AWS Machine Learning Blog

MAY 30, 2024

To serve their customers, Vitech maintains a repository of information that includes product documentation (user guides, standard operating procedures, runbooks), which is currently scattered across multiple internal platforms (for example, Confluence sites and SharePoint folders).

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The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters.

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AWS Machine Learning Blog

AUGUST 20, 2024

You can use SageMaker Ground Truth to create labeling jobs, which are workflows where data objects (such as images, videos, or documents) need to be annotated by human workers. Workers can then use these URLs to directly access the necessary files, such as images or documents, in their web browsers for annotation purposes.

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The MLOps Blog

MAY 10, 2023

Cons of Saving ML Models with Joblib 1 Joblib is optimized for numpy arrays, and may not work as well with other object types. 3 Compared to Pickle, Joblib is less well known, which can make it more difficult to locate help and documentation around it. You can find all of this information in the model metadata tab of a Neptune project.

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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

AUGUST 31, 2023

One of the tools available as part of the ML governance is Amazon SageMaker Model Cards , which has the capability to create a single source of truth for model information by centralizing and standardizing documentation throughout the model lifecycle. They provide a fact sheet of the model that is important for model governance.

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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

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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The MLOps Blog

MARCH 14, 2023

This is where version control tools and documentation helped us as team members, and new joiners had access to specific versions of code, data, and ML models. The important part is, all these challenges are very common for any type of ML or DL use case. helped our ML developers better understand the ML 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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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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Machine Learning ML Automation Data Scientist 52

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

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. These stages are applicable to both use case and model stages.

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AWS Machine Learning Blog

AUGUST 31, 2023

One of the tools available as part of the ML governance is Amazon SageMaker Model Cards , which has the capability to create a single source of truth for model information by centralizing and standardizing documentation throughout the model lifecycle. They provide a fact sheet of the model that is important for model governance.

ML 52

ML Data Scientist Machine Learning Automation 52

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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Large Language Models Metadata Machine Learning AI 52

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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Large Language Models Metadata Machine Learning AI 52

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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Large Language Models Metadata Machine Learning AI 52

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. words for English).

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Generative AI Prompt Engineer Prompt Engineering AI 126

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. You want to digitize all of them and then group them into categories and have read about OCR (optical character recognition) software that could “read” a document scan and spew out a blob of text.

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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. The second part is documentation, which is a huge part.

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ML Data Scientist Machine Learning Data Science 52

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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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

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. This is your Custom Python Hook speaking!" A session persists over time unless manually stopped or timed out.

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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. Today we have invited a pretty unique and interesting guest, Stefan Krawczyk. Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., This brings interpersonal challenges, and the AI/ML teams are encouraged to build good relationships with clients to help support the models by telling people how to use the solution instead of just exposing the endpoint without documentation or telling them how.

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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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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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Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

APRIL 19, 2024

Refer to the guidance provided in the API documentation for more details. 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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