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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Flipboard

FEBRUARY 13, 2023

Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application. The crop_faces.py

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Data Pipeline Deep Learning Deep Learning Python 140

Data Science Dojo

MARCH 15, 2023

It comprises of four features, it is customizable, observable with a full view of data visualization, testable and versionable to track changes, and can easily be rolled back if needed. Modern stack : It is built using modern open-source technologies such as Python, Flask, and Vue.js, making it easy to extend and integrate with other tools.

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Azure Data Science Data Engineering Data Engineer 195

Towards AI

FEBRUARY 29, 2024

Apache Kafka plays a crucial role in enabling data processing in real-time by efficiently managing data streams and facilitating seamless communication between various components of the system. Apache Kafka Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications.

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PyImageSearch

MARCH 20, 2023

Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application. Figure 1: Overview of our Face Recognition Pipeline (source: image by the author).

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AWS Machine Learning Blog

AUGUST 22, 2024

This makes managing and deploying these updates across a large-scale deployment pipeline while providing consistency and minimizing downtime a significant undertaking. Generative AI applications require continuous ingestion, preprocessing, and formatting of vast amounts of data from various sources. We use Python to do this.

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Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss.

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Deep Learning Deep Learning Data Pipeline Computer Science 140

The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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Data Pipeline ETL SQL Data Quality 59

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Choose Choose File and navigate to the location on your computer where the CloudFormation template was downloaded and choose the file.

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The MLOps Blog

JANUARY 26, 2024

Many ML systems benefit from having the feature store as their data platform, including: Interactive ML systems receive a user request and respond with a prediction. An interactive ML system either downloads a model and calls it directly or calls a model hosted in a model-serving infrastructure. All of them are written in Python.

Machine Learning 72

Machine Learning Machine Learning ML ML 72

Mlearning.ai

MARCH 15, 2023

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

Python 52

Python AWS Exploratory Data Analysis Machine Learning 52

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., Monte Carlo Monte Carlo is a popular data observability platform that provides real-time monitoring and alerting for data quality issues. and Pandas or Apache Spark DataFrames.

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Machine Learning Machine Learning ML ML 134

phData

MARCH 22, 2023

Snowpark , an innovative technology from the Snowflake Data Cloud , promises to meet this demand by allowing data scientists to develop complex data transformation logic using familiar programming languages such as Java, Scala, and Python. Second, the performance of the inference logic easily and rapidly scales.

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ML ML Machine Learning Machine Learning 52

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. For this reason, many DJL users also use it for inference only. With v0.21.0

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ML ML Deep Learning Deep Learning 77

phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. Download a free PDF by filling out the form.

Clustering 98

Clustering Database SQL Data Pipeline 98

PyImageSearch

FEBRUARY 5, 2024

Implementing Precision and Recall Calculations in Python Now that we have defined and segregated our samples into True Positives , True Negatives , False Positives , and False Negatives , let us try to use them to compute specific metrics to evaluate our model. label_pred !=1 1 ), and their ground-truth label was positive ( label_gt =1 ).

Database 60

Database Data Pipeline Deep Learning Deep Learning 60

phData

FEBRUARY 24, 2023

When you think of the lifecycle of your data processes, Alteryx and Snowflake play different roles in a data stack. Alteryx provides the low-code intuitive user experience to build and automate data pipelines and analytics engineering transformation, while Snowflake can be part of the source or target data, depending on the situation.

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Analytics Analytics Database Python 52

The MLOps Blog

FEBRUARY 5, 2023

Some industries rely not only on traditional data but also need data from sources such as security logs, IoT sensors, and web applications to provide the best customer experience. For example, before any video streaming services, users had to wait for videos or audio to get downloaded. pip install tensorflow== 2.7.1 !pip

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

PyImageSearch

JUNE 5, 2023

Jump Right To The Downloads Section CycleGAN: Unpaired Image-to-Image Translation (Part 3) In the first tutorial of this series on unpaired image-to-image translation, we introduced the CycleGAN model. Start by accessing this tutorial’s “Downloads” section to retrieve the source code and example images. Let us open the train.py

Deep Learning 56

Deep Learning Deep Learning Data Pipeline Python 56

AWS Machine Learning Blog

AUGUST 21, 2024

Inside this folder, you’ll find the processed data files, which you can browse or download as needed. Access the output data using the AWS SDK Alternatively, you can access the processed data programmatically using the AWS SDK. decode('utf-8') # Initialize a list output_data = [] # Process the JSON data.

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Mlearning.ai

FEBRUARY 6, 2023

When building your Processing Docker image, don't place any data required by your container in these directories. In this example, all our code is inside the src directory FROM python:3.8 In this example, all our code is inside the src directory FROM python:3.8 More on this is discussed later. Below is a sample DOCKER file.

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PyImageSearch

JANUARY 8, 2024

We will understand the dataset and the data pipeline for our application and discuss the salient features of the NSL framework in detail. Finally, in the 4th part of the tutorial series, we will look at our application’s training and inference pipeline and implement these routines using the Keras and TensorFlow libraries.

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Deep Learning Deep Learning Data Pipeline Computer Science 64

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

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ML ML Data Lakes Machine Learning 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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Machine Learning Machine Learning AI AI 52

The MLOps Blog

MARCH 15, 2023

The data pipelines can be scheduled as event-driven or be run at specific intervals the users choose. Below are some pictorial representations of simple ETL operations we used for data transformation. For that, we used another pipeline based on AWS Glue. For more information, please refer to this video.

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AWS ETL ML ML 52

PyImageSearch

JANUARY 29, 2024

Luckily, both TensorFlow and OpenCV are pip-installable: $ pip install tensorflow $ 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 minutes. In this tutorial, we will discuss the model.py

Deep Learning 56

Deep Learning Deep Learning Machine Learning Machine Learning 56

The MLOps Blog

JANUARY 18, 2023

You can download the package using the following code. !pip Updating the data pipeline When updating a training pipeline, it can be helpful to follow a few best practices to ensure a smooth transition and minimize errors and deployment holdups. In order to monitor the model, you can use a platform like neptune.ai.

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Deep Learning Deep Learning ML ML 52

phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. Top 10 Python Scripts for use in Matillion for Snowflake 1. The default value is Python3.

Python 52

Python ETL AWS Database 52

PyImageSearch

JULY 24, 2023

Going Beyond with Keras Core The Power of Keras Core: Expanding Your Deep Learning Horizons Show Me Some Code JAX Harnessing model.fit() Imports and Setup Data Pipeline Build a Custom Model Build the Image Classification Model Train the Model Evaluation Summary References Citation Information What Is Keras Core? Enter Keras Core!

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Deep Learning Deep Learning Machine Learning Machine Learning 86

PyImageSearch

DECEMBER 18, 2023

Libraries like NumPy in Python are capable of tensor manipulations, especially matrix multiplications, which are essential in linear algebra. Keras 3 supports various data pipelines , allowing flexibility in how data is fed into the models. pip install -q tf-nightly # needed for some data processing in keras-cv !pip

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ML ML Deep Learning Deep Learning 67

ODSC - Open Data Science

DECEMBER 9, 2024

The Widespread Adoption of Open DataScience The use of open source data science tools has absolutely explodedwere talking a whopping 650% growth over the past five years. Additionally, a clear majority of current projects ( 85% to be exact) leverage open-source programming languages like Python and R rather than proprietary options.

Data Science 40

Data Science Machine Learning Machine Learning Python 40

AWS Machine Learning Blog

OCTOBER 24, 2024

Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale. Choose Create notebook.

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