened that when the system started clustering the images, it started to make some sort of a sense. The post NASA ML Lead on its WorldView citizen scientist no-code tool appeared first on Snorkel AI.

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Snorkel AI

FEBRUARY 6, 2023

But then, well, I’m presenting here, so I probably will have a demo ready, right, to show you. It just happened that when the system started clustering the images, it started to make some sort of a sense. The post NASA ML Lead on its WorldView citizen scientist no-code tool appeared first on Snorkel AI.

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DataRobot Blog

JULY 27, 2022

Investing in AI/ML is no longer an option but is critical for organizations to remain competitive. The Demo: Autoscaling with MLOps. Operationalize ML Faster with MLOps Automation. In this demo, we are completely unattended. Admin keys are not required for this demo.

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ODSC - Open Data Science

JULY 22, 2023

On both days, we had our AI Expo & Demo Hall where over a dozen of our partners set up to showcase their latest developments, tools, frameworks, and other offerings. You can read the recap here and watch the full keynote here. Expo Hall ODSC events are more than just data science training and networking events.

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AWS Machine Learning Blog

AUGUST 24, 2023

The need for profiling training jobs With the rise of deep learning (DL), machine learning (ML) has become compute and data intensive, typically requiring multi-node, multi-GPU clusters. ML practitioners have to cope with common challenges of efficient resource utilization when training such large models.

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phData

JUNE 28, 2023

Access to GPUs within Snowflake will allow organizations to build and harness AI, machine learning (ML), and large language models (LLM) right within Snowflake. Today’s most cutting-edge Generative AI and LLM applications are all trained using large clusters of GPU-accelerated hardware.

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The MLOps Blog

AUGUST 15, 2024

We’re working with super-large GPU clusters and are looking at training runs that take weeks or months. Neptune is known for its user-friendly UI and seamlessly integrates with popular ML/AI frameworks, enabling quick adoption with minimal disruption. Pretraining is undoubtedly the most expensive activity.

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ODSC - Open Data Science

APRIL 19, 2023

Unifying ML With One Line of Code Can there be unity in machine learning frameworks? Ivy, a tool that can be used to unify different machine learning frameworks by transpiling ML code to run in any other ML framework with the addition of a single function decorator. Well in this session, Ivy will prove that it is possible.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

Generative AI is a modern form of machine learning (ML) that has recently shown significant gains in reasoning, content comprehension, and human interaction. Under Connect Amazon Q to IAM Identity Center , choose Create account instance to create a custom credential set for this demo. We examine some of these use cases in future posts.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

Amazon Transcribe is a machine learning (ML) based managed service that automatically converts speech to text, enabling developers to seamlessly integrate speech-to-text capabilities into their applications. This is where AI and machine learning (ML) come into play, offering a future-ready approach to revolutionize IT operations.

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Towards AI

JULY 21, 2023

They fine-tuned BERT, RoBERTa, DistilBERT, ALBERT, XLNet models on siamese/triplet network structure to be used in several tasks: semantic textual similarity, clustering, and semantic search. I tend to view LIT as an ML demo on steroids for prototyping. Broadcaster Stream API Fast.ai Comes with a UI out of the box.

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Mlearning.ai

MAY 23, 2023

How to find the most dominant colors in an image using KMeans clustering In this blog, we will find the most dominant colors in an image using the K-means clustering algorithm , this is a very interesting project and personally one of my favorites because of its simplicity and power. Check out the demo here… [link] 21.

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The MLOps Blog

NOVEMBER 14, 2023

This is where visualizations in ML come in. Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Which one is right for you depends on your goal.

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Mlearning.ai

MAY 21, 2023

How to find the most dominant colors in an image using KMeans clustering In this blog, we will find the most dominant colors in an image using the K-means clustering algorithm , this is a very interesting project and personally one of my favorites because of its simplicity and power. Check out the demo here… [link] 21.

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AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Amazon SageMaker JumpStart provides one-click, end-to-end solutions for many common ML use cases. Demo notebook.

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Google Research AI blog

JULY 23, 2023

Posted by Cat Armato, Program Manager, Google Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more.

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AWS Machine Learning Blog

MAY 30, 2024

Input context length for each table’s schema for demo is between 2,000–4,000 tokens. OpenSearch Service currently has tens of thousands of active customers with hundreds of thousands of clusters under management, processing hundreds of trillions of requests per month. Principal Enterprise Architect at CBRE Chakra Nagarajan is a Sr.

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Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. If I could try to read it back to you quickly: take a canonical ML problem. AR: That makes a ton of sense.

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Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. If I could try to read it back to you quickly: take a canonical ML problem. AR: That makes a ton of sense.

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Mlearning.ai

JANUARY 31, 2023

It turned out that a better solution was to annotate data by using a clustering algorithm, in particular, I chose the popular K-means. So I simply run the K-means on the whole dataset, partitioning it into 4 different clusters. The label of a cluster was set as a label for every one of its samples. in both metrics.

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