Towards AI

JUNE 27, 2023

For instance, today’s machine learning tools are pushing the boundaries of natural language processing, allowing AI to comprehend complex patterns and languages. These tools are becoming increasingly sophisticated, enabling the development of advanced applications.

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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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Towards AI

MAY 2, 2023

Use plain English to build ML models to identify profitable customer segments. In this post, we explore the concept of querying data using natural language, eliminating the need for SQL queries or coding skills. Last Updated on May 9, 2023 by Editorial Team Author(s): Sriram Parthasarathy Originally published on Towards AI.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. This same interface is also used for provisioning EMR clusters.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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Data Science Dojo

JULY 17, 2023

It is used for machine learning, natural language processing, and computer vision tasks. TensorFlow First on the AI tool list, we have TensorFlow which is an open-source software library for numerical computation using data flow graphs. It is a cloud-based platform, so it can be accessed from anywhere.

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AWS Machine Learning Blog

JUNE 11, 2024

In these cases, the model sizes are smaller, which means the communication overhead with GPUs or ML accelerator instances outweighs their compute performance benefits. As early adopters of Graviton for ML workloads, it was initially challenging to identify the right software versions and the runtime tunings.

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Towards AI

SEPTEMBER 5, 2024

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. This repo is designed for educational exploration.

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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. It can also be used for determining the optimal number of clusters.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. It removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs).

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. Their impact on ML tasks has made them a cornerstone of AI advancements. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings.

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Hacker News

AUGUST 5, 2024

When Meta introduced distributed GPU-based training , we decided to construct specialized data center networks tailored for these GPU clusters. We have successfully expanded our RoCE networks, evolving from prototypes to the deployment of numerous clusters, each accommodating thousands of GPUs.

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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

MAY 1, 2024

Using the Neuron Distributed library with SageMaker SageMaker is a fully managed service that provides developers, data scientists, and practitioners the ability to build, train, and deploy machine learning (ML) models at scale. Cluster update is currently enabled for the TRN1 instance family as well as P and G GPU-based instance types.

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Mlearning.ai

APRIL 10, 2023

How this machine learning model has become a sustainable and reliable solution for edge devices in an industrial network An Introduction Clustering (cluster analysis - CA) and classification are two important tasks that occur in our daily lives. 3 feature visual representation of a K-means Algorithm.

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IBM Journey to AI blog

NOVEMBER 13, 2023

Nodes run the pods and are usually grouped in a Kubernetes cluster, abstracting the underlying physical hardware resources. Kubernetes’s declarative, API -driven infrastructure has helped free up DevOps and other teams from manually driven processes so they can work more independently and efficiently to achieve their goals.

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Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. Then we use K-Means to identify a set of cluster centers. A visual representation of the silhouette score can be seen in the following figure.

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AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. About the Authors Melanie Li is a Senior AI/ML Specialist TAM at AWS based in Sydney, Australia.

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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AWS Machine Learning Blog

DECEMBER 29, 2022

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Note: If you already have an RStudio domain and Amazon Redshift cluster you can skip this step. 1 Public subnet.

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. SageMaker-managed clusters of ml.p4d.24xlarge

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AWS Machine Learning Blog

OCTOBER 18, 2024

Business challenge Today, many developers use AI and machine learning (ML) models to tackle a variety of business cases, from smart identification and natural language processing (NLP) to AI assistants. Because you pay only for what you use, you can manage your training costs more effectively.

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AWS Machine Learning Blog

MARCH 4, 2024

Alida’s customers receive tens of thousands of engaged responses for a single survey, therefore the Alida team opted to leverage machine learning (ML) to serve their customers at scale. The new service achieved a 4-6 times improvement in topic assertion by tightly clustering on several dozen key topics vs. hundreds of noisy NLP keywords.

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AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process.

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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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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.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job.

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AWS Machine Learning Blog

APRIL 17, 2023

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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AWS Machine Learning Blog

JANUARY 24, 2024

Text representation with Embed – Developers can access endpoints that capture the semantic meaning of text, enabling applications such as vector search engines, text classification and clustering, and more. Cohere Embed comes in two forms, an English language model and a multilingual model, both of which are now available on Amazon Bedrock.

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AWS Machine Learning Blog

JANUARY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. They can process various types of input data, including tabular, image, and text.

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AWS Machine Learning Blog

MARCH 2, 2023

In our solution, we implement a hyperparameter grid search on an EKS cluster for tuning a bert-base-cased model for classifying positive or negative sentiment for stock market data headlines. A desired cluster can simply be configured using the eks.conf file and launched by running the eks-create.sh to launch the cluster.

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AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process.

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AWS Machine Learning Blog

JUNE 21, 2024

His background includes pivotal roles in developing ML-driven cybersecurity and drug discovery platforms. He has deep ML experience in speech recognition, translation, natural language processing, and advertising, and has published over 30 papers in these areas.

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AWS Machine Learning Blog

JANUARY 12, 2024

In contrast, text embeddings use machine learning (ML) capabilities to capture the meaning of unstructured data. Embeddings are generated by representational language models that translate text into numerical vectors and encode contextual information in a document.

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