CloudStakes Technology

  In the past few years, we have seen the advancement of developer tools and platforms for technologies like machine learning and artificial intelligence in industries, including banking, finance, healthcare, and more. Also, AI developers have tried several techniques, from utilizing cognitive cloud-based APIs, framework libraries to trained models only to integrate AI capabilities into applications. Developers (i.e., AI engineers and researchers), in the end, have decided to choose a suitable machine learning framework in order to train their ML models.        The reason behind choosing the ML framework is to make the development process of machine learning models faster and smoother. These tools minimize developers' ML deployment efforts and reduce overall ML model development time. However, organizations can design ML framework according to their needs. In most cases, they choose to use the existing one that matches their demands.    Thi...

Today businesses’ data is growing exponentially and to manage and utilize such data requires a smart solution, like AI (Artificial Intelligence). Well, there are two approaches available to build AI-related projects either Machine Learning or Deep Learning. Machine learning and deep learning terms contain different computation capabilities and that’s why many times used interchangeably. Even though they have different capabilities there is slight confusion between developers to decide which one is the more viable option to choose for the project implementation. This blog contains clear differentiation between  deep learning and machine learning concepts . Before we make a verdict let’s first understand both Machine Learning and Deep Learning terms in detail. What is Machine Learning? Machine Learning is derived from AI that depends upon  statistical learning algorithm s. The main work of machine learning systems is to analyze and predict the data as per their continuous data b...