How Is Machine Learning Platform Different From AI Platform?





In today’s generation, time plays a major role in the success and failure of your business or your organization. According to data scientists, time is very expensive and only those who can afford it manage other resources around it too well. Machine learning (ML) and artificial intelligence (AI) are important tools that play in the management of the company’s time. Let us learn briefly about the machine learning or ML platform before getting into the details of it.  

What is the ML Platform? 

If one has to describe it broadly, then the machine learning platform is a complex software that streamlines the creation of different machine learning models in the big data environment and also helps the applications that are involved in the processing of big data with the help of machine learning techniques. ML platform goal is to focus on the modeling life cycle, experimentation, deployment, and reproducibility to increase the productivity of the data scientists. 

These ml platforms can either be ready-made or custom-built and are based on operational software. We can classify the ml platforms in the following types, namely: 

  1. Semi-specialized platforms 
  2. Self-hosted studios 
  3. Cloud machine learning IDEs
  4. High-level platforms 

Some of these ml platforms are more flexible than others which includes the time for model development, configuration, and maintenance. Although most of the high-level platforms deal only with the tabular data to keep the maintenance low and save time. 

Read: Is AI Enough Or We Need UX To Guide AI?

Difference Between ML Platforms and AI Platforms? 

The next question that follows is the evident difference between the ML platform and AI platforms. These are the terms of computer science in which the term artificial intelligence refers to the programming of the computer in such a way that it can perform all the tasks that humans can do in a better and a rapid manner. Thereby, it is the inclusion of all the capabilities of a human in the best possible manner in devices like computers, laptops, mobile phones, and other similar gadgets. 

Now coming to the part of machine learning placed against artificial intelligence, the major factor that chalk out the difference is the programming factor. In the ML platforms, the AI programs the devices in such a way that they can automatically learn and improve from their own experience. So there is no need for explicitly programming as every function and process is designed in such a process that the platform can perform tasks by itself.

Related: Is Artificial Intelligence The Future? Possibilities And Expectation

Key Differences Between ML and AI

To simplify the differences further concerning their implications. Starting with the basics, Artificial intelligence is concerned with the acquisition of knowledge and its application whereas machine learning is the acquisition of knowledge and then the skill learned from the knowledge obtained. 

Another key difference is that artificial intelligence aims at increasing the success of the process and the result of the desired task which stimulates natural intelligence to complete the task. On the other hand, machine learning focuses on taking from data available and learning from it. Thus, the main aim of machine learning is to improve and maximize the performance of machines on this task. 

A very famous example of the ai and ml platform can be seen in google. Google ML platform because of artificial intelligence. The ml kit of Google brings machine learning expertise to the developers of mobile in a very easy way with the help of Firebase. The presence of google ai also helps in learning from the educational resources of the machine. 

Best ML Platform you should know about 

To know and learn better through machine learning, here are some of the best data science and ml platforms you can try: 

1. Altair 

The platform Altair which was formerly known as data watch provides an array of solutions with the help of its knowledge works portfolio that headlines with the advanced mining and a predictive analytics workbench called a knowledge studio. With Altair, you have the feature of data preparation tasks, visual data profiling, in-base data analytics, predictive modeling, and many more. 

2. Alteryx 

This ML platform provides data learning and machine learning functionality and automates data preparation, data reporting, data blending, predictive analysis, data science, and changes more than 260 drag and drop building boxes. Alteryx also allows users to see variable relationships and different distributions easily and quickly. There is no requirement for coding and the software can also be deployed in the cloud against the firewall or a hosted environment. 

3. Anaconda 

Another best ML platform that offers the best data science and machine learning capabilities with its different editions is Anaconda and is an open-source and R-focused platform. With this tool, you can perform data science and machine learning via Linux, Windows, and macOS. One can even download 1500 python and R data science, libraries, packages, etc. The results that you have obtained can also be viewed on bokeh, data shader, holoviewer, and other similar platforms.

4. SAP ML Platform 

The SAP ml platform is the open software and infrastructure platform which serves as a service system. Therefore, SAP is very helpful for businesses to inject artificial intelligence and machine learning into their platform and facilitate machine learning. This helps the consumers to consume the AI functionality and facilitate machine learning through the applications provided. The AI and ML technology is offered directly to the consumers by the SAP platform and exposes underlying services and solutions through these platforms. 

5. AWS ML Platform 

Innovation plays an important role in enhancing the user experience and improving productivity. AWS ML platform offers these services both in Machine learning and artificial intelligence. One is able to train, build, deploy the ML models along with the Amazon Sagemaker and also provides tools for ML development life cycle in one united environment. 

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