Over the last few years, there has been tremendous excitement around and interest in the potential for machine learning (ML) technologies applied to Artificial Intelligence (AI). Tech giants spent huge billions on AI with technology and finance leading the way. Such investment has enabled numerous consumer services such as Alexa and Siri to become mainstream.
Despite those successes, the development and deployment of AI applications remain expensive in terms of time and the need for specialized talents. The lack of any common frameworks for development has kept AI applications from becoming mainstream as a general solution to IT problems.
KEY CHALLENGES BEFORE IMPLEMENTING MACHINE LEARNING MODELS
- FEATURE ENGINEERING
- NON-LINEARITY
- VARIANCE BIAS
- DIMENSIONALITY
- MODULARITY
- PROCESSING PERFORMANCE
- CLASS IMBALANCE
5 Best Open Source Frameworks For Machine Learning Model Hosting
Acumos
Acumos AI is a platform and open-source framework that makes it easy to build, share, and deploy AI applications. Acumos AI standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment.
This frees data scientists and model trainers to focus on their core competencies and accelerate innovation.
RAPIDS
The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
TURI CREATE
Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
- Easy-to-use: Focus on tasks instead of algorithms
- Visual: Built-in, streaming visualizations to explore your data
- Flexible: Supports text, images, audio, video and sensor data
- Fast and Scalable: Work with large datasets on a single machine
- Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps
Streamlit
Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful data apps in hours, not weeks. All in pure Python.
RAY
Ray is a distributed execution framework that makes it easy to scale your applications and to leverage state of the art machine learning libraries.
