Rapid and cost-effective development of AI solutions: The three steps of machine learning projects.
Use pre-trained models where possible to immediately benefit from the opportunities of AI. Creating conversational interfaces like chatbots, making videos searchable, classifying images, converting audio to text, extracting information and sentiment from text, and translating text are all easy. For other, more specific data needs, Google's Tensorflow can be used to generate tailored models.
The three steps of machine learning projects
Large amounts of data is needed for deep learning.
Every organization should have a strategy for collecting data already before planning the next steps.
A neural network is constructed based on the data available, and the output needed. The data is used to train the network. Code created here is only a tool to create and train the network - it is not used in the third step. This may take a while...
But after that, the trained model can be used on many platforms, again and again. It can be deployed as an API at the back end, or as a front end component for web apps and mobile apps. Front end usage enables almost real-time running of the model.
Classify, detect, predict, recommend, automate, create
Business benefits for can be found in using machine learning to predict probable values based on new inputs.
This capability to predict probable values for new, unknown input values can be used to classify text or images. A model can detect anomalities in transactions or log events. It can predict future values and recommend new options for the users, based on the previous selections.
Machine learning is a subset of artificial intelligence, AI. Expanding the learning capabilities enables automating tasks. Business logic or decisions can be automated. A feedback loop enables further unsupervised learning.
It also enables creating new ideas. AI-assisted systems can already create new space layout alternatives for buildings, generate images, and create text.