Towards AI

FEBRUARY 13, 2023

It also overcomes complex challenges in speech recognition and computer vision, such as creating a transcript of a sound sample or a description of an image. Our model gets a prompt and auto-completes it. NLP doesn’t just deal with written text. Cool, we learned what NLP is in this section. Note that this […]

Auto-classification 91

Auto-classification NLP Auto-complete Computer Vision 91

Flipboard

FEBRUARY 13, 2023

Luckily, OpenCV is pip-installable: $ pip install opencv-contrib-python If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide — it will have you up and running in a matter of minutes. . Or requires a degree in computer science? EPOCHS ) on Lines 20-23.

Computer Vision 106

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AWS Machine Learning Blog

FEBRUARY 20, 2024

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times. __dict__[WAV2VEC2_MODEL].get_model(dl_kwargs={"model_dir":

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Metadata Auto-complete Machine Learning Deep Learning 95

AWS Machine Learning Blog

SEPTEMBER 5, 2023

These models have revolutionized various computer vision (CV) and natural language processing (NLP) tasks, including image generation, translation, and question answering. Python 3.10 The notebook queries the endpoint in three ways: the SageMaker Python SDK, the AWS SDK for Python (Boto3), and LangChain.

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Auto-complete Python Computer Vision Large Language Models 93

AWS Machine Learning Blog

FEBRUARY 19, 2024

In this post, we walk through how to use the Titan Image Generator and Titan Multimodal Embeddings models via the AWS Python SDK. Code examples are provided in Python, and JavaScript (Node.js) is also available in this GitHub repository. For Python scripts, you can use the AWS SDK for Python (Boto3).

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AWS Machine Learning Blog

MAY 9, 2023

In this post, we help you understand the Python backend that is supported by Triton on SageMaker so that you can make an informed decision for your workloads and achieve great results. SageMaker MMEs can horizontally scale using an auto scaling policy and provision additional GPU compute instances based on specified metrics.

Python 81

Python ML Auto-complete Deep Learning 81

Viso.ai

MARCH 29, 2024

About us: At viso.ai, we’ve built the end-to-end machine learning infrastructure for enterprises to scale their computer vision applications easily. Viso Suite, the end-to-end computer vision solution What is Streamlit? Streamlit is a Python-based library specifically developed for machine learning engineers.

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Computer Vision Auto-classification Machine Learning Python 52

PyImageSearch

JUNE 5, 2023

trainInput = trainInput.map( read_train_example, num_parallel_calls=AUTO).shuffle( repeat() trainOutput = trainOutput.map( read_train_example, num_parallel_calls=AUTO).shuffle( batch(config.INFER_BATCH_SIZE) testOutput = testOutput.map(read_test_example, num_parallel_calls=AUTO).shuffle( Let us open the train.py

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Computer Vision Auto-complete Deep Learning Explainability 56

PyImageSearch

OCTOBER 2, 2023

in their paper Auto-Encoding Variational Bayes. Auto-Encoding Variational Bayes. With that, we’ve completed the training of a variational autoencoder on the Fashion-MNIST dataset. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? The torch.nn

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AWS Machine Learning Blog

JANUARY 8, 2024

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. When the script ends, a completion status along with the time taken will be returned to the SageMaker studio console. We provide you with two different solutions for this use case.

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IDP Auto-complete Python Natural Language Processing 88

AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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Flipboard

NOVEMBER 20, 2023

We also discuss how to transition from experimenting in the notebook to deploying your models to SageMaker endpoints for real-time inference when you complete your prototyping. After confirming your quota limit, you need to complete the dependencies to use Llama 2 7b chat. Python 3.10 transformers==4.33.0 accelerate==0.21.0

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Auto-complete LLM Machine Learning Natural Language Processing 128

AWS Machine Learning Blog

OCTOBER 11, 2023

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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AWS Machine Learning Blog

SEPTEMBER 25, 2023

Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity. The inference server handles this automatically, so no client-side code changes are needed.

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Flipboard

AUGUST 17, 2023

Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml Launch SageMaker Studio Complete the following steps to launch your SageMaker Studio domain: On the SageMaker console, choose Domains in the navigation pane.

ML 93

ML Auto-complete Auto-classification Machine Learning 93

AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. For Secret type , choose Other type of secret.

Data Scientist 90

Data Scientist Generative AI ML Machine Learning 90

AWS Machine Learning Blog

JUNE 13, 2023

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.

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Generative AI Auto-complete AI Modeling Machine Learning 65

AWS Machine Learning Blog

MARCH 28, 2024

LangChain is an open source Python library designed to build applications with LLMs. When you create an AWS account, you get a single sign-on (SSO) identity that has complete access to all the AWS services and resources in the account. There was no monitoring, load balancing, auto-scaling, or persistent storage at the time.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One of the primary reasons that customers are choosing a PyTorch framework is its simplicity and the fact that it’s designed and assembled to work with Python. xlarge instance.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. The SageMaker Python SDK is a high-level Python API that abstracts some of the steps and configuration, and makes it easier to deploy models. For that, we are offering improvements in the Python SDK.

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ML Auto-complete Python LLM 103

Pickl AI

JANUARY 23, 2023

It supports languages like Python and R and processes the data with the help of data flow graphs. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs. Keras supports a high-level neural network API written in Python. Cons Low level computation cannot be handled by keras.

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Machine Learning ML Neural Network Auto-classification 52

AWS Machine Learning Blog

MAY 25, 2023

Prerequisites Complete the following prerequisite steps: If you’re a first-time user of QuickSight in your AWS account, sign up for QuickSight. amazonaws.com/ :latest Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Download the CloudFormation template kendrablog-sam-template.yml.

ML 71

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

Now you can also fine-tune 7 billion, 13 billion, and 70 billion parameters Llama 2 text generation models on SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK. What is Llama 2 Llama 2 is an auto-regressive language model that uses an optimized transformer architecture.

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AWS Machine Learning Blog

SEPTEMBER 11, 2023

It’s an auto-regressive language model that uses an optimized transformer architecture. Discover models You can access the foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. Eiffel Tower: No trip to Paris is complete without a visit to the iconic Eiffel Tower.

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Machine Learning LLM Auto-complete ML 103

AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. His expertise lies in Deep Learning in the domains of Natural Language Processing (NLP) and Computer Vision.

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Auto-complete Deep Learning LLM Software Engineer 72

AWS Machine Learning Blog

JULY 18, 2023

What is Llama 2 Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Discover models You can access the foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. In this post, we walk through how to use Llama 2 models via SageMaker JumpStart.

ML 98

ML Machine Learning Auto-complete Large Language Models 98

AWS Machine Learning Blog

MAY 4, 2023

For ultra-large models that don’t fit into a single accelerator, data flows directly between accelerators with NeuronLink, bypassing the CPU completely. You can use a SageMaker notebook or an Amazon Elastic Compute Cloud (Amazon EC2) instance to compile the model. These endpoints are fully managed and support auto scaling.

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The MLOps Blog

JANUARY 19, 2024

But nowadays, it is used for various tasks, ranging from language modeling to computer vision and generative AI. You can find all the code in two Colab notebooks: Fine-tuning Model selection Related post How to Version and Organize ML Experiments That You Run in Google Colab Read more We will use Python 3.10 in our codes.

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AWS Machine Learning Blog

JANUARY 31, 2024

In this post, we present an approach to develop a deep learning-based computer vision model to detect and highlight forged images in mortgage underwriting. If the image is completely unmodified, then all 8×8 squares should have similar error potentials. Each re-encoding (resave) of the image adds more quality loss to the image.

Computer Vision 90

Computer Vision Auto-complete Deep Learning Auto-classification 90

AWS Machine Learning Blog

DECEMBER 14, 2023

Complete the following steps to edit an existing space: On the space details page, choose Stop space. Reconfigure the compute, storage, or runtime. EFS mounts provide a solid alternative for sharing Python environments like conda or virtualenv across multiple workspaces. Choose Create JupyterLab space. Choose Create space.

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Generative AI AI Tools ML AI 98

Google Research AI blog

FEBRUARY 28, 2023

Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M UGIF A multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone. We also continued to release sustainability data via Data Commons and invite others to use it for their research.

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The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., Can you see the complete model lineage with data/models/experiments used downstream? Auto-annotation tools such as Meta’s Segment Anything Model and other AI-assisted labeling techniques.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

Mlearning.ai

MAY 23, 2023

Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements. It is the discounting of those subjects that did not complete the trial. What are auto-encoders?

Data Science 52

Data Science Neural Network Deep Learning Machine Learning 52

AWS Machine Learning Blog

MARCH 6, 2023

Although this post focuses on LLMs, most of its best practices are relevant for any kind of large-model training, including computer vision and multi-modal models, such as Stable Diffusion. The AWS Python SDK Boto3 may also be combined with Torch Dataset classes to create custom data loading code.

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Large Language Models LLM Machine Learning ML 78

Explosion

NOVEMBER 21, 2019

sense2vec reloaded: the updated library sense2vec is a Python package to load and query vectors of words and multi-word phrases based on part-of-speech tags and entity labels. It’s just as important for the model to learn what’s not an entity.We’re ignoring auto-replies and bot messages (e.g. assert doc[3:6].text

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. It has faster loading and uses multi-process loading on Python. It supports AOT compilation and uses CPU to partition the model.

Prompt Engineer 78

Prompt Engineer Prompt Engineering Deep Learning Large Language Models 78

Dlabs.ai

JANUARY 16, 2023

The lines are then parsed into pythonic dictionaries. We decided to analyze the images in the following ways: Split the image into tiles and use tiles as input to a computer vision model Extract histograms of colors in images Tile generation The raw TIFF input images are too big to use for processing directly.

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Neural Network ML Auto-classification Convolutional Neural Networks 52