Opting for the correct machine learning framework
Machine learning (ML) systems are interfaces that enable data scientists and developers to develop and deploy machine learning models quicker and in an easier manner. Machine learning is leveraged in nearly every field, noteworthy ones being insurance, healthcare, finance, and marketing. Leveraging these utilities, enterprises can scale their machine learning initiatives while upkeeping an effective machine learning lifecycle.
Enterprises can opt to develop their own customized machine learning framework, however most enterprises opt for a current framework that is in line with their requirements. In this blog post, we’ll demonstrate critical considerations for choosing the correct machine learning framework for your project and briefly review four different famous ML frameworks.
Through this blog post, you will learn:
- How to opt for the correct ML Framework
- Evaluating your requirements
- Parameter Optimization
- Scaling Training and Deployment
- Leading machine learning frameworks
- Sci-Kit Learn
How to opt for the correct ML framework
There are several critical considerations you should consider when choosing a machine learning framework with regards to your project.
Assessing your requirements
When you begin your search with regards to a machine learning framework, ask yourself these three unique questions:
- Will you be leveraging the framework for deep learning applications or classical machine learning algorithms?
- What is your programming language of preference for artificial intelligence (AI) model production?
- What software, hardware, and cloud services are leveraged in scaling?
R and Python are languages that are broadly leveraged in machine learning (ML), but disparate languages like C, Java, and Scala are also available. A majority of machine learning applications presently are authored in Python and are moving away from R as R was developed by statisticians, and is considerable awkward to work on. Python is more sophisticated and progressive programming language, it provides a simple and concise syntax, and it is more accessible.
Machine learning (ML) algorithms leverage varying strategies to undertake analysis of training information and go about applying what they learn to new instances and scenarios.
Algorithms contain parameters, which you can perceive of as a dashboard with switches and dials that administer the operation of the algorithm. They alter the weights of variables to be considered, provide definitions to how much to take of outliers, and make other modifications to the algorithm. When opting for a machine learning framework, it is critical to consider whether this alteration should be automated or manual.
Scaling Training and Deployment
In the training stage of AI algorithms production, scalability is the amount of data that we can undertake analysis of and the rate of the analysis. Performance can be enhanced through distributed algorithms and processing, and via the leveraging of hardware acceleration, mainly graphical processing units (GPUs)
In the deploying stage of an artificial intelligence project, scalability is in relation to the number of concurrent end-users or applications that can access the model at the same time.
As there are varying requirements in the training and deployment stage, enterprises have a tendency to produce models in one kind of environment (for e.g., Python-Based machine learning frameworks working in the cloud) and execute them in a differing environment with strict requirements for performance and high availability – for instance, in a data centre that is on the premises.
When opting for a framework, it is critical to consider if it is compatible with both variants of scalability, and observe if it supports your planned development and production environments.
Leading machine learning frameworks
Let’s observe a few of the most famous machine learning frameworks in utilization presently:
- Sci-Kit Learn
TensorFlow was developed by Google and put out as an open-source project. It is a dynamic and capable machine learning utility with an extensive library of extensive and flexible functionalities, and enables you to develop classification models, regression models, neural networks, and a majority of other variants of machine learning models. This also consists of the capacity to go about customizing machine learning algorithms to your particular necessities. TensorFlow functions on both GPUs and CPUs. The main challenge with TensorFlow is that it is not very accessible for starters.
Primary features of TensorFlow:
- Visibility onto computational graph: TensorFlow makes it simple to visualize any portion of the computing process of an algorithm, referred to as a graph, which not compatible with older frameworks like SciKit or Numpy.
- Modular: TensorFlow is very modular and you can leverage its components standalone, without the need to utilize the entire framework.
- Distributed training: TensorFlow imparts robust support with regards to distributed training on both GPUs and CPUs.
- Parallel neural network training: TensorFlow imparts pipelines that enable you to go about training several neural networks and several GPUs in parallel, making it really efficient on large distributed systems.
With the release of TensorFlow 2.0, TensorFlow had added various critical new features.
- Deploying on several platforms: Enhanced compatibility for mobile devices, IoT and other surroundings, leveraging the SavedModel format that enables you to go about exporting TensorFlow models to literally any platform.
- Eager execution: In TensorFlow 1.x, users were required to develop the cumulative compute graph and execute it, in order to evaluate and debug their work. TensorFlow 2.0, much like PyTorch facilitates eager execution. This implies that models can be altered and debugged while they’re being developed, without requiring to execute the entire model.
- Tighter integration of Keras: Keras was compatible with TensorFlow, but didn’t feature integration into the library prior. In TensorFlow 2.x, Keras is an official high-level API that comes with TensorFlow.
- Enhanced support for distributed computing: Enhanced training and performance leveraging GPUs, which is up to three-fold quicker than in TensorFlow 1.x, in addition to the capability to work with several GPUs and Google TensorFlow Processing Units. (TPUs)
PyTorch is an ML-framework on the basis of Caffe2 and Torch, which is perfect for neural network design. PyTorch is open-sourced and is compatible with cloud-based software development. It is compatible with Lua Language for user interface development. It features integration with Python and features compatibility with famous libraries like Cython and Numba.
Primary features of PyTorch:
- Is compatible with eager execution and improved flexibility via leveraging native Python code for model development.
- Quickly switches from development to graph mode, giving high performance levels and quicker development in C++ runtime settings.
- Leverages asynchronous execution and P2P communications to enhance performance both in model training and in production settings.
- Furnishes an end-to-end workflow enabling you to develop frameworks in Python and deploy on iOS and Android. Extensions of the PyTorch API manage typical pre-processing and integration activities needed to integrate machine learning models into mobile applications.
Sci-Kit Learn is open-sourced, is very accessible, even for ones who are new to machine learning, and carries comprehensive documentation. It enables the developer to modify the algorithm’s preset parameters either when being leveraged or at runtime, making it simple to tune and troubleshoot models.
Sci-Kit Learn supports machine learning development with a comprehensive Python library. It is one of the ideal utilities available with regards to information mining and analysis. Sci-Kit Learn has comprehensive pre-processing functionalities, and facilitates algorithm and model design with regards to classification, clustering, dimensionality reduction, regression, and model selection.
Primary features of Sci-Kit Learn:
- Is compatible with a majority of supervised learning algorithms – linear regression, compatibility with vector machines (SVM), Bayesian, decision trees, etc.
- Is compatible with unsupervised learning algorithms – factoring, cluster analysis, unsupervised neural networks, and principal component analysis (PCA).
- Executes feature extraction and cross validation – obtains features from text and imagery can undergo extraction, and evaluates the accuracy of models on new unobserved information.
- Is compatible with clustering and ensemble strategies – Can bring together forecasts from differing models, and can group unlabelled information.
This is an open-sourced machine learning framework produced to find a solution to the business problems of decision support system processes. It features integration with other frameworks, which includes the ones we looked through above, to manage actual model development and training. H2O is broadly leveraged in risk and fraud trend analysis, patient analysis in healthcare, advertising expenditures and ROI, and client intelligence.
H2O components consist of:
- Deep Water: goes about integrating H2O with other frameworks such as TensorFlow and Caffe
- Sparkling water: goes about integrating H2O with Spark, the big data processing platform.
- Steam: this is an enterprise variant that facilitates training and deployment of machine learning models, making them available via APIs, and integrating them into apps.
- Driverless AI: facilitates non-technical personnel to collate information, modify parameters, and leverage ML to decide the ideal algorithm to find a solution to a particular business issue.