AWS Machine Learning Blog

AUGUST 3, 2023

Docker in your development environment (local machine or a SageMaker notebook instance where you are deploying the solution from). This folder contains two main files: a Dockerfile that defines the Docker image that will run in our Lambda function, and the app.py The AWS Cloud Development Kit (AWS CDK) installed.

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AWS Machine Learning Blog

APRIL 19, 2024

Looking to cloud infrastructure, we needed a system that allows us to access GPUs, scale up and down quickly to handle spiky, heterogeneous workloads, and run large Docker images. To simplify deployment and improve reproducibility, we follow the principles of the do-framework and the structure of the depend-on-docker template.

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phData

JUNE 30, 2023

Once a container is built, it can be transferred to any compute environment with a container runtime such as Docker or Kubernetes. Real-Time AI/ML Applications – Most machine uses of machine learning models in production systems fall into one of two categories: batch or real-time. Why Does Snowpark Container Services Matter?

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Applied Data Science

OCTOBER 25, 2021

Docker Docker wraps software in containers., Docker seems to be the tool of choice for commercial purposes, but, wandering around research-oriented GitHub projects myself, I most often encounter CONDA environments. To use it, one needs to install all listed packages from scratch.

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AWS Machine Learning Blog

AUGUST 11, 2023

The input data includes text strings, a list of desired categories for classification, and whether the classification is multi-label or not for synchronous (real-time) inference. deploy_image_uri – Use the image_uris Python SDK function to get the pre-built SageMaker Docker image for the model_id.

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O'Reilly Media

AUGUST 8, 2023

At its peak, ChatGPT was in very exclusive company: it’s not quite on the level of Python, Kubernetes, and Java, but it’s in the mix with AWS and React, and significantly ahead of Docker. Although large language models clearly fall into the category of NLP, we suspect that most users associate NLP with older approaches to building chatbots.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Docker v20.10 Pre-equipped with tools like AWS CLI, AWS CDK, and Docker, AWS Cloud9 can function as your deployment workstation. The response from the LLM is stored in DynamoDB along with the user’s query, the timestamp, a unique identifier, and other arbitrary identifiers for the item such as question category. Python 3.6

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DrivenData Labs

MARCH 27, 2023

Download the official competition Docker image using make pull Save all of your submission files, including the required main.py adding: solution.py (deflated 73%) Launch an instance of the competition Docker image, and run the same inference process that will take place in the official runtime using make test-submission Run scripts/score.py

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AWS Machine Learning Blog

MAY 8, 2024

Then, the selected features associated with the churn reason are further classified into two categories: network issue-based and non-network issue-based. Reusable components – Employing containerized environments, such as Docker images, and custom modules promoted the bring your own code approach within Dialog Axiata’s ML pipelines.

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AWS Machine Learning Blog

MAY 16, 2023

Recent advances in ML have given rise to a new class of models known as foundation models , which are typically trained on billions of parameters and are adaptable to a wide category of use cases, such as text summarization, generating digital art, and language translation. The deploy method may take a few minutes.

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ODSC - Open Data Science

JUNE 1, 2023

We can then create the table and schema by running the following command: docker run -v $PWD/pinot/config:/config --network pizza-shop apachepinot/pinot:0.12.0-arm64 We’ll also update our Streamlit dashboard to view the top-selling products and categories. The config to connect to Kafka is defined under tableIndexConfig.streamConfigs.

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AWS Machine Learning Blog

JULY 3, 2023

We upload the Ground Truth UI template and label categories file, and create the verification job. The notebook linked to this post uses a private workforce to perform the labeling; you can change this if you’re using other types of workforces.

Deep Learning 78

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. The new category is often called MLOps.

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AWS Machine Learning Blog

AUGUST 22, 2023

When choosing an FL framework, we usually consider its support for model category, ML framework, and device or operation system. With the SageMaker pre-built Docker images for the scikit-learn framework and SageMaker managed training process, we train a logistic regression model on this dataset using federated learning.

Machine Learning 69

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O'Reilly Media

SEPTEMBER 15, 2021

While Google is the third-most-important cloud provider, only 26 respondents (roughly 1%) claimed any Google certification, all under the “Other” category. The cloud and containers category includes tools like Docker and Kubernetes, cloud providers like AWS and Microsoft Azure, and disciplines like MLOps.

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AWS Machine Learning Blog

JULY 20, 2023

These categories include: Boosting – Training sequentially multiple weak learners, where each incorrect prediction from previous learners in the sequence is given a higher weight and input to the next learner, thereby creating a stronger learner. The following code block shows the code we use to start the training.

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Explosion

AUGUST 3, 2017

prodigy dataset gh_issues "Classify issues on GitHub" prodigy textcat.teach gh_issues en_core_web_sm "docs" --api github --label DOCS Opening localhost:8080 , we get a sequence of recent GitHub issue titles, displayed with our category as the title. If the category is correct, click accept , press a , or swipe left on a touch interface.

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AWS Machine Learning Blog

MAY 31, 2023

For demonstration purposes, we use a pre-trained ResNet50 model that can classify images into 1,000 categories. docker run --gpus=all --rm -it -v `pwd`/workspace:/workspace nvcr.io/nvidia/pytorch:23.02-py3 Prerequisites You first need an AWS account and an AWS Identity and Access Management (IAM) administrator user.

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Snorkel AI

MAY 3, 2023

As SaaS, hosted by a customer on their Kubernetes cluster, Docker, or something else? As we thought through designs and whether or not they were deployable, we also realized that we could bucket our customers into two categories: compute-constrained and compute-abundant. Customer infrastructure profiles How will the system be deployed?

Machine Learning 52

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Snorkel AI

MAY 3, 2023

As SaaS, hosted by a customer on their Kubernetes cluster, Docker, or something else? As we thought through designs and whether or not they were deployable, we also realized that we could bucket our customers into two categories: compute-constrained and compute-abundant. Customer infrastructure profiles How will the system be deployed?

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 3, 2023

As SaaS, hosted by a customer on their Kubernetes cluster, Docker, or something else? As we thought through designs and whether or not they were deployable, we also realized that we could bucket our customers into two categories: compute-constrained and compute-abundant. Customer infrastructure profiles How will the system be deployed?

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AWS Machine Learning Blog

JANUARY 17, 2023

Encode categorical features into one-hot values, where the missing values are counted as one of the categories for each feature. After training is complete, we retrieve the AutoGluon inference image and deploy the model: # Retrieve the inference docker container uri inference_image_uri = image_uris.retrieve( "autogluon", region=boto3.Session().region_name,

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The MLOps Blog

MAY 10, 2023

Docker is one of the tools which allows you to do this. The model registry is a category of tools that solve this issue for you. Once a model is packaged as a Bento, it can be deployed to various serving platforms like AWS Lambda , Kubernetes , or Docker. Now let’s see how we can save our model. You can know more about it here.

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The MLOps Blog

JANUARY 23, 2023

Frameworks like Kedro, ZenML, and Metaflow fall into this category. Wouldn’t some artifact registry like Docker Hub or JFrog Artifactory be the thing? They are mainly in CI/CD pipelines, docker registry, production model monitoring tools, or experiment trackers. Tools like Prefect, Argo, and Kubeflow help you do that.

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The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: I think it comes down to understanding the different types of feature stores that are out there, and even understanding why a virtual feature store is maybe just a terrible name for what FeatureForm is category-wise; it’s not very descriptive. It had broken a bunch of stuff for Docker. What took the most time?

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The MLOps Blog

MAY 9, 2023

json' , } ) Packages and containers You could decide to use a container tool such as Docker or another method to ensure your code can run anywhere. txt' , 'output2' : '/output2.json'

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AWS Machine Learning Blog

JANUARY 9, 2023

SageMaker inference endpoints use Docker containers for hosting ML models. Containers allow you package software into standardized units that run consistently on any platform that supports Docker. The ML application in this use case may want to use different custom models for recommending different categories of products.

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The MLOps Blog

JUNE 27, 2023

May be interesting for you Deploying ML Models on GPU With Kyle Morris Vector databases and data retrieval Vector databases are a new category of a database management system designed to search across images, video, text, audio, and other forms of unstructured data via their content rather than human-generated labels or tags.

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The MLOps Blog

DECEMBER 29, 2022

Using Docker, Kubernetes, Pulumi, setting up, and being able to containerize apps that rely on GPUs. Those are very different categories of optimizing models. Is this on Docker? Kyle: Basically, be Docker , Kubernetes , Pulumi. Things around deploying GPUs, and generally just working with GPU-based models?

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Explosion

MARCH 17, 2019

If you’ve been using our annotation tool Prodigy , you might recognize this format from the pattern files you can load in to bootstrap new entity types and text categories. average was 2 lbs.") The formats are fully compatible, so you’ll be able to use your Prodigy patterns with the entity ruler, and vice versa. For the v2.1

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