AWS Machine Learning Blog

MAY 16, 2024

elasticmapreduce/libs/script-runner/script-runner.jar,Args=["s3://bucket/prefix/data_prep_batch.sh"] --auto-terminate MLOps component 2: Automated training and deployment of models In this section, we discuss the components of the model training and deployment pipeline. We load tested it with Locust using five g4dn.2xlarge

Deep Learning 98

Deep Learning Auto-complete Algorithm ML 98

Unite.AI

DECEMBER 19, 2023

We subscribe to more than 50 feeds which we download files from to train our model. Generally, these customers are also adopting a “shift left” with DevOps. What makes our model unique is it does not need data or files from customers to learn and grow.

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Neural Network Deep Learning ML Metadata 147

AWS Machine Learning Blog

NOVEMBER 9, 2023

Automated retraining mechanism – The training pipeline built with SageMaker Pipelines is triggered whenever a data drift is detected in the inference pipeline. Download and save the publicly available UCI Mammography Mass dataset to the S3 bucket you created earlier in the dev account. data/ mammo-train-dataset-part2.csv csv dataset.

Data Drift 104

Data Drift Auto-complete ML Automation 104

Heartbeat

JULY 31, 2023

Comet’s data management feature allows users to manage their training data, including downloading, storing, and preprocessing data. Comet provides a built-in hyperparameter optimization feature that automates the process of hyperparameter tuning. Using Comet saves time and reduces the risk of human error.

LLM 52

LLM Machine Learning Deep Learning Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 29, 2024

Major benefits include high scalability of the service, in large part due to automation and a self-service model, and an attractive pricing model that’s primarily based on resource consumption. Download the source code from the GitHub repo. The password should be at least 8 characters, with uppercase characters and numbers.

Data Scientist 103

Data Scientist DevOps AI AI 103

Explosion

AUGUST 3, 2017

That’s why we’re pleased to introduce Prodigy , a downloadable tool for radically efficient machine teaching. Most of those insights have been used to make spaCy better: AI DevOps was hard, so we made sure models could be installed via pip. You can also download the annotated data set we’ve created with Prodigy for this example.

Convolutional Neural Networks 52

Convolutional Neural Networks Python Machine Learning Algorithm 52

Unite.AI

JUNE 23, 2023

I noticed that IoT was creating an immense amount of data, but networks were not optimized for uploading it, just downloading it. This convergence unlocks the potential for innovative applications and services across IoT, autonomous systems, smart cities, industrial automation and more. at the edge.

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DevOps Automation AI AI 147

AWS Machine Learning Blog

MARCH 1, 2023

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. An Amazon Comprehend flywheel automates this ML process, from data ingestion to deploying the model in production. Choose Create job.

Data Ingestion 89

Data Ingestion DevOps Automation ML 89

AWS Machine Learning Blog

OCTOBER 9, 2023

Download the sample product reviews datasets: sample_product_reviews.csv – Contains 2,000 synthesized product reviews and is used for sentiment analysis and Text Analysis predictions. Click Download CSV to download the results to your computer. Prerequisites Complete the following prerequisites: Have an AWS account.

Machine Learning 82

Machine Learning Categorization ML DevOps 82

AWS Machine Learning Blog

NOVEMBER 14, 2023

Many of these foundation models have shown remarkable capability in understanding and generating human-like text, making them a valuable tool for a variety of applications, from content creation to customer support automation. In the following code example, download and repack the latest version of the base model first. !aws

LLM 94

LLM ML Natural Language Processing Machine Learning 94

DataRobot Blog

FEBRUARY 11, 2022

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. Download Now.

Data Drift 52

Data Drift DevOps Machine Learning Data Scientist 52

AWS Machine Learning Blog

OCTOBER 16, 2023

They provide advanced technology that combines AI-powered automation with human feedback, deep insights, and expertise. Furthermore, DevOps were burdened with manually provisioning GPU instances in response to demand patterns. Download the model weights. This monorepo is integrated with a continuous integration (CI) tool.

Data Scientist 88

Data Scientist Computer Vision DevOps ML 88

Flipboard

MARCH 7, 2023

You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images. Implement an automated image authoring process As already mentioned, you can use the Studio Image Build CLI to implement an automated CI/CD process of app image creation and deployment with CodeBuild and sm-docker CLI.

Python 93

Python Automation Data Scientist ML 93

AWS Machine Learning Blog

SEPTEMBER 11, 2023

IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible while following best practices in the area of development operations (DevOps). You can use Lifecycle Configurations to automate customization for your Studio environment.

Auto-complete 99

Auto-complete ML Machine Learning DevOps 99

AWS Machine Learning Blog

JUNE 14, 2023

SageMaker provides a set of templates for organizations that want to quickly get started with ML workflows and DevOps continuous integration and continuous delivery (CI/CD) pipelines. SageMaker Projects helps organizations set up and standardize environments for automating different steps involved in an ML lifecycle.

ML 82

ML Automation Data Scientist DevOps 82

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JANUARY 11, 2023

In this prototype, we follow a fully automated provisioning methodology in accordance with IaC best practices. IaC is the process of provisioning resources programmatically using automated scripts rather than using interactive configuration tools. HashiCorp provides Terraform with a binary installer, which you can download and install.

ML 70

ML Python Automation Machine Learning 70

The MLOps Blog

APRIL 17, 2023

Robustness You need an elastic data model to support: Varying team sizes and structures (a single data scientist only, or maybe a team of one data scientist, 4 machine learning engineers, 2 DevOps engineers, etc.). And, of course, you may also want to promote or download your models from this view.

Metadata 52

Metadata Data Scientist Explainability ML 52

IBM Journey to AI blog

JUNE 5, 2024

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. App analytics include: App usage analytics , which show app usage patterns (such as daily and monthly active users, most- and least-used features and geographical distribution of downloads).

Automation 189

Automation DevOps ML AI 189

The MLOps Blog

JUNE 5, 2023

So they download all of the text on the internet, and they train language models to predict all of that text. I’ve seen tools that help you write and author pull requests more efficiently, and that help automate building documentation. Why do we have MLOps as opposed to DevOps? So along those lines exactly.

Large Language Models 52

Large Language Models Machine Learning Software Engineer ML 52