AI states to a system that tackles complex decision-making tasks, effectively mirroring human intelligence. In contrast, machine learning is a subdivision of AI and belongs to an AI system that can autonomously learn using algorithms and massive amounts of data to make accurate predictions.

AI in healthcare:

• It can aid doctors in making accurate and swift diagnoses of diseases using patient samples and medical history, among other data.
• AI can assist vaccine research and development teams in rapidly creating successful new vaccines.

AI in banking and finance:

• It can analyze huge volumes of data, detect fraud, and run predictive tasks.
• AI-powered apps can also provide financial recommendations and assistance based on a customer’s expenditure patterns.

AI in insurance:

• AI can benefit both insurers and the insured by forecasting the most appropriate premiums based on risk factors and the insured’s history.
• AI can help detect fraudulent insurance claims or adherence problems.

ML: Machine Learning aims to use data and algorithms to mirror human learning processes, thus improving accuracy over time.

NLP: Natural Language Processing combines computational linguistics, rule-based modelling of human language, and machine learning, statistical, and deep learning models.

Deep learning: This is a subclass of machine learning where neural networks, algorithms inspired by the human brain, learn from vast datasets. Deep learning algorithms refine their performance through multiple iterations.

Computer vision: A field of computer science that focuses on maturing digital systems capable of processing, analyzing, and making sense of visual data similar to how humans do. Machines acquire visual information, process it, and figure out the results using specialized software algorithms.

Scikit Learn, TensorFlow, Theano, Caffe, MxNet, Keras, PyTorch, CNTK, Auto ML, OpenNN, H20: Open Source AI Platform, Google ML Kit

An AI Engineering team consists of data engineers, data scientists, domain experts, product designers, AI/ML solution architects, data modelling experts, and software engineers.

Before commencing an AI Engineering project, consider the following prerequisites:

• Do you have categorized data?
• Do you possess a robust data pipeline to support model training?
• Have you picked the right model?

Now, let’s dig into the steps of an AI Engineering project:

• Data acquisition: This involves data collection, creating a data pipeline, data validation, and data exploration.
• Model development: This encompasses feature engineering, training, and evaluation.
• Deployment: This stage includes integration, testing, and validation.
• Monitoring: Continuously observe the performance of AI models in a production environment.

The timeframe for an AI project to become functional can range from a few months to a year, depending totally on the project’s scope and complication. It’s important not to underestimate the time involved in organising the data before a data science engineer creates an AI algorithm.

• Uncertain goals and key performance indicators (KPIs).
• Failure to adopt AI early, resulting in technical concerns during execution.
• Creating isolated proof of concepts (POCs) that do not function in a live environment.
• Inadequate data for creating data pipelines.
• Insufficient skills and expertise.