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Mexa Wallet

November 16, 2020

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Happy New Year'2024

What are the basics of building the microservices for our business?

Basics of APIs: APIs proved the way for businesses to integrate with other systems. The businesses began building and leveraging APIs to allow the users to share and embed content into streams of social media interactions. Many Web and mobile apps that we use today are built on top of other APIs. APIs will perform and return data correctly, quickly, securely, and reliably. API is a set of programming instructions and standards for accessing web-based software applications or services. The developers use APIs to get instructions to interact with services and connect data between different systems. It describes the available functionality of a service, how it will be used and accessed, and what format to access for inputs and outputs. The businesses and applications are built on open APIs to pass data back and forth between systems. The APIs take a set of instructions ( a request) from an application, that request to the database, fetches the data, or facilitates some actions, an

What are the basics of building machine learning models for our clients?

Process of Building End to End Machine Learning PipeLine: In the Research environment, the data scientist investigates and develops the model that generates the business value. The typical ML pipeline involves gathering data from different data sources. It can be described as, In data analysis: We understand the data, the variables to use, how they have related to each other, regulations in the business, what we want to predict etc., We transform these data into various forms like creating or extracting the new feature. In Feature Engineering: We make the variables are ready for the ML model. There are a variety of problems in our data set. It involves missing values, categorical variables, distribution, outliers etc., The missing data is the absence of values for certain observations within a variable. It affects all ML models. So, we need to prepare some sort of number to use ML. The second problem is categorical variables may have labels. This problem has 3 types, * Car

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