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 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

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Third, despite the larger adoption of centralized analytics solutions like data lakes and warehouses, complexity rises with different table names and other metadata that is required to create the SQL for the desired sources.

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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

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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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 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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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

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

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 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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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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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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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

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

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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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments.

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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. Machine learning use cases at Brainly The AI department at Brainly aims to build a predictive intervention system for its users.

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Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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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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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

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 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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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. It’s two things. Mikiko Bazeley: 100%. Aside neptune.ai

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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. For example, you can stick in the model, but you can also stick a lot of metadata and extra information about it.

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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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Dlabs.ai

JUNE 11, 2019

Most of them are not directly computer-readable as they are scans and there is some metadata, but not complete enough to be sufficient. But preprocessing requires an ML engineer to get to know the data and make the right choices on how to process it based on preliminary tests when those are possible. Fast forward a little bit.

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AWS Machine Learning Blog

APRIL 19, 2024

environment: HF_MODEL_ID: databricks/dolly-v2-7b HF_TASK: text-generation apiVersion: sagemaker.services.k8s.aws/v1alpha1 kind: Model metadata: name: flan-t5-xxl spec: modelName: flan-t5-xxl executionRoleARN: containers: - image: 763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.0.1-tgi0.9.3-gpu-py39-cu118-ubuntu20.04

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AWS Machine Learning Blog

JUNE 14, 2023

SageMaker Projects helps organizations set up and standardize environments for automating different steps involved in an ML lifecycle. Although notebooks are helpful for model building and experimentation, a team of data scientists and ML engineers sharing code need a more scalable way to maintain code consistency and strict version control.

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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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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

JANUARY 13, 2023

Increase transparency and collaboration by creating a centralized view of all models across TR alongside metadata and health metrics. TR evaluated different relational databases but ended up choosing DynamoDB because the metadata schema for models coming from legacy sources will be very different. Model deployment.

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