Earn money online

  Introduction - In the ever-evolving landscape of data science and machine learning, harnessing the power of cloud platforms has become essential. Azure Machine Learning (ML) is a Microsoft's cloud based machine learning service which provides a robust environment for developing, deploying, and managing machine learning models. If you are working with R and have developed a custom package then integrating it into Azure Machine Learning can streamline your workflow and enhance collaboration. In this article we will walk through the process of uploading an R package to Azure Machine Learning, that will enable you to leverage the platform's scalable computing resources and collaborative features. Concepts - Following terminologies are crucial for uploading package into Azure ML. So, let's first understand the key concepts related to uploading an R package to Azure ML: Azure Machine Learning Workspace: It is the central hub for your machine learning operations in Azure which ...

  What is an API Gateway? An API Gateway is critical component of modern design, particularly in microservices-based applications, because it provides a layer of abstraction that facilitates client-underlying service interaction. It improves the maintainability of distributed systems, scalability and security. How to create it? Summarizing all the steps we will be taking for creating API Gateway: Step 1 : Install Dependencies Step 2 : Request parsing Step 3 : Authenticate the request Step 4 : Configuration file Step 5 : Resolve the request Step 6 : Request forwarding Step 7 : Deliver response In this article we will see how to build our own simple API gateway from scratch. Basically API gateway is just a centralized place for requests to be made to your APIs, so instead of consumers or users making requests directly to your APIs, they make it to API gateway and the gateway job is to handle those requests and send them to the correct APIs and get the responses back to the sender. S...

  SARIMA - SARIMA stands for Seasonal Auto Regressive Integrated Moving Average. This model is useful for forecasting time series data. In this tutorial we will learn how we can use it for forecasting with the help of example. The classic ARIMA accepts the parameters (p,d,q), while SARIMA or Seasonal ARIMA accepts an additional set of parameters (P,D,Q)m that specifically describe the seasonal components of the model. Here P, D, and Q terms represent the seasonal regression, differencing, and moving average coefficients, and m represents the number of data points that is rows in each seasonal cycle. So if you have monthly data with a yearly seasonal cycle, m would be equal to 12. When we actually import it within statsmodels, the implementation of seasonal ARIMA is called SARIMAX. The "X" added to the name represents that the function also supports exogenous regressor variables. Importing SARIMAX means we are just performing classic ARIMA with a seasonal ARIMA model. Example ...