AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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AWS Machine Learning Blog

MAY 15, 2024

This blog post shares more about how generative AI solutions from Amazon Ads help brands create more visually rich consumer experiences. In this blog post, we describe the architectural and operational details of how Amazon Ads implemented its generative AI-powered image creation solution on AWS.

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The MLOps Blog

JANUARY 26, 2024

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline?

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AWS Machine Learning Blog

OCTOBER 20, 2023

An ML engineer deploys the model pipeline into the ML team test environment using a shared services CI/CD process. After stakeholder validation, the ML model is deployed to the team’s production environment. ML operations This module helps LOBs and ML engineers work on their dev instances of the model deployment template.

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The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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Marktechpost

JULY 19, 2023

Stakeholders such as ML engineers, designers, and domain experts must work together to identify a model’s expected and potential faults. Instead, ML engineers collaborate with domain experts and designers to describe a model’s expected capabilities before it is iterated and deployed.

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AWS Machine Learning Blog

AUGUST 20, 2024

In this blog post, we show you how to enable this feature, allowing you to enhance your data security as needed, and outline the success criteria for this feature, including the scenarios where it will be most beneficial. Follow the guidance provided in this blog to configure these security measures effectively.

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AWS Machine Learning Blog

OCTOBER 28, 2024

You can use this framework as a starting point to monitor your custom metrics or handle other unique requirements for model quality monitoring in your AI/ML applications. Data Scientist at AWS, bringing a breadth of data science, ML engineering, MLOps, and AI/ML architecting to help businesses create scalable solutions on AWS.

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AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Endpoints had to be deployed manually as well.

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The MLOps Blog

MAY 10, 2023

Finally, you can store the model and other metadata information using the INSERT INTO command. Storing ML models in model registry A model registry is a central repository that can store, version, and manage machine learning models. It typically includes features like model versioning , metadata control, comparing model runs, etc.

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AWS Machine Learning Blog

JANUARY 17, 2023

This post guides you through the steps to get started with setting up and deploying Studio to standardize ML model development and collaboration with fellow ML engineers and ML scientists. cdk.json – Contains metadata, and feature flags. Marcelo Aberle is an ML Engineer in the AWS AI organization.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017.

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AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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AWS Machine Learning Blog

MAY 30, 2024

In this blog, we walkthrough the architectural components, evaluation criteria for the components selected by Vitech and the process flow of user interaction within VitechIQ. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data. It also contains each scene’s metadata, its image ID, and a preview image reference.

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AWS Machine Learning Blog

OCTOBER 2, 2024

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

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AWS Machine Learning Blog

JUNE 25, 2024

He has experience with multiple programming languages, AWS cloud services, AI/ML technologies, product and operations management, pre and early seed start-up ventures, and corporate finance. Geeta Gharpure is a senior software developer on the Annapurna ML engineering team. os operator: In values: - linux - key: node.kubernetes.io/instance-type

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AWS Machine Learning Blog

JULY 24, 2024

Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. By logging your datasets with MLflow, you can store metadata, such as dataset descriptions, version numbers, and data statistics, alongside your MLflow runs.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. 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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The MLOps Blog

JANUARY 24, 2024

help data scientists systematically record, catalog, and analyze modeling artifacts and experiment metadata. is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. Experiment trackers like neptune.ai Even though neptune.ai Aside neptune.ai

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AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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AWS Machine Learning Blog

MARCH 1, 2023

Solution overview The ML solution for LTV forecasting is composed of four components: the training dataset ETL pipeline, MLOps pipeline, inference dataset ETL pipeline, and ML batch inference. ML engineers no longer need to manage this training metadata separately.

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AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. We defined logic that would take in model metadata, format the endpoint deterministically based on the metadata, and check whether the endpoint existed.

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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. Fotinos Kyriakides is an ML Engineer with AWS Professional Services. The following diagram illustrates the end-to-end workflow for Ball Recovery Time.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Security SMEs review the architecture based on business security policies and needs.

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AWS Machine Learning Blog

AUGUST 31, 2023

Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. They provide a fact sheet of the model that is important for model governance.

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The MLOps Blog

MARCH 15, 2023

Cost and resource requirements There are several cost-related constraints we had to consider when we ventured into the ML model deployment journey Data storage costs: Storing the data used to train and test the model, as well as any new data used for prediction, can add to the cost of deployment. S3 buckets. Redshift, S3, and so on.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. Here you can provide the metadata for this model hosting information along with the input format/template your specific model expects. What is FMEval?

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AWS Machine Learning Blog

JUNE 8, 2023

A session stores metadata and application-specific data known as session attributes. Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. A session persists over time unless manually stopped or timed out. He leads the NYC machine learning and AI meetup.

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AWS Machine Learning Blog

AUGUST 31, 2023

Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. They provide a fact sheet of the model that is important for model governance.

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The MLOps Blog

MARCH 14, 2023

Read more How to Solve Reproducibility in ML Machine learning reproducibility checklist: solutions we adapted As ML engineers we make sure that every problem should have one or multiple possible solutions, as is the case for ML reproducibility challenges. seemed like the right solution.

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The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. Stefan: Thankfully, Stitch Fix had a blog that had a reasonable amount of readership. Stefan: Yeah.

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The MLOps Blog

OCTOBER 3, 2023

I see so many of these job seekers, especially on the MLOps side or the ML engineer side. There’s no component that stores metadata about this feature store? Mikiko Bazeley: In the case of the literal feature store, all it does is store features and metadata. For example, Shopify published a blog post about Merlin.

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The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data. If you want to learn more about Brainly’s technology ecosystem, check out their technology blog.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

By directly integrating with Amazon Managed Service for Prometheus and Amazon Managed Grafana and abstracting the management of hardware failures and job resumption, SageMaker HyperPod allows data scientists and ML engineers to focus on model development rather than infrastructure management.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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