Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. AI combines computer science and robust datasets to enable problem-solving. AI developers take the models that data scientists create and make them into deployable models that can be used in applications.
AI and the cloud
Integrating AI and machine learning technologies with cloud environments is an increasingly common scenario, driven by use of microservices and the need to scale rapidly. Developers are faced with the challenge to not only build machine learning applications, but to ensure that they run well in production in cloud-native and hybrid cloud environments.
When developing AI-powered services and applications that run in cloud environments, there is a vast array of development areas to consider including:
- Machine learning libraries
- Datasets
- Data exchanges
- Deep learning libraries
| GitHub repo | Governing body | Get Started guide |
|---|
| Keras | | TBD | TBD |
| IBM Cloud | GCP | AWS | Azure |
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| Machine Learning Frameworks | TBD | TBD | TBD | TBD |
| Datasets | 1.18 - 1.21 | TBD | TBD | TBD |
| Deep learning libraries | TBD | TBD | | |