AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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Heartbeat

AUGUST 14, 2023

Region-based segmentation can be performed using clustering algorithms, such as k-means clustering or mean-shift clustering, which group similar pixels into clusters. The resulting clusters can then be used to segment the image into different regions. We pay our contributors, and we don’t sell ads.

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Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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ODSC - Open Data Science

JULY 22, 2023

Expo Hall ODSC events are more than just data science training and networking events. Thank you to everyone who attended for making this event possible, and showing once again why we do what we do — connecting the greater data science community together to push the industry forward. What’s next?

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ODSC - Open Data Science

JULY 14, 2023

Probabilistic Machine Learning for Finance and Investing Deepak Kanungo | Founder and CEO, Advisory Board Member | Hedged Capital LLC, AIKON This session will introduce you to the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

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AWS Machine Learning Blog

NOVEMBER 3, 2023

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

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Women in Big Data

DECEMBER 13, 2023

The event was part of the chapter’s technical talk series 2023. Benefits of NLP ? NLP has many applications – Machine Translation, Text Summarization, Searching, Question Answering, Named-Entity Recognition, Parts-of-Speech: (POS), Clustering, Sentiment Analysis, Text Classification, Chatbots and Virtual Assistants.

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AWS Machine Learning Blog

APRIL 18, 2024

A Lambda function called the Call Event Processor, fed by Kinesis Data Streams, processes and optionally enriches meeting metadata and transcription segments. The Call Event Processor integrates with the meeting assist services. The stacks take about 35–40 minutes to deploy. Authentication is provided by Amazon Cognito.

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Pickl AI

SEPTEMBER 3, 2023

On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour.

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Pickl AI

MAY 10, 2023

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. What is Classification?

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ODSC - Open Data Science

OCTOBER 13, 2023

After that, he worked as a quant at a hedge fund on a 600 GPU cluster. Interested in attending an ODSC event? Learn more about our upcoming events here. After studying chemical engineering Jepson worked for Intel/Micron in applied semiconductor building process control and fault models.

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Heartbeat

SEPTEMBER 28, 2023

In this article, we take a deep dive into a machine learning project aimed at predicting customer churn and explore how Comet ML, a powerful machine learning experiment tracking platform, plays a key role in increasing project success. ?I Our project uses Comet ML to: 1. The entire code can be found on both GitHub and Kaggle.

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Towards AI

JANUARY 24, 2023

After the success of our new livestream Events format in our 36,000-member Learn AI Together Discord community, we are very excited to announce our new open platform for AI video and audio content contributors. While the monetization of ChatGPT was inevitable, it could be argued that it is too early to ask users to pay for it. It […]

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Explosion

NOVEMBER 28, 2023

We’re also currently testing in beta, Prodigy Teams , our hybrid cloud SaaS product that works on top of Prodigy with enhanced features for collaboration while preserving scriptability and data privacy by keeping your data and models hosted on your private cluster. With Prodigy v1.14.1

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ODSC - Open Data Science

NOVEMBER 20, 2023

Bridging the gap between accuracy and simplicity, this hybrid method offers the precise identification of customer groups created by machine learning clustering methods and the simple business profiles yielded by rules-based segmentation methods. Interested in attending an ODSC event? Learn more about our upcoming events here.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. Best practices for ml model packaging Here is how you can package a model efficiently.

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AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface.

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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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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. Three significant events affected the evolution of these models. 2018) “ Language models are few-shot learners ” by Brown et al.

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AWS Machine Learning Blog

APRIL 5, 2024

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Geobox enables city departments to do the following: Improved climate adaptation planning – Informed decisions reduce the impact of extreme heat events.

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AWS Machine Learning Blog

JANUARY 17, 2024

It can represent a geographical area as a whole or it can represent an event associated with a geographical area. We then discuss the various use cases and explore how you can use AWS services to clean the data, how machine learning (ML) can aid in this effort, and how you can make ethical use of the data in generating visuals and insights.

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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. Operationalize ML Faster with MLOps Automation. This may be a different path depending on your Algorithmia Cluster environment, if you’re having difficulties reach out to the DataRobot and Algorithmia team. But have no fear!

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AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. Durga Sury is an ML Solutions Architect on the Amazon SageMaker Service SA team. She is passionate about making machine learning accessible to everyone.

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Snorkel AI

MAY 3, 2023

Table of contents Why we needed to redesign our interactive ML system In this section, we’ll go over the market forces and technological shifts that compelled us to re-architect our ML system. Customers tackle high cardinality and multi-label ML problems, requiring far more training data to cover rare classes.

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Mlearning.ai

FEBRUARY 16, 2023

With the help of Snowflake clusters, organizations can effectively deal with both rush times and slowdowns since they ensure scalability upon demand. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

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Heartbeat

OCTOBER 25, 2023

Unsupervised learning techniques, such as clustering and dimensionality reduction, aid in identifying patterns and structures within datasets. Clustering algorithms can group similar biological samples or identify distinct subtypes within a disease. We pay our contributors, and we don’t sell ads.

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Heartbeat

JANUARY 5, 2024

Document Retrieval and Clustering: LangChain can simplify retrieval and clustering using embedding models. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Google Research AI blog

APRIL 30, 2023

Posted by Catherine Armato, Program Manager, Google The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels.

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Mlearning.ai

JULY 25, 2023

Note that the length is set to one, thus the t and z-statistic will be # computed per row. OUTPUT_TABLE_name --allow_large_results --use_legacy_sql=false 'SELECT CENTROID_ID, AVG(mean_steps_norm) AS steps, AVG(mean_total_distance_norm) AS distance, AVG(mean_calories_norm) AS calories, AVG(mean_hr_norm) AS hr FROM `northern-eon-377721.google_analytics_exercise.kmeans_predict_exercise_type`

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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. The Kubernetes Event Driven Autoscaler ( KEDA ) is configured to automatically scale the number of service pods, based on the custom metrics available in Prometheus. xlarge nodes is included to run system pods that are needed by the cluster.

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Dataconomy

MAY 25, 2023

This enables them to respond quickly to changing conditions or events. ML algorithms for analyzing IoT data using artificial intelligence Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances.

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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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Mlearning.ai

JUNE 9, 2023

LLMs and the Emerging ML Tech Stack Machine learning tech stack with Large language model Similar arch mentioned in this article However, how does this architecture solve above challenges? It proposes embedding and LLM programming framework before LLM endpoint.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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AWS Machine Learning Blog

DECEMBER 18, 2023

Amazon SageMaker Canvas Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. Through Atlas Data Federation, data is extracted into Amazon S3 bucket.

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