概要
Machine learning (ML) models have the unique ability to continuously improve without being explicitly programmed to do so. However, developing an effective model is far from an easy process—especially if you lack the requisite tools.
In this article, we’ll discuss six of the most essential types of machine learning tools: programming languages, ML libraries, ML frameworks, advanced ML tools, data storage and management for ML, and data platforms to support ML projects.
According to PwC, artificial intelligence (AI) will generate a $15.7 trillion boost to the global economy by 2030. Eager to earn their slice of the pie, many enterprises are setting their sights on machine learning development.
Why? Because artificial intelligence systems can process information more rapidly than humans and, in numerous situations, they can automate human decision-making. They prove highly effective in identifying clients who may default on payments or abandon your company, understanding their interests, and predicting their likely next actions. For a model to excel in these tasks, it must be meticulously developed.
Below, we’ll discuss six of the most essential types of machine learning tools. These categories aren’t specific software solutions you necessarily need, but the key elements without which ML couldn’t exist.
At its most basic, a programming language is a formal notation system for coding computer programs. Engineers use programming languages to instruct computers to perform specific tasks or operations, acting as a bridge between human understanding and machine execution.
Of course, choosing the right language is one of the most important decisions a data scientist can make when developing a machine learning model. Different languages offer varying levels of support for certain ML tasks and can directly impact the efficiency and flexibility of the end product.
Some of the most popular programming languages for machine learning applications include:
Ultimately, the right programming tool depends heavily on its intended use case. Other factors to consider include the amount of community support; generally, the bigger the community, the more resources available. Lastly, take the learning curve into account. If you’re not an experienced data scientist, you may have an easier time understanding Python than Java or R.
Libraries provide machine learning algorithms for various data science and data analysis purposes. They can serve as the building blocks for ML models and ensure data teams don’t have to do everything from scratch.
Some library options are open source, and therefore free to use, while others are paid products.
Is one better than the other? Ultimately, there’s no definitive answer. Many of the most popular and advanced machine learning tools are open source, but paid equivalents may be more scalable and performant, depending on the use case.
What’s most important is to consider each library’s potential limitations. For example, some may only offer machine learning algorithms in a certain programming language. Others are more suited for academic data analysis than enterprise applications, so choose according to your business needs.
A machine learning framework is a broad-spectrum platform that supports ML development. These typically include algorithm libraries as well as pre-built model training features, tools for model construction and deployment, and so on.
Think of a machine learning framework as a more comprehensive toolkit. Like a library, it ensures data scientists don’t have to build their machine learning model from the ground up. However, it provides a much firmer and more holistic foundation that helps organize and manage the entire end-to-end process.
Choosing a solid framework is important, but unfortunately, there’s no one-size-fits-all solution. As with most machine learning tools, the best choice depends on your experience and the model’s intended application, as well as your available resources.
Teams new to machine learning may have the most success with Scikit-learn, one of the oldest open-source frameworks on the market. Written in Python, this option is generally best for developers who want to implement popular machine learning algorithms like linear or logistic regression or decision trees. Scikit-learn also supports both supervised and unsupervised learning.
Frameworks with a wide range of features will be best for data teams and engineers who want or need as much support as possible or are under a tight schedule. Meanwhile, those who need frameworks with specific capabilities may have more limited choices.
Data scientists and ML engineers who need to create more complex models, want to do as much hands-on programming as possible, or need to build neural networks will require ML tools with advanced capabilities. This category includes both libraries and frameworks but deserves consideration all on its own for the degree of complexity it supports.
Examples of advanced machine learning tools include:
Tools like these may not entirely be meant for advanced users (TensorFlow is fairly flexible) but should not be used by beginners. Deep learning projects require adequate skill and experience, as do the other advanced ML initiatives such frameworks are designed to support.
ML doesn’t only necessitate great volumes of data (even for model training alone; the scope of the data handled only increases from there). A great deal of it (though not all) will be unstructured. Managing all this data can be exceptionally difficult, especially if you lack the requisite tools that simplify data preparation.
Cloud-based object storage, for instance, can scalably handle large datasets without major infrastructure changes. Likewise, object storage offers flexibility in terms of data formats and types—images, videos, text, and so on—which ML models often require to perform certain tasks.
Moreover, data scientists must have a sufficient management framework capable of handling unstructured data, such as a data lake. In short, data lakes are design patterns that focus on original raw data fidelity and long-term storage. They allow you to store data as is without having to first structure it and can be used to run different types of analytics through machine learning models, thereby uncovering new insights.
Inefficient data preparation and inflexible tools are perhaps the two most common reasons that enterprise analytics initiatives fail.
Fortunately, a comprehensive data platform like Teradata VantageCloud offers everything data scientists need in one cloud-based package. From lakehouse deployment patterns and lake object storage to our ClearScape AnalyticsÔ engine for AI innovation, you gain all the capabilities required to streamline development, analyze performance, and activate data to its full potential.
VantageCloud integrates with leading cloud platforms, including Amazon SageMaker, Azure Machine Learning, and the Google Cloud ecosystem. That way, no matter how you approach development, you can move AI/ML initiatives into wide-scale production and drive measurable business value from your analytics investments.
Connect with us to learn more about Teradata VantageCloud and how we can help your organization tap into the power of AI/ML.