Google Research AI blog

JUNE 7, 2023

After developing a new model, one must evaluate whether the speech it generates is accurate and natural: the content must be relevant to the task, the pronunciation correct, the tone appropriate, and there should be no acoustic artifacts such as cracks or signal-correlated noise.

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DagsHub

AUGUST 19, 2023

The prompt has a significant effect on the behavior and output of the model and is also used in the evaluation stage. It will enable the engineer developing it to evaluate the result of the model based on a set of prompts, compare it to other outputs and gauge the model's performance. text, image, video, etc.).

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Data Science Dojo

AUGUST 28, 2023

Model evaluation: Once the LLM is trained, it needs to be evaluated to see how well it performs. Model Management In LLMOps, efficient training, evaluation, and management of LLM models are paramount. Model fine-tuning Model training: Once the data is prepared, the LLM is trained.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Data and model management provide a central capability that governs ML artifacts throughout their lifecycle. ML model development allows various personas to develop a robust and reproducible model training pipeline, which comprises a sequence of steps, from data validation and transformation to model training and evaluation.

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AWS Machine Learning Blog

AUGUST 30, 2023

Overview of solution In this section, we discuss the key components of our solution: choosing the strategy for the task, fine-tuning an LLM, and evaluating the results. Evaluate the results When the training is complete, it’s critical to evaluate the results.

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Mlearning.ai

FEBRUARY 17, 2023

> MLflow has artifact storage. Each run will track the Code version, Start and end time of the run, Source, Key-value input parameters, Key-value metrics, and Artifacts. Recorded artifacts can be in any format such as images or data files. Save evaluation metrics as a dictionary.

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Google Research AI blog

JUNE 29, 2023

Fully erasing the influence of the data requested to be deleted is challenging since, aside from simply deleting it from databases where it’s stored, it also requires erasing the influence of that data on other artifacts such as trained machine learning models. The goal of the competition is twofold.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Specifically, we briefly introduce MLOps principles and focus on the main differentiators compared to FMOps and LLMOps regarding processes, people, model selection and evaluation, data privacy, and model deployment. The following figure illustrates the topics we discuss. Our approach applies to both open-source and proprietary models equally.

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ODSC - Open Data Science

MAY 29, 2023

As you operate an ML model in a production environment you will accumulate artifacts such as versions of your model and dataset, and metrics and statistics for each version. All of these have introduced new artifacts — prompts, PEFT weights, fine-tuning datasets, etc — to the set of items that must be captured, managed, and tracked.

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Heartbeat

OCTOBER 9, 2023

This piece will examine how computer vision has been employed to protect and restore cultural heritage artifacts. Computer vision techniques enable us to generate accurate digital representations of artifacts previously thought to be lost. This simulated restoration approach helps preserve the physical artifacts.

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Google Research AI blog

NOVEMBER 17, 2022

Data Cards are transparency artifacts that provide structured summaries of ML datasets with explanations of processes and rationale that shape the data and describe how the data may be used to train or evaluate models. For example, we've adapted Evaluation Gaps in ML practices into a worksheet for more complete dataset documentation.

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Google Research AI blog

JULY 20, 2023

Last year we announced that Jigsaw , Google’s incubator for building technology that explores solutions to threats to open societies, leveraged our structured societal context knowledge approach during the data preparation and evaluation phases of model development to scale bias mitigation for their widely used Perspective API toxicity classifier.

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Google Research AI blog

MARCH 16, 2023

We also offer research support to some of our organization’s most challenging efforts, including the 1,000 Languages Initiative and ongoing work in the testing and evaluation of language and generative models. We examine systemic social issues and generate useful artifacts for responsible AI development.

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AWS Machine Learning Blog

DECEMBER 13, 2023

We create an automated model build pipeline that includes steps for data preparation, model training, model evaluation, and registration of the trained model in the SageMaker Model Registry. The model registry maintains records of model versions, their associated artifacts, lineage, and metadata. all implemented via CloudFormation.

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The MLOps Blog

APRIL 16, 2024

to share the artifacts and metrics logged by your pipelines with your team, organization, or external stakeholders. The pipeline consists of 4 simple steps, 2 of which use the Neptune-ZenML integration to log training and evaluation metadata. models, metrics, datasets). You’d like to connect ZenML to neptune.ai neptune.ai

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AWS Machine Learning Blog

MARCH 1, 2023

You can use a flywheel to orchestrate the tasks associated with training and evaluating new custom model versions. A flywheel iteration is a workflow that uses the new datasets to evaluate the active model version and to train a new model version. F1-score is an important evaluation metric in machine learning.

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Flipboard

FEBRUARY 15, 2023

We show how to build an end-to-end CI/CD pipeline for data preprocessing and fine-tuning ML models, registering model artifacts to the SageMaker model registry , and automating model deployment with a manual approval to stage and production. An S3 bucket is created for output model artifacts generated from the pipeline.

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Data Science Dojo

OCTOBER 7, 2023

In this blog, we delve into Large Language Model Evaluation and Tracing with LangSmith, emphasizing their pivotal role in ensuring application reliability and performance. Before we jump to understanding the LLM evaluation and tracing process, first have a brief understanding of LangSmith and LangChain. What is LangSmith and LangChain?

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Towards AI

JUNE 5, 2023

As such, my intention with this blog is not to duplicate those definitions but rather to encourage you to question and evaluate your current ML strategy. Key objectives: Before diving into the questions, it’s important to understand the evaluation lens through which they are written. Automation✓ The system must emphasize automation.✓

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Towards AI

APRIL 3, 2024

Diversity attributes: skin-colours, disabilities, gender, age Typical datasets used for training and evaluation are: Celeb-HQ, CalebA-HQ, CelebV-HQ, VoxCeleb2, FaceForensics++ and FFHQ. SimSwap: An Efficient Framework For High Fidelity Face Swapping, 2021, code Image by Chen R.

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Machine Learning (Theory)

DECEMBER 1, 2020

However, this requirement poses a risk of bias in reviewers’ evaluations. Reviewers give almost one point lower score (95% Confidence Interval: [0.24, 1.30]) on a 10-point Likert item for the overall evaluation of a paper when they are told that a paper is a resubmission. Research question. Implications. Research question.

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AWS Machine Learning Blog

JANUARY 5, 2024

Model training: Using the SageMaker SDK, this step runs training code with the respective model image and trains datasets from pre-processing scripts while generating the trained model artifacts. Save model: This step creates a model from the trained model artifacts. It then aggregates that data for further processing.

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phData

OCTOBER 12, 2022

This GraphQL API allows you to easily query various information about your dbt Cloud runs and projects and helps you evaluate not just your current data health , but your overall data health at a point in time. You can see this sort of information in action when you watch how a job is executed in dbt Cloud.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Reproducibility and traceability must be enabled automatically by the end-to-end data processing pipelines, where many mandatory documentation artifacts, such as data lineage reports and model cards, can be prepared automatically. Compliance – The platform can also help improve regulatory compliance of AI-enabled propositions.

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AWS Machine Learning Blog

APRIL 21, 2023

The first pipeline includes the steps needed to prepare data, train the model, and evaluate the performance of the model. If the model performs acceptably according to the evaluation criteria, the pipeline continues with a step to baseline the data using a built-in SageMaker Pipelines step. Evaluate the model. Train the model.

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AWS Machine Learning Blog

OCTOBER 5, 2023

We use the Amazon Comprehend model training output artifacts like a confusion matrix to tune model performance and guide you on improving your training data. For this dataset, we tested how the model’s performance on the evaluation dataset changes as we provide more samples. On the New menu, choose Terminal.

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AWS Machine Learning Blog

APRIL 18, 2024

With SageMaker JumpStart, you can evaluate, compare, and select foundation models (FMs) quickly based on predefined quality and responsibility metrics to perform tasks like article summarization and image generation. None of your data is used to train the underlying models.

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Heartbeat

APRIL 11, 2023

Artifacts are just one of the many tools in the Comet toolbox to help ease model management. Evaluate the model To determine how well the model generalizes to new data after training, we must evaluate its performance on a test set. To evaluate the model, we will make use of the dataset’s test collection.

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AWS Machine Learning Blog

AUGUST 16, 2023

The first step in our workflow is to prepare the BigEarthNet-S2 dataset for DINO training and evaluation. As before, we use the custom BigEarthNet dataset to load images during training and evaluation. We use the binary cross-entropy for the loss function and compute the average precision to evaluate the performance of the model.

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AWS Machine Learning Blog

MAY 23, 2023

Models are mathematical artifacts that take input data, perform calculations and computations on them, and generate predictions or inferences. You can benefit from all SageMaker features and functions, including model training, tuning, evaluation, deployment, and monitoring.

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Google Research AI blog

JULY 11, 2023

Posted by Amir Yazdanbakhsh, Research Scientist, and Vijay Janapa Reddi, Visiting Researcher, Google Research Computer Architecture research has a long history of developing simulators and tools to evaluate and shape the design of computer systems. It comprises two main components: 1) the ArchGym environment and 2) the ArchGym agent.

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Google Research AI blog

MARCH 30, 2023

This starting condition led to a model-centric paradigm in which (1) the training dataset and test dataset were “frozen” artifacts and the goal was to develop a better model, and (2) the test dataset was selected randomly from the same pool of data as the training set for statistical reasons.

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Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

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AWS Machine Learning Blog

MARCH 25, 2024

AWS Config enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS uses third-party auditors to evaluate its services for compliance with various programs. AWS Artifact allows you to download third-party audit reports. You can monitor Amazon Transcribe using AWS CloudTrail and Amazon CloudWatch.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

To build Amazon Personalize artifacts in this post, we use a dataset from IMDb, the world’s most authoritative source for movie, TV, and celebrity content, available on AWS Marketplace, as well as the MovieLens dataset prepared by GroupLens research at the University of Minnesota, consisting of user rankings for various movies.

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AWS Machine Learning Blog

MAY 16, 2023

Because the models are hosted and deployed on AWS, you can rest assured that your data, whether used for evaluating or using the model at scale, is never shared with third parties. Retrieve artifacts and deploy an endpoint. The inference script is prepacked with the model artifact. Select a pre-trained model.

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Heartbeat

MARCH 5, 2024

The augmented photos can occasionally have artifacts due to image augmentation : These artifacts may be brought on by the individual data augmentation methods employed or by the computer vision model's inherent constraints.

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