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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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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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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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

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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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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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

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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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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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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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody. Everybody can train a model.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody. Everybody can train a model.

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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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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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AWS Machine Learning Blog

AUGUST 14, 2023

Amazon SageMaker JumpStart is a machine learning (ML) hub offering algorithms, models, and ML solutions. The code for all the steps in this demo is available in the following notebook. This page lists available end-to-end ML solutions, pre-trained models, and example notebooks. Drain the pasta.']

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AWS Machine Learning Blog

APRIL 18, 2024

Check out the following demo to see how it works. For example, this could be a softphone (such as Google Voice ), another meeting app, or for demo purposes, you can simply play a local audio recording or a YouTube video in your browser to emulate another meeting participant. He is passionate about AI/ML.

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AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. In this demo, we use a Jumpstart Flan T5 XXL model endpoint. SageMaker Savings Plans apply only to SageMaker ML Instance usage. Rachna Chadha is a Principal Solution Architect AI/ML in Strategic Accounts at AWS.

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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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Snorkel AI

AUGUST 9, 2023

However, as enterprises begin to look beyond proof-of-concept demos and toward deploying LLM-powered applications on business-critical use cases, they’re learning that these models (often appropriately called “ foundation models ”) are truly foundations, rather than the entire house.

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Snorkel AI

AUGUST 9, 2023

However, as enterprises begin to look beyond proof-of-concept demos and toward deploying LLM-powered applications on business-critical use cases, they’re learning that these models (often appropriately called “ foundation models ”) are truly foundations, rather than the entire house.

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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

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

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

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

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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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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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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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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AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). This technique is achieved through the use of ML algorithms that enable the understanding of the meaning and context of data (semantic relationships) and the learning of complex relationships and patterns within the data (syntactic relationships).

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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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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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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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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.

Machine Learning 52

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. If all goes well, of course ?

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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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