Research indicates that organizations have a hard time productizing machine learning models. AI platforms help businesses build, manage and deploy machine learning and deep learning models at scale. It makes AI technology more attainable and affordable by reducing software development work such as data management and deployment.
What is an AI platform?
An AI platform is a set of services that support the machine learning life cycle. This includes support for gathering and preparing data as well as training, testing, and deploying machine learning models for applications at scale.
How does it work?
Data and Integration layer allows easy access to data from various systems so AI algorithms can be trained. The data should be in good quality so that the AI scientists are able to build the data flows without spending time on data quality improvement. Data management tools provide similar functionality.
Experimentation layer enables Data Scientists to generate and verify a hypothesis. 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.
Operations and Deployment layer is where the model risk assessment is managed so that the model governance team or compliance team can verify the model. This layer also offers tools for controlling the deployment of models across the enterprise. For example, AI platforms can deploy and scale machine learning models on multiple infrastructure providers. This saves machine learning engineers from dealing with the details of deploying their model on different infrastructures to serve different enterprise applications.
Why is it important now?
With the rise of citizen data scientist, accessibility of AI and analytics tools are important. AI platforms are helpful tools to democratize and productize ML models by providing tools for managing the end-to-end machine learning life cycle. They achieve this through a SaaS interface designed to simplify user interactions for less-specialized technical personnel. Without these platforms, the influence of AI technology would be limited since a higher share of resources would be spent on building and maintaining models.
Gartner’s survey highlights that productizing ML models is one of the most important barriers towards delivering business value from machine learning:
What are its use cases?
AI platforms can be used in every situation where machine learning is involved.
What are alternative ML deployment options?
Internal bespoke development
What it is: Developing a machine learning program with internal resources.
Technical Requirements: High. It requires coding and mathematical/statistical expertise for building machine learning models. Due to high technical requirements, only the largest technology companies or well-funded AI-focused startups can effectively build a machine learning application with internal resources.
Outsourced bespoke development
What it is: Hiring an AI specialist or an AI company to handle the development
Technical Requirements: Comparably lower than internal bespoke development. It requires skills of problem framing, value-adding solution finding, data management and integration with business processes.
What are the major AI platform solution providers?
- 5 Analytics
We have also prepared a sortable, prioritized list of AI platforms including leading software that supports various steps in the machine learning life cycle.
AI platforms can help organizational transformation. If you wonder how you can apply AI Transformation within your organization, we recommend this article.
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