A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier. It is a tool that allows enterprises to scale their machine learning efforts securely while maintaining a healthy Machine Learning lifecycle. These frameworks have become standard practices in recent years, but with these frameworks being more standardised, businesses are not sure what they are to use. Some of the key features of good ML frameworks are:
- Optimised for performance
- Developer friendly — The framework utilises traditional ways of building models.
- Is easy to understand and code on
- Is not completely a black box
- Provide parallelization to distribute the computation process
When it comes to choosing the right framework, you need to figure out the needs of your business. When you start your search for a machine learning framework, ask these three questions:
- Will you use the framework for deep learning or classic machine learning algorithms?
- What is your preferred programming language for artificial intelligence (AI) model development?
- What hardware, software, and cloud services are used for scaling?
Each machine learning framework has different algorithms that use different methods to analyse training data and apply what they learn to new examples. These algorithms have parameters that can be adjusted and tweaked that control how the algorithm operates and when choosing a machine learning framework, it is important to consider whether this adjustment should be automatic or manual.
When it comes to the training phase of AI algorithm development, scalability is the amount of data that can be analysed and the speed of analysis. In the deployment phase of an AI project, scalability is related to the number of concurrent users or applications that can access the model simultaneously. When choosing a framework, it is important to consider whether it supports both types of scalability, and see if it supports your planned development and production environments
- Amazon Machine Learning: This is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. It also provides visualisation tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Eden AI ML Engineers deploy ML models using AWS services like AML.
- Apache Mahout: This is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification.
- Apache MXNet: This is a modern open-source deep learning framework used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by major Public Cloud providers including AWS and Azure Amazon has chosen MXNet as its deep learning framework of choice at AWS.
- Apache Singa: SINGA is an Apache Incubating project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, its extensible to run over a wide range of hardware, and has a focus on health-care applications.
- Caffe2: This aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries.
- H2OH2O.ai emerged in 2011 from a grassroots culture of data transformation. With H2O, the main product, a plethora of machine learning models (from linear models to tree-based ensemble methods to Deep Learning) can be trained from R, Python, Java, Scala, JSON, H2O’s Flow GUI, or the REST API, on laptops or servers running Windows, Mac or Linux, in the cloud or on premise, on clusters of up to hundreds of nodes, on top of Hadoop or with the Sparkling Water API for Apache Spark.
There are many things to consider when it comes to choosing the right framework, reach out to us so that we can help you out instead. Send us an email and we will be happy to assist where possible. Contact us @[email protected].
