Algorithm, AWS, Clustering and Computer Science

Boost your forecast accuracy with time series clustering

AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. In this post, we seek to separate a time series dataset into individual clusters that exhibit a higher degree of similarity between its data points and reduce noise.

Clustering

Clustering ML ML AWS

Data Science Career FAQs Answered: Educational Background

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn — [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow. in these fields.

Data Science

Data Science Data Scientist Apache Hadoop Machine Learning

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

MARCH 15, 2024

To mitigate these risks, the FL model uses personalized training algorithms and effective masking and parameterization before sharing information with the training coordinator. Therefore, ML creates challenges for AWS customers who need to ensure privacy and security across distributed entities without compromising patient outcomes.

AWS

AWS ML ML Machine Learning

Webinars

The Project Clinic: Assessing Project Health, Planning, and Execution

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

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Top NLP Skills, Frameworks, Platforms, and Languages for 2023

ODSC - Open Data Science

FEBRUARY 17, 2023

Data Science Fundamentals Going beyond knowing machine learning as a core skill, knowing programming and computer science basics will show that you have a solid foundation in the field. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

Deep Learning

Deep Learning Deep Learning Data Science Natural Language Processing

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

JULY 25, 2023

With expertise in Python, machine learning algorithms, and cloud platforms, machine learning engineers optimize models for efficiency, scalability, and maintenance. They possess a deep understanding of statistical methods, programming languages, and machine learning algorithms. Cloud Platforms: AWS, Azure, Google Cloud, etc.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

Creating an artificial intelligence 101

Dataconomy

MARCH 13, 2023

Algorithms: AI algorithms are used to process the data and extract insights from it. There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Develop AI models using machine learning or deep learning algorithms.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm

Azure service cloud summarized: Part I

Mlearning.ai

APRIL 24, 2023

One can only train and mange so many algorithms/commands with one computer, thus it is attractive to use a service cloud platform with more computers, storage, and deployment options. In this post I list six important modules that I think could help in performing Data Science tasks quickly.

Azure

Azure SQL Database Python

Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

AUGUST 22, 2023

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. Flower has an extensive implementation of FL averaging algorithms and a robust communication stack. Each account or Region has its own training instances.

Machine Learning

Machine Learning Machine Learning AWS ML

Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. In the first post , we described FL concepts and the FedML framework.

AWS

AWS Analytics Analytics Machine Learning

How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker

AWS Machine Learning Blog

JANUARY 20, 2023

In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. Step-by-step solution Step 1 A client makes a request to the AWS API Gateway endpoint.

AWS

AWS AI AI Computer Science

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

FEBRUARY 10, 2023

As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. probability and Cover 1 Man with 31.3%

ML

ML ML Machine Learning Machine Learning

Dialogue-guided intelligent document processing with foundation models on Amazon SageMaker JumpStart

AWS Machine Learning Blog

MAY 24, 2023

You can efficiently deploy the pre-trained J2-jumbo-instruct, or other Jurassic-2 models available on AWS Marketplace, into your own own virtual private cloud (VPC) using Amazon SageMaker. 24xlarge") # Create a Sgaemkaer endpoint then deploy a pre-trained J2-jumbo-instruct-v1 model from AWS Market Place.

AI

AI AI AWS ML

Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

APRIL 18, 2023

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

ML

ML ML Deep Learning Deep Learning

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

APRIL 18, 2023

To make things easy, these three inputs depend solely on the model name, version (for a list of the available models, see Built-in Algorithms with pre-trained Model Table ), and the type of instance you want to train on. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

ML

ML ML Deep Learning Deep Learning

How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

Database

Database AWS ETL SQL

Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

NOVEMBER 1, 2023

Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). About the authors Alfred Shen is a Senior AI/ML Specialist at AWS. With SageMaker, you can seamlessly deploy IDEFICS-9b-instruct on a g5.2xlarge instance for inference tasks.

AWS

AWS Clustering Deep Learning Deep Learning

Data Science Current

Boost your forecast accuracy with time series clustering