AI refers to a system that solves tasks that complex decision making. It basically mimics the human intelligence.

On the other hand, machine learning is a subset of AI and refers to an AI system that can self-learn using an algorithm and lots of data to make accurate predictions.

• AI in healthcare:
  • It can help doctors by precise and quick diagnosis of diseases using patient samples, medical history etc.
  • AI can help vaccine R&D teams in quickly rolling out new effective vaccines.

• AI in banking and finance:
  • It can analyse large volumes of data, detect fraud, and can perform predictive tasks too.
  • AI-powered apps can also offer financial advices and guidance based on a customer’s spending pattern.

• AI in insurance:
  • AI can help both insurers and insured by predicting the most appropriate premiums based on risk factors and history of insured.
  • AI can help by detecting fraud insurance claims or adherence issue.

Read our blog that extensively talks about use cases of AI industry wise

1. ML: Machine Learning focuses on the use of data and algorithms to mimic the way humans learn, thus improving the accuracy with time.
2. NLP: It stands for natural language processing, known for the combining computational linguistics, rule-based modelling of human language with machine learning, statistical, and deep learning models.
3. Deep learning: It is a subset of machine learning where neural networks, algorithms based on the human brain learn from huge amount of data. A deep learning algorithm can perform a task several times each time modifying a little for better outcomes.
4. Computer vision: A field of computer science that focuses on developing digital systems that can be used to process, analyse, and make sense of visual data like humans do. Machines retrieve the visual information, handles it, and then interprets the results via special software algorithms.

Check out our article that explains the AI technologies in detail.

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

An AI development team comprises of domain experts, data scientists, data engineers, product designers, data modelling experts, AI/ML solution architect and software engineers.

Before you start AI development project, check out the prerequisites given below:

Do you have the labelled data?
Do you a strong data pipeline to assist model training?
Have you selected the right model?
Now, let’s focus on the steps involved in an AI development project:

Data acquisition: It involves data collection, data pipeline creation, data validation and data exploration.
Model development: It involves feature engineering, training and evaluation.
Deployment: It involves integration, testing and validation.
Monitoring: Keep a watch on how AI models perform in production.

If you are interested in knowing in detail about the prerequisites and AI implementation, read our blog on all you need to know about AI implementation

For an AI project to go live, it can take from few months to a year, totally depending on the scope and complexity of the AI project. It is advised not to underestimate the time it takes to prepare the data before a data science engineer builds an AI algorithm.

Unclear goals and KPIs
Failing to adopt AI early leading to tech issues during implementation.
Developing isolated POCs that fails to work in production environment.
Insufficient data to build data pipelines
Insufficient skills and experience.