Software Platforms for Big Data, AI/ML/DL, Neural Networks, and Computer Vision

- AI Platforms

Research shows that it is difficult for organizations to commercialize machine learning models. 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.

[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.

[More to come ...]