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Software Platforms for Artificial Intelligence

ML Life-Cycle_112122A
Machine Leaning Life-Cycle - Japapoint]

- Overview of AI Technology

Artificial intelligence (AI) is a way for computers to perform tasks that normally require human intelligence. It is the development and use of algorithms, programs and systems capable of self-learning, reasoning and correction.

Scientists develop AI by studying how the human brain works and by simulating cognitive processes (i.e. thinking). You can usually consider AI if it can replicate three basic skills: 

  • Learning: AI recognizes patterns or relationships in data. For example, this could mean gaining the ability to distinguish objects in pictures from their backgrounds. 
  • Reasoning: AI makes informed decisions or predictions based on data. This is how a chatbot answers a question or your email inbox filters potential spam. 
  • Self-correction: AI continues to improve its performance over time, remembering mistakes or bad outcomes to avoid in future iterations. Practice makes perfect!


Not all AI is created equal. Some machines are better at performing these skills than others, and they could, in theory, one day become sophisticated enough to perform any intellectual task a human can. Such AI is known as artificial general intelligence (AGI).

In practice, AI is used as an umbrella term covering a wide range of different technologies and applications: e.g. machine learning, deep learning, natural language processing (NLP), computer vision (CV), robotic process automation (RPA), etc.


- AI and AI Platforms

The term artificial intelligence (AI) broadly refers to the application of technology to perform tasks similar to human cognitive functions, and is usually defined as "the ability of a machine to imitate the behavior of human intelligence". 

AI generally involves the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

An AI platform is a software solution that helps enterprises develop and deploy AI applications. They typically include tools and services for data scientists, developers, and business users.

Use the AI ​​platform to train your machine learning models at scale, host your trained models in the cloud, and use your models to make predictions on new data.

AI platforms help businesses build, manage, and deploy machine learning and deep learning models at scale. It makes AI technology more accessible and affordable by reducing software development efforts such as data management and deployment.


- Some AI Platforms

An AI platform is a set of services that support the machine learning lifecycle. This includes support for collecting and preparing data, as well as training, testing, and deploying machine learning models for large-scale applications.

Here are some AI platforms: 

  • IBM Watson
  • Google AI Platform
  • DataRobot
  • Microsoft Azure
  • TensorFlow
  • OpenAI
  • Amazon Web Services (AWS)
  • NVIDIA Deep Learning AI Software 


Top 25 Algorithms_010321A
[Top 25 Algorithms]

- Putting the Machine Learning Pieces Together

Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don’t quite know how to put them together. 

Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. 

The following will introduce a complete ML solution, which contains a real-world process that allows you to understand how all the parts are put together. 

  • Data cleaning and formatting
  • Exploratory data analysis
  • Feature engineering and selection
  • Compare several machine learning models on a performance metric
  • Perform hyperparameter tuning on the best model
  • Evaluate the best model on the testing set
  • Interpret the model results 
  • Draw conclusions and document work 


- Python is Most Suitable for AI and ML

Does Machine Learning (ML) require coding? Yes, ML requires a programming language. First, understanding ML involves algorithms. Mathematics is a required skill for learning the concept of algorithms. But when you're implementing ML to solve real-world problems, you do need to code. Python and R are the programming languages of choice in AI and data science.

ML and AI, as a unit, are still developing but are rapidly growing in usage due to the need for automation. AI makes it possible to create innovative solutions to common problems, such as fraud detection, personal assistants, spam filters, search engines, and recommendations systems.  

The demand for smart solutions to real-world problems necessitates the need to develop AI further in order to automate tasks that are tedious to program without AI. 

Python programming language is considered the best algorithm to help automate such tasks, and it offers greater simplicity and consistency than other programming languages. 

Further, the presence of an engaging python community makes it easy for developers to discuss projects and contribute ideas on how to enhance their code.


[More to come ...]



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