Pickl AI

NOVEMBER 4, 2024

Summary: Python for Data Science is crucial for efficiently analysing large datasets. With numerous resources available, mastering Python opens up exciting career opportunities. Introduction Python for Data Science has emerged as a pivotal tool in the data-driven world. As the global Python market is projected to reach USD 100.6

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Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective.

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phData

MARCH 22, 2023

As companies continue to adopt machine learning (ML) in their workflows, the demand for scalable and efficient tools has increased. In this blog post, we will explore the performance benefits of Snowpark for ML workloads and how it can help businesses make better use of their data. Total': (t_write - t_start).total_seconds()

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PyImageSearch

DECEMBER 4, 2023

Home Table of Contents ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent! This blog is the 1st of a 3-part series: ML Days in Tashkent — Day 1: City Tour (this tutorial) ML Days in Tashkent — Day 2: Sprints and Sessions ML Days in Tashkent — Day 3: Demos and Workshops ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent!

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Pickl AI

NOVEMBER 7, 2023

Python’s simplicity and readability, along with its vast community and resources, make it an excellent choice for beginners. With this Python Cheat Sheet at your disposal, you’re well on your way to mastering the basics. Keep practicing, and you’ll be creating amazing Python applications in no time.

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Mlearning.ai

APRIL 6, 2023

Automate and streamline our ML inference pipeline with SageMaker and Airflow Building an inference data pipeline on large datasets is a challenge many companies face. SageMaker Batch Job Allows you to run batch inference on large datasets and generate predictions in a batch mode using machine learning (ML) models hosted in SageMaker.

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Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

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phData

JULY 2, 2024

Snowpark, offered by the Snowflake AI Data Cloud , consists of libraries and runtimes that enable secure deployment and processing of non-SQL code, such as Python, Java, and Scala. In this blog, we’ll cover the steps to get started, including: How to set up an existing Snowpark project on your local system using a Python IDE.

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Mlearning.ai

FEBRUARY 7, 2023

Photo by Christopher Gower on Unsplash There are multiple ways to get started with Python, but I have found Anaconda to be the best. If you are interested in learning Python, this is a quick guide. Follow the download instructions below, and you will be writing code soon. Visit [link] and download. Launch Spyder.

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Mlearning.ai

MAY 31, 2023

In this article, we will explore the process of creating a simple chatbot using Python and NLP techniques. Setting up the Development Environment To get started, we need to set up our Python development environment with the necessary libraries and tools. These techniques form the foundation for building intelligent chatbots.

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Towards AI

JUNE 27, 2023

Let’s get started with the best machine learning (ML) developer tools: TensorFlow TensorFlow, developed by the Google Brain team, is one of the most utilized machine learning tools in the industry. PyTorch PyTorch, a Python-based machine learning library, stands out among its peers in the machine learning tools ecosystem.

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Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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PyImageSearch

DECEMBER 11, 2023

Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google. The focus of his presentation was clear and forward-thinking: Accelerate AI/ML research and application.

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AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. Create and run the SageMaker pipeline.

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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.

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The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. Our next generation release that is faster, more Pythonic and Dynamic as ever for details. With the recent PyTorch 2.0 Refer to PyTorch 2.0:

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). The ML Process The machine learning process typically consists of the following steps: Data Collection Gathering relevant data is the first step in the machine learning process.

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Heartbeat

FEBRUARY 9, 2023

One can build NLP projects in different ways, and one of those is by using the Python library S paCy. SpaCy is a free, open-source library written in Python for advanced Natural Language Processing. Installation of SpaCy The installation of Python on the system is a prerequisite for configuring SpaCy. What is spaCy?

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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AWS Machine Learning Blog

OCTOBER 19, 2023

For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). This post demonstrates how to use Amazon SageMaker Data Wrangler and Amazon Comprehend to automatically redact PII from tabular data as part of your machine learning operations (ML Ops) workflow.

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Mlearning.ai

JULY 29, 2023

post1 - scikit-learn==0.24.2 - cryptography==41.0.1 - python-dateutil==2.8.2 - datasets==2.11.0 - soundfile==0.12.1 - librosa==0.10.0.post2 post1 - scikit-learn==0.24.2 - cryptography==41.0.1 - python-dateutil==2.8.2 - datasets==2.11.0 - soundfile==0.12.1 - librosa==0.10.0.post2 post1 - azureml-mlflow==1.51.0 - azureml-metrics==0.0.14.post1

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Mlearning.ai

JULY 4, 2023

In this article I will show you how to run a version of the Vicuna model in WSL2 with GPU acceleration and prompt the model from Python via an API. The CUDA toolkit can be downloaded from the NVidia website. venv Using python venv is a personal preference — I like how lightweight it is. Simply run python download-model.py

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI. Pegasus Transformer This is a part of the Transformers library available in Python 3.

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Data Science Dojo

JUNE 14, 2023

Learn how the synergy of AI and ML algorithms in paraphrasing tools is redefining communication through intelligent algorithms that enhance language expression. Paraphrasing tools in AI and ML algorithms Machine learning is a subset of AI. Pegasus Transformer This is a part of the Transformers library available in Python 3.

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Heartbeat

JUNE 26, 2023

A guide to performing end-to-end computer vision projects with PyTorch-Lightning, Comet ML and Gradio Image by Freepik Computer vision is the buzzword at the moment. Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries.

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ODSC - Open Data Science

JANUARY 5, 2024

Python is still one of the most popular programming languages that developers flock to. In this blog, we’re going to take a look at some of the top Python libraries of 2023 and see what exactly makes them tick. NumPy arrays are similar to lists in Python, but they are optimized for performance.

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AWS Machine Learning Blog

APRIL 19, 2023

Right now, most deep learning frameworks are built for Python, but this neglects the large number of Java developers and developers who have existing Java code bases they want to integrate the increasingly powerful capabilities of deep learning into. Business requirements We are the US squad of the Sportradar AI department.

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Mlearning.ai

MAY 15, 2023

Hey guys in this video we will see the best Python Interview Questions. Python has become one of the most popular programming languages in the world, thanks to its simplicity, versatility, and vast array of applications. As a result, Python proficiency has become a valuable skill sought after by employers across various industries.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. Machine Learning In this section, we look beyond ‘standard’ ML practices and explore the 6 ML trends that will set you apart from the pack in 2021.

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AWS Machine Learning Blog

OCTOBER 2, 2023

You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. What is SageMaker JumpStart With SageMaker JumpStart, ML practitioners can choose from a growing list of best-performing foundation models.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Solution architecture The following diagram illustrates the lab’s architecture: This solution uses the following AI and machine learning (AI/ML), serverless, and managed technologies: Amazon SageMaker , a fully managed machine learning service that enables data scientists and developers to build, train and deploy machine learning applications.

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AWS Machine Learning Blog

OCTOBER 22, 2024

Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Create a complete AI/ML pipeline for fine-tuning an LLM using drag-and-drop functionality. In this example, you will use a Python function.

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. When an On-Demand job is launched, it goes through five phases: Starting, Downloading, Training, Uploading, and Completed.

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AWS Machine Learning Blog

APRIL 25, 2024

Hugging Face is a popular open source hub for machine learning (ML) models. AWS and Hugging Face have a partnership that allows a seamless integration through SageMaker with a set of AWS Deep Learning Containers (DLCs) for training and inference in PyTorch or TensorFlow, and Hugging Face estimators and predictors for the SageMaker Python SDK.

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