Automated machine learning, or AutoML, has generated plenty of excitement as a pathway to “democratizing data science.”
The goal is straightforward enough: By embracing a new AI mindset and automating key elements of algorithm design, AutoML can make machine learning more accessible to users of various stripes, including individuals, small startups, and large enterprises.
By definition, fully manual deep learning processes rely on the skillsets of data scientists and other specialists to carry out key processes, including programming neural networks, handling data, conducting architecture searches, and so on.
While manually implementing DL from scratch poses many obstacles, the accumulated work of DL programmers and data scientists has led to the creation of high-level frameworks like Caffe, TensorFlow, and PyTorch, which provide DL models and pipelines for users to write their own networks and more.
This level of automation builds upon the availability of trained models like open-source repositories and labeled data, which are then fine-tuned to solve a given problem.
When the deep learning pipeline is fully automated, meta-models will set the parameters needed for a given task.
Although full automation is still several years off, working toward meta-models will deliver vital gains in efficiency and sophistication even at the lower levels of autonomy (much as innovators working to ultimately develop self-driving cars have already rolled out improvements to automotive technology).
- The Automation-Human Balance Takes Shape in Security
- 3 Tactics to Accelerate a Digital Transformation
- Putting Production on Repeat with Machine Tool Automation
- AI in manufacturing: Optimizing costs and enabling the workforce
- RPA: Why you need to care about this totally unsexy technology
- Buildings IOT Implements Smart Building Management System for Thor Equities’ 800 Fulton Market Development in Chicago
- Artificial Intelligence (AI) in Energy