Platforms that Help Deploy AI and ML Applications on the Cloud

Artificial intelligence and machine learning are influencing almost every industry today. This article highlights the different ways in which these are used in our daily lives and how some open source cloud platforms enable their use.

The goal of artificial intelligence (AI) is to construct machines and automated systems capable of mimicking human perception. On a global level, AI is changing societies, politics and the economy in many different ways. Examples of AI applications are Google Help, Siri, Alexa and self-driving cars like Tesla.

Today, AI is used in a variety of industries to effectively solve difficult problems. It is used in the healthcare industry to make more accurate and faster diagnoses than humans. Doctors can use AI to diagnose a disease and receive an alert when a patient’s condition worsens.

Data security is vital for every business and the number of cyber attacks is constantly increasing. With the help of artificial intelligence, the security of data can be improved. An example of this is the integration of intelligent bots to detect software errors and cyber attacks.

Twitter, WhatsApp, Facebook and Snapchat are just some of the social media platforms that use AI algorithms to store and manage billions of profiles. AI can organize and sift through massive amounts of data to find the latest trends, hashtags, and needs of different people.

Key Applications of Machine Learning
Figure 1: Key applications of machine learning

The tourism industry is increasingly reliant on AI as the latter can help with a variety of travel-related tasks, including booking hotels, flights and best consumer itineraries. Chatbots controlled by artificial intelligence are used in the travel industry for better and faster customer service.

Table 1: Machine learning tools and frameworks

tool/platform URL
power lit
learn scikit
Apache Spark
hug face
Apache Mahout

Machine learning in different domains

Machine learning (ML) refers to all techniques and tools that allow software applications and gadgets to react and develop independently. AI can learn without really being explicitly programmed to perform the required action thanks to machine learning techniques. Rather than relying on predefined computer instructions, the ML algorithm learns a pattern from example inputs and then anticipates and executes tasks entirely based on the learned pattern. When rigorous algorithms aren’t an option, machine learning can be a lifesaver. It will take up the new procedure by analyzing previous ones and then putting them into practice. ML has paved the way for technical advances and technologies previously unthinkable across a variety of industries. It is used today in a variety of cutting-edge technologies – from predictive algorithms to Internet TV live streaming.

A notable ML and AI technique is image recognition, a method of categorizing and identifying a feature or element in a digital image. Classification and face recognition are performed using this method.

Streamlit Cloud for machine learning
Figure 2: Streamlit machine learning cloud

The use of machine learning for recommendation systems is one of the most widespread and well-known applications. In today’s e-commerce world, product recommendation is a formidable tool that leverages powerful machine learning techniques. Websites use AI and ML to track past purchases, search trends, and cart history, and then generate product recommendations based on that data.

There is a lot of interest in the healthcare industry in using machine learning algorithms. An ML algorithm can be used to predict ED wait times across multiple hospital departments. Details of staff shifts, patient records, and records of department calls and ER layouts are all used to create the algorithm. Machine learning algorithms can be used to detect a disease, plan treatments, and forecast.

Main characteristics of the cloud platforms used for machine learning
  • Algorithms or feature extraction
  • Association Rule Mining
  • Big data-based predictive analytics
  • Classification, regression and clustering
  • Loading and transforming data
  • Data preparation, data pre-processing and visualization
  • dimensional reduction
  • Distributed linear algebra
  • Hypothesis testing and core methods
  • Processing of image, audio, signal and image data sets
  • Model selection and optimization module
  • Preprocessing and data flow programming
  • recommendation systems
  • Support for text mining and image mining through plugins
  • visualization and plotting

Cloud-based delivery of AI and ML applications

AI and ML applications can be deployed on cloud platforms. A number of cloud service providers today allow programmers to create models for effective decision-making in their field.

These cloud-based platforms are integrated with pre-trained machine learning and deep learning models on which the applications can be deployed with no coding or minimal scripting.

Categories of ML deployments in Streamlit
Figure 3 Categories of ML deployments in Streamlit

Streamlite: Streamlit gives data scientists and ML professionals access to various machine learning models. It is open source and compatible with cloud deployments. The ML models can be prepared for use with datasets in moments.

Streamlit offers a range of machine learning and source code models in multiple categories including natural language processing, geography, education, computer vision, etc.

Streamlit offers a range of machine learning and source code models in multiple categories including natural language processing, geography, education, computer vision, etc.

Hugging Face for Machine Learning
Figure 4: Machine learning hugging face

Hugging Face: This is another platform with pre-trained models and architectures for ML and AI across a range of categories. Many corporate giants use this platform, including Facebook AI, Microsoft, Google AI, Amazon Web Services, and Grammarly.

A number of pre-trained and ready-to-use models are available in Hugging Face for various applications, including natural language processing and computer vision.

The following tasks can be performed with the ML models in Hugging Face:

  • audio-to-audio processing
  • Automatic voice recognition
  • computer vision
  • fill mask
  • image classification
  • image segmentation
  • object detection
  • answering questions
  • sentence similarity
  • summary
  • text classification
  • text generation
  • Text-to-speech translation
  • Token Classification
  • translation classification

The problem solvers available in Hugging Face are streamlined and effective, aiding in the rapid deployment of models (Figure 5).

Problem solvers and models in Hugging Face
Figure 5: Problem solvers and models in Hugging Face

These cloud-based platforms are useful for researchers, practitioners, and data scientists across multiple fields, simplifying the development of real-world applications that perform well.