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Software Platforms for Artificial Intelligence

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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.

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.

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.


- The Layers of AI Platforms

AI platforms have layers that allow organizations to deploy machine learning models from a variety of frameworks, languages, platforms, and tools. These layers can be divided into three categories:

  • The data and integration layer: it allows easy access to data from various systems so AI algorithms can be trained. Data should be of good quality so that AI scientists can build data streams without spending time improving data quality. Data management tools provide similar functionality.
  • The experimental layer: it enables data scientists to generate and validate hypotheses. A good experimentation layer automates processes such as feature engineering, feature selection, model selection, model optimization, and model interpretability. AutoML tools also provide similar functionality.
  • The operations and deployment layer: it is where model risk assessments are managed so that the model governance team or compliance team can validate the model. This layer also provides tools for controlling the deployment of models across the enterprise. For example, AI platforms can deploy and scale machine learning models across multiple infrastructure providers. This frees machine learning engineers from having to deal with the details of deploying models on different infrastructures to serve different enterprise applications.


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[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 machine learning 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 Machine Learning

Machine learning and AI, as a unit, are still developing but are rapidly growing in usage due to the need for automation. Artificial Intelligence 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.



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