AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

AWS 88

AWS ML ML AI 88

Smart Data Collective

FEBRUARY 20, 2022

AWS (Amazon Web Services), the comprehensive and evolving cloud computing platform provided by Amazon, is comprised of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS). With its wide array of tools and convenience, AWS has already become a popular choice for many SaaS companies.

AWS 115

AWS Cloud Computing Data Lakes Database 115

AWS Machine Learning Blog

FEBRUARY 2, 2024

One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). Because embeddings are an important source of data for NLP models in general and generative AI solutions in particular, we need a way to measure whether our embeddings are changing over time (drifting).

AWS 90

AWS Clustering ETL Database 90

Snorkel AI

SEPTEMBER 5, 2023

As Snorkel Flow’s customer base and ML product capabilities have scaled, our cloud costs have unsurprisingly increased. In order to grow efficiently, we set out to understand how we can improve our margins while operating state-of-the-art ML software. the orchestrator for our Ray cluster). Let’s dive in!

ML 72

ML ML Clustering Machine Learning 72

AWS Machine Learning Blog

NOVEMBER 22, 2023

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. In order to achieve this, the M5 team regularly evaluates new techniques to reduce cost.

AWS 93

AWS ML ML Deep Learning 93

AWS Machine Learning Blog

FEBRUARY 13, 2024

Self-checkout process BigBasket introduced an AI-powered checkout system in their physical stores that uses cameras to distinguish items uniquely. The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores.

AWS 99

AWS AI AI ML 99

AWS Machine Learning Blog

AUGUST 8, 2023

You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you can choose Apache Spark or Ray to easily process large datasets, without worrying about cluster management.

AWS 67

AWS Clustering Machine Learning Machine Learning 67

Snorkel AI

SEPTEMBER 5, 2023

As Snorkel Flow’s customer base and ML product capabilities have scaled, our cloud costs have unsurprisingly increased. In order to grow efficiently, we set out to understand how we can improve our margins while operating state-of-the-art ML software. the orchestrator for our Ray cluster). Let’s dive in!

ML 59

ML ML Clustering Machine Learning 59

AWS Machine Learning Blog

MAY 16, 2024

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. When the preprocessing batch was complete, the training/test data needed for training was partitioned based on runtime and stored in Amazon S3.

AWS 85

AWS ML ML Deep Learning 85

AWS Machine Learning Blog

SEPTEMBER 8, 2023

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

AWS 102

AWS Clustering ML ML 102

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.

AWS 85

AWS Data Pipeline ML ML 85

AWS Machine Learning Blog

MARCH 30, 2023

Metadata of the match is processed within the AWS Lambda function MetaDataIngestion , while positional data is ingested using the AWS Fargate container called MatchLink. Additionally, the ball recovery times are sent to a specific topic in the MSK cluster, where they can be accessed by other Bundesliga Match Facts.

AWS 72

AWS Machine Learning Machine Learning Apache Kafka 72

ODSC - Open Data Science

MARCH 27, 2023

Botnets Detection at Scale — Lesson Learned from Clustering Billions of Web Attacks into Botnets. ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

AWS Machine Learning Blog

APRIL 18, 2024

It’s straightforward to deploy in your AWS account. Prerequisites You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. Everything you need is provided as open source in our GitHub repo.

AWS 108

AWS Analytics Analytics AI 108

AWS Machine Learning Blog

APRIL 19, 2023

Business requirements We are the US squad of the Sportradar AI department. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models.

ML 75

ML ML Deep Learning Deep Learning 75

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.

ML 102

ML ML AWS Data Scientist 102

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.

AWS 70

AWS Machine Learning Machine Learning ML 70

AWS Machine Learning Blog

APRIL 29, 2024

To build a production-grade AI system today (for example, to do multilingual sentiment analysis of customer support conversations), what are the primary technical challenges? For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time.

AWS 98

AWS ML ML Python 98

AWS Machine Learning Blog

MAY 25, 2023

One of the most common applications of generative AI and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. Amazon Lex provides the framework for building AI based chatbots. AWS Identity and Access Management roles and policies for access management.

AWS 76

AWS Clustering Python ML 76

AWS Machine Learning Blog

NOVEMBER 3, 2023

To process match metadata, we use an AWS Lambda function called MetaDataIngestion , while positional data is brought in using an AWS Fargate container known as MatchLink. Simultaneously, the shot speed data finds its way to a designated topic within our MSK cluster.

AWS 89

AWS Apache Kafka Data Scientist Algorithm 89

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. The SageMaker extension expects the JupyterLab environment to have valid AWS credentials and permissions to schedule notebook jobs.

AWS 74

AWS Data Scientist ML ML 74

AWS Machine Learning Blog

JULY 3, 2023

PDF Translate – An open-source library written in Java and published on AWS Samples in GitHub. The source and translated PDF documents can also be found in the AWS Samples GitHub repo. Prerequisites Before you get started, set up your AWS account and the AWS Command Line Interface (AWS CLI).

AWS 69

AWS ML ML Clustering 69

AWS Machine Learning Blog

JULY 11, 2023

As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. When it comes to ML, this restricts data scientists from downloading any package from public repositories like PyPI , Anaconda , or Conda-Forge.

AWS 57

AWS Python ML ML 57

AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

ML 109

ML ML AWS Machine Learning 109

AWS Machine Learning Blog

MAY 1, 2024

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart.

AWS 86

AWS ML ML Clustering 86

AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

AWS 105

AWS ML ML Machine Learning 105

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. You can also use an AWS CloudFormation template by following the GitHub instructions to create a domain. aws s3 cp $BUILD_ROOT/model.tar.gz $S3_PATH !bash bin/bash MODEL_NAME=RN50.pt

AWS 86

AWS ML ML K-nearest Neighbors 86

AWS Machine Learning Blog

JANUARY 24, 2024

Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) with these solutions has become increasingly popular. Despite their wealth of general knowledge, state-of-the-art LLMs only have access to the information they were trained on.

AWS 125

AWS Database AI AI 125

AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. It plays a crucial role in every model’s development process and allows data scientists to focus on the most promising ML techniques. The following diagram presents the overall solution workflow.

Algorithm 86

Algorithm AWS ML ML 86

ODSC - Open Data Science

AUGUST 15, 2023

Advancements in data science and AI are coming at a lightning-fast pace. Troubleshooting Search and Retrieval with LLMs Xander Song | Machine Learning Engineer and Developer Advocate | Arize AI Some of the major challenges in deploying LLM applications are the accuracy of results and hallucinations. Check out a few of them below.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. With containers, scaling on a cluster becomes much easier. With containers, scaling on a cluster becomes much easier. Run the ML task on Amazon ECS.

AWS 77

AWS Machine Learning Machine Learning Clustering 77

Mlearning.ai

MAY 23, 2023

Check out this course to upskill on Apache Spark —  [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus. Check this course to upskill on AWS —  [link] Domain Knowledge Having expertise in a specific industry domain, such as finance, healthcare, or marketing, can be advantageous. in these fields.

Data Science 52

Data Science Data Scientist Apache Hadoop Machine Learning 52

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. Companies are finding NLP to be one of the best applications of AI regardless of industry.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

AWS Machine Learning Blog

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. The data scientist is responsible for moving the code into SageMaker, either manually or by cloning it from a code repository such as AWS CodeCommit.

AWS 122

AWS Machine Learning Machine Learning Data Scientist 122

AWS Machine Learning Blog

AUGUST 14, 2023

Amazon SageMaker JumpStart is a machine learning (ML) hub offering algorithms, models, and ML solutions. Topic: Topic: food Chatbot and conversational AI This is a discussion between a [human] and a [robot]. Co-founder and CEO of Platform.sh, he previously co-founded Commerce Guys, a leading Drupal ecommerce provider.

AWS 71

AWS Natural Language Processing Machine Learning Machine Learning 71

Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It provides a large cluster of clusters on a single machine. AWS SageMaker also has a CLI for model creation and management.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

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. Customers such as Stability AI use SageMaker HyperPod to train their foundation models, including Stable Diffusion. “As

Clustering 110

Clustering AWS Machine Learning Machine Learning 110