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

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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Clustering AWS Machine Learning Machine Learning 121

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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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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AI AI Exploratory Data Analysis Clustering 98

Mlearning.ai

AUGUST 28, 2023

Resources include the: Resource group, Azure ML studio, Azure Compute Cluster. Resources include the: Resource group, Azure ML studio, Azure Compute Cluster. Resources include the: Resource group, Azure ML studio, Azure Compute Cluster. The src file contains the .py py scripts to train the model.

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AWS Machine Learning Blog

APRIL 19, 2024

Solution overview For this demo, we use the SageMaker controller to deploy a copy of the Dolly v2 7B model and a copy of the FLAN-T5 XXL model from the Hugging Face Model Hub on a SageMaker real-time endpoint using the new inference capabilities. About the Authors Rajesh Ramchander is a Principal ML Engineer in Professional Services at AWS.

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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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Machine Learning Machine Learning AWS ML 118

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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Iguazio

APRIL 24, 2024

Generative AI is by no means a replacement for the previous wave of AI/ML (now sometimes referred to as ‘traditional AI/ML’), which continues to deliver significant value, and represents a distinct approach with its own advantages. In the end, we explain how MLOps can help accelerate the process and bring these models to production.

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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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SQL ML ML Python 52

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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SQL ML ML Python 52

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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Natural Language Processing AWS Machine Learning Machine Learning 132

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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Machine Learning Machine Learning ML ML 134

Tableau

DECEMBER 1, 2021

I did not realize as Chris demoed his prototype PhD system that it would become Tableau Desktop , a product used today by millions of people around the world to see and understand data, including in Fortune 500 companies, classrooms, and nonprofit organizations. Gestalt properties including clusters are salient on scatters.

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AWS Machine Learning Blog

OCTOBER 23, 2024

The following demo shows Agent Creator in action. At its core, Amazon Bedrock provides the foundational infrastructure for robust performance, security, and scalability for deploying machine learning (ML) models. This integrated architecture not only supports advanced AI functionalities but also makes it easy to use.

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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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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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ML ML Supervised Learning Azure 98

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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Clustering AI AI Azure 59

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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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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Machine Learning Machine Learning AWS ML 94

The MLOps Blog

DECEMBER 29, 2022

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Kyle Morris from Banana about deploying models on GPU. Kyle: Yes.

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ML ML Machine Learning Machine Learning 52

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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Tableau

DECEMBER 1, 2021

I did not realize as Chris demoed his prototype PhD system that it would become Tableau Desktop , a product used today by millions of people around the world to see and understand data, including in Fortune 500 companies, classrooms, and nonprofit organizations. Gestalt properties including clusters are salient on scatters.

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Tableau ML ML Database 98

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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Clustering Python ML ML 52

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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Algorithm Azure Clustering ML 52

AWS Machine Learning Blog

NOVEMBER 1, 2023

The demo implementation code is available in the following GitHub repo. About the authors Alfred Shen is a Senior AI/ML Specialist at AWS. He is a dedicated applied AI/ML researcher, concentrating on CV, NLP, and multimodality. Dr. Changsha Ma is an AI/ML Specialist at AWS.

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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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Deep Learning Deep Learning SQL Natural Language Processing 98

BAIR

FEBRUARY 17, 2024

We frequently see this with LLM users, where a good LLM creates a compelling but frustratingly unreliable first demo, and engineering teams then go on to systematically raise quality. Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together.

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AI AI DataOps Data Pipeline 145

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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Algorithm ML ML Deep Learning 52

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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ML ML Supervised Learning Deep Learning 52

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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ML ML Supervised Learning Deep Learning 52

Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Conclusion To get started today with SnapGPT, request a free trial of SnapLogic or request a demo of the product. Clay Elmore is an AI/ML Specialist Solutions Architect at AWS.

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Database AWS ETL SQL 158

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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Machine Learning Machine Learning Data Scientist ML 69

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 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

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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