AWS Machine Learning Blog

AUGUST 22, 2024

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. The SageMaker Pipelines decorator feature helps convert local ML code written as a Python program into one or more pipeline steps. SageMaker Pipelines can handle model versioning and lineage tracking.

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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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Marktechpost

AUGUST 8, 2023

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. A Seamless Integration for Airbnb’s ML Practitioners Chronon has proven to be a game-changer for Airbnb’s ML practitioners.

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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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Marktechpost

JULY 25, 2024

It is ideal for ML engineers, data scientists, and technical leaders, providing real-world training for production-ready generative AI using Amazon Bedrock and cloud-native services.

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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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ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. We should start by considering the broad elements that should constitute any ML solution, as indicated in the following diagram: Figure 1.2:

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AWS Machine Learning Blog

DECEMBER 4, 2024

You can optionally add request metadata to these inference requests to filter your invocation logs for specific use cases. These logs capture the prompts, responses, and metadata from your previous model interactions, making them a valuable source of data. If you haven’t done this yet, see to the prerequisites section for instructions.

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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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The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. . – How about the ML engineer? Let me explain.

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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

MAY 15, 2024

Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). The image is then uploaded into an Amazon Simple Storage Services (Amazon S3) bucket for images and the metadata about the image is stored in an Amazon DynamoDB table (step 6).

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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

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

FEBRUARY 12, 2025

Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements. You can use metadata filtering to narrow down search results by specifying inclusion and exclusion criteria.

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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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ODSC - Open Data Science

MARCH 27, 2023

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. Some of the questions you’ll explore include How much documentation is appropriate?

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AWS Machine Learning Blog

JANUARY 28, 2025

FMEval is an open source LLM evaluation library, designed to provide data scientists and machine learning (ML) engineers with a code-first experience to evaluate LLMs for various aspects, including accuracy, toxicity, fairness, robustness, and efficiency. This allows you to keep track of your ML experiments.

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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

JUNE 27, 2023

Earth.com didn’t have an in-house ML engineering team, which made it hard to add new datasets featuring new species, release and improve new models, and scale their disjointed ML system. We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

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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

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

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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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

MAY 30, 2024

Additionally, VitechIQ includes metadata from the vector database (for example, document URLs) in the model’s output, providing users with source attribution and enhancing trust in the generated answers. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

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AWS Machine Learning Blog

AUGUST 20, 2024

You can call the SageMaker ListWorkteams or DescribeWorkteam APIs to view workteams’ metadata, including the WorkerAccessConfiguration. Abhinay Sandeboina is a Engineering Manager at AWS Human In The Loop (HIL). He has been in AWS for over 2 years and his teams are responsible for managing ML platform services.

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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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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 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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TheSequence

JULY 12, 2023

At Cruise, we noticed a wide gap between the complexity of cloud infrastructure, and the needs of the ML workforce. ML Engineers want to focus on writing Python logic, and visualizing the impact of their changes quickly. Could you please tell us about the vision and inspiration behind this project?

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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

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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