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NumPy VS Kevel

Compare NumPy VS Kevel and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Kevel logo Kevel

Kevel's APIs make it easy for engineers and PMs to quickly launch a fully-customized, white-labeled, server-side ad server.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Kevel Landing page
    Landing page //
    2023-07-27

Kevel's APIs make it easy for engineers and PMs to build their own server-side, fully-customized ad server. Top e-retailers and user communities use Kevel to build innovative ad servers to promote anything from native ads to internal content to sponsored listings.

Engineers reliably see a 90%+ reduction in dev time using Kevel's APIs versus doing it entirely from scratch. Kevel's customer list includes Fortune 500 brands, public companies, and unicorn startups, including Klarna, Yelp, Edmunds, Bed Bath & Beyond, Ticketmaster, Wattpad, imgur, Strava, and many more.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Kevel features and specs

  • Customizable Ad Infrastructure
    Kevel allows businesses to build and control their ad serving platforms, giving them the flexibility to design a system tailored to their specific needs.
  • API-First Approach
    Kevel provides robust APIs that make it easy to integrate with existing systems and applications, facilitating seamless ad management and delivery.
  • Rapid Implementation
    With Kevel, businesses can quickly set up and start serving ads, thanks to its user-friendly interface and comprehensive developer documentation.
  • Scalability
    Kevel handles high traffic volumes and can scale with the growing needs of a business, ensuring consistent performance without disruption.
  • Advanced Targeting and Reporting
    Kevel offers sophisticated targeting options and detailed reporting, enabling businesses to optimize their ad campaigns for better ROI.

Possible disadvantages of Kevel

  • Cost
    Kevel's advanced features and customization capabilities come at a higher price point, which might not be suitable for smaller businesses or startups with limited budgets.
  • Complexity for Non-Technical Users
    The platform's rich feature set and API-driven design can be overwhelming for users without technical expertise, potentially requiring additional training or support.
  • Integration Challenges
    While Kevel offers strong API support, integrating it into a complex, existing infrastructure may still pose challenges and require dedicated development resources.
  • Limited Ready-Made Solutions
    Unlike some other ad-serving platforms, Kevel focuses on customizability over ready-made solutions, which means businesses need to invest time in building and fine-tuning their ad infrastructure.
  • Dependence on Technical Resources
    The high degree of customization and flexibility may necessitate ongoing support from developers or engineers, which could be a limitation for companies with constrained technical resources.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Kevel

Overall verdict

  • Overall, Kevel is a solid choice for businesses seeking flexibility and control over their advertising technology stack. It's particularly effective for companies that need custom solutions rather than one-size-fits-all ad products.

Why this product is good

  • Kevel is recognized for its comprehensive suite of APIs that enable companies to quickly and easily build custom ad platforms. The platform provides robust support for ad serving, user targeting, and analytics, making it a suitable choice for businesses looking to implement personalized advertising experiences. Moreover, it emphasizes privacy and compliance, which is increasingly important in today's digital landscape.

Recommended for

  • Companies developing custom ad platforms
  • Businesses prioritizing privacy and compliance in advertising
  • Organizations requiring robust API support for ad serving
  • Teams that prefer flexible ad solutions over packaged products

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Kevel videos

Overview of Kevel

Category Popularity

0-100% (relative to NumPy and Kevel)
Data Science And Machine Learning
Advertising
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Ad Networks
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Kevel

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Kevel Reviews

A Beginner’s Guide to Ad Servers (Plus: 8 Ad Servers Reviewed)
Adzerk is a suite of APIs that make it easy for engineers and PMs to design, build, and launch a fully-customized, server-side ad server. Sold as an infrastructure-as-a-service for enterprises, plans start in the $3K-$5K/month range and scale based on needed features and monthly request volume.
Best Ad Serving Platforms For 2018: Third Party Technology Companies (Free Options Included In List)
AdZerk offers members of the ad industry a unique and custom solution unlike any other. With the AdZerk API, users can build custom online ad serving platforms to cater to their own specific needs.

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

Kevel mentions (0)

We have not tracked any mentions of Kevel yet. Tracking of Kevel recommendations started around Mar 2021.

What are some alternatives?

When comparing NumPy and Kevel, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

AdSense - Earn money with website monetization from Google AdSense. We'll optimize your ad sizes to give them more chance to be seen and clicked.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Google Ad Manager - Grow revenue wherever your users are with an integrated ad management platform that surfaces insights for smarter business decisions.

OpenCV - OpenCV is the world's biggest computer vision library

Epom - An ad serving solution for publishers, advertisers, ad and affiliate networks