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This article introduces the knowledge of "how developers choose the most appropriate machine learning framework". Many people will encounter this dilemma in the operation of actual cases. next, let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Three questions to consider before choosing a machine learning framework!

According to Mike Gualtieri, vice president and chief analyst for AI at Forrester Research, there are three issues that developers need to think about when choosing a machine learning framework:

1. Is the framework used for deep learning or classical machine learning?

2. What is the preferred programming language when developing the AI model?

3. What hardware, software and cloud services are used to expand the development process?

Choose deep learning or classic machine learning?

Deep learning is a branch of machine learning. At the algorithm level, classical machine learning algorithms may not be unable to achieve the function of deep learning. Many machine learning algorithms can complete various deep learning tasks perfectly in applications. Although some frameworks support these two algorithms to some extent, machine learning frameworks tend to perform better.

From a difference point of view, the deep learning framework pays more attention to the neural network direction, especially TensorFlow. Other machine learning frameworks that perform well in deep learning include MXNet and Caffe, which support writing algorithms for image tagging and advanced natural language processing, as well as other applications.

The biggest difference between deep learning and machine learning framework is that the data structure is different. Deep learning framework can be trained to deal with structured data, but machine learning framework is not suitable for unstructured data. Before choosing a framework, you must understand the data type of the enterprise and the type of application you want to build.

The classical machine learning algorithm is suitable for various optimization and statistical analysis, and the most popular machine learning framework is Scikit-learn. Scikit-learn is suitable for writing in Python, but Comprehensive R Archive Network (also known as CRAN) may be more suitable for writing applications in R. Other popular software packages include Apache Spark MLlib and H2O.ai. H2O.ai has an open source machine learning algorithm that works well.

What is the preferred programming language for developing the AI model?

As far as programming languages are concerned, Python and R are common choices for machine learning developers. Of course, you can use other languages, such as C, Java and Scala. Gualtieri says that most machine learning applications today are written in Python because the R language is designed by statisticians and is not the most elegant programming language. In comparison, Python is a more modern programming language, and Caffe and TensorFlow are also the mainstream choices for developing Python encoders for machine learning models.

The difference between test environment and actual production environment

In the early stages of development, data scientists may choose models or algorithms for different datasets, but if you decide to run the same model against all datasets in a production environment, then you can look at some frameworks that support distributed architectures, such as Apache Spark's MLlib or H20, because scalability is a very practical and important issue.

There are many similar scenarios in deep learning. For example, AI developers want to annotate images. They can download TensorFlow and run it on the desktop to train algorithms and experiment with different models. Once they have sorted out a feasible model, they can't wait to throw it into the entire production environment, but this model is not necessarily suitable for the whole production development environment, because the test environment is very different from it. Hardware conditions and cloud service support also need to be considered.

In the training phase of AI algorithm, scalability refers to the amount of data that can be analyzed and the speed of data analysis. The performance can be significantly improved by using distributed algorithms and distributed processing. In the actual deployment phase, scalability is more related to the number of concurrent users or applications that can hit the model immediately. The reason for the problems with many AI projects is that the training environment and production environment are very different, but data scientists use only one set of tools.

Parameter optimization

Another key factor in choosing a machine learning framework is parameter optimization. Each algorithm uses a different method to analyze the training data and apply its learning content to the new example. Each parameter can be adjusted by different combinations of knobs and dials, and some possible combinations can be enumerated by adjusting the weights and abnormal values of different variables. When choosing a machine learning framework, it is important to consider whether you want to adjust these parameters automatically or manually. The more knobs and dials you need to adjust, the more difficult it is to find the right combination.

Whether machine learning framework is the best choice to solve specific problems is the last question that enterprises have to consider. We can divide machine learning development tools into three categories: notebook-based, multi-modal and automated.

Notebook-based-use tools such as Python-based Jupyter to provide complex control over all aspects of machine learning model customization, and the machine learning framework uses this concept to reduce the workload of customization.

Multi-modal (multiple schemas) is essentially a way of writing low-quality code that combines data science with specialized tools such as Salesforce Einstein, enabling developers to extend the core AI model to specific use cases.

Automated (Automation method) uses tools to automatically try all possible algorithms for a given input dataset until the best candidate for a particular use case is determined. These tools include products such as Google AutoML,DataRobot and H20.ai.

Automation is really attractive because the technical level of data scientists may not be very high, especially at a time when there is a shortage of talents in society as a whole, and with the support of automation tools, statisticians can sometimes do some of the work of data scientists, of course, do not expect these automation tools to completely replace human labor.

The open source machine learning framework provides rich community support

Chad Meley, vice president of marketing at Teradata, said that despite vendors' efforts to develop machine learning tools and frameworks, open source frameworks will continue to dominate the field because they bring together the wisdom of experts in relevant fields around the world.

Many larger cloud providers are also actively providing their own frameworks, such as Google and Amazon. According to the enterprise IT strategy, CIO may prefer to be consistent with specific cloud providers, or emphasize portability across multiple clouds and on-premises deployments. Currently, the Google-backed TensorFlow framework has the highest share of the market, followed by Caffe, Keras, MXNet, CNTK and PyTorch.

In addition, MLlib provided by Spark is also a good choice, it also provides SQL, graphics processing, streaming and other functions. Spark itself is easy to use and has a large user base, which ensures a steady improvement in technology and strong vitality to ensure that it will survive for a long time to come.

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