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Unleashing the Power of Private Clouds: Comparing the Top Providers and Their Offerings
In the world of technology, the private cloud has become a game-changer for businesses looking to streamline their operations, increase efficiency, and protect sensitive data. With so many private cloud providers to choose from, it can be difficult to know which one is right for your organization. To help make the decision easier, we’re going to compare four of the top private cloud providers: SAP, OVH, OpenShift, and DataFortress.
Each of these providers has its own unique offerings, strengths, and weaknesses. By comparing them side by side, we’ll help you make an informed decision about which private cloud provider is best suited to your needs. Whether you’re looking for robust security features, scalability, or seamless integration with your existing infrastructure, we’ve got you covered.
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Going from Wordpress to serverless, unhackable high-speed static websites
I have been building websites in the past, but have always been struggling with the slow performance of WordPress. If it is loaded with plugins, it will need quite some resources and can be a pain if you are just developing an idea on a small server.
Additionally, safety is of concern as well, and as a system that 33,6% of websites are using , it is quite attractive for hackers to find exploits and other issues in it. But again, as it is massively popular, there is almost every time a plugin for the issues you are having, which makes it easy to use, and a great “all-in-one” tool.
Read MoreFace Detection using MTCNN
MTCNN is a python (pip) library written by Github user ipacz , which implements the [paper Zhang, Kaipeng et al. “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks.” IEEE Signal Processing Letters 23.10 (2016): 1499–1503. Crossref. Web](https://arxiv.org/abs/1604.02878%5D(https://arxiv.org/abs/1604.02878%20%22https://arxiv.org/abs/1604.02878) .
In this paper, they propose a deep cascaded multi-task framework using different features of “sub-models” to each boost their correlating strengths.
MTCNN performs quite fast on a CPU, even though S3FD is still quicker running on a GPU – but that is a topic for another post.
Read MoreHow to deploy an automated trading bot using the Facebook Prophet Machine Learning model to AWS Lambda (serverless)
I divided this post into the “Why did I do it” and the “Technical How To”. If you want to skip the “Why” part, feel free to directly jump to the Technical part.
1. Reliability: The algorithm will execute independently of other systems, updates, …
2. Performance Efficiency: I can run several algorithms on one (small) system, independent from each other.
3. Cost Savings: AWS allows for 3,2 million compute-seconds per month, basically letting me run all my algorithms for free.
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Docker Compose vs. Kubernetes vs. Traditional Hosting: What's the Best Way to Host Your Application?
Docker: The Lightweight, Portable Option for Hosting Applications
Docker: Leightweight, proof-of-concept, running multiple programs on one server
Docker has become a popular tool for application development and deployment in recent years. Its popularity can be attributed to its lightweight, portable nature, making it an excellent choice for hosting applications. Unlike traditional virtual machines, Docker containers do not require a complete operating system to be installed, resulting in a much smaller footprint. This feature also enables the use of the same container across different environments, making it easy to move from development to production. Additionally, Docker containers are easily scalable, allowing you to add or remove resources as needed. If you’re looking for a cost-effective and efficient way to host your applications, Docker might be the perfect solution.
Read MoreEnhancing stock data for your Python Algorithmic Trading Model
Let us say you are planning to build your own algorithmic trading model.
You will most likely use only price (Close) data for your model and algorithm, but soon you will discover, that your model does not perform well.
Soon you will use typical OHLCV data, which refers to Open, High, Low, Close, Volume, which is already better, but the model does not seem to perform quite well enough.
What can you do?
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