Wouldn’t it be great if all companies focused on making data-driven decisions to enhance customer experience and offer more personalized solutions? Well, there is one technology that can be used to reshape the customer experience by keeping a history of their buying preferences and offering recommendations, suggesting additional products to buy, easing their navigation experience, and more. This technology is nothing but machine learning. We are living in a digital world where many companies have realized the great impact of incorporating artificial intelligence and machine learning to streamline their operations, offer best-in-class customer services, and ultimately generate higher revenues.
But what does this mean for you? Well, it translates into a growing number of machine learning job opportunities to tap. It comes with no surprise that machine learning talent is in high demand these days, especially those who can use various machine learning algorithms to create innovative products. Companies like Google, Amazon, IBM, Facebook, Microsoft, and many more are investing hugely in this technology, thereby creating better career prospects for machine learning engineers.
If you are interested in starting a career in machine learning, then one of the important things you should be well-versed in is ML tools. This article makes you familiar with the top ML tools that you should gain a clear understanding of.
Top Machine Learning Tools
ML tools help engineers when performing various tasks like selecting models, preparing data, working with large data sets, text mining, data visualization, regression, clustering, classification, dimensionality reduction, and so on. Here are some of the good ML tools.
TensorFlow
TensorFlow is an end-to-end open-source machine learning platform. It is a core library, developed by Google, to help engineers develop and train ML models. With TensorFlow, it is easier to build and deploy ML-powered applications to solve real-world problems. There are multiple levels of abstraction in this tool that allows you to use an appropriate one as per your project needs. Its major feature is the simple and flexible architecture that can take new ideas from concept level to code, and thereby models to publication faster.
XGBoost
XGBoost is an open-source optimized distributed gradient boosting library that is highly flexible, portable, and efficient. The Gradient Boosting framework can be used to implement machine learning algorithms. The library can handle problems as long as the data fit into the user’s memory, meaning there are millions of instances. It facilitates parallel tree boosting that helps in solving various data science problems faster and accurately. The library is compatible with Windows, macOS, and Linux and supports languages like Java, C++, Python, Julia, R, Perl, and Scala.
PyTorch
The official website describes PyTorch as an open-source machine learning framework that accelerates the path from prototyping to production deployment. Some of the top features that PyTorch offers are distributed training, production-ready, cloud support, and a robust ecosystem. It enables flexible and fast experimentation and effective production through a user-friendly front-end, distributed training, and an ecosystem of libraries and tools. TorchServe is used for deploying PyTorch models, supporting features like logging, multi-model serving, metrics, and more.
Apache Mahout
Apache Mahout is a library dedicated to scalable machine learning. Mahout is currently being used in many areas like powering a recommendation engine alongside Apache Hadoop and Hive, creating user interest models, and for anti-spam analytics. Using Mahout, machine learning engineers can quickly develop their own distributed algorithms with a mathematically expressive Scala syntax. The library’s core algorithms include clustering, recommendation mining, classification, collaborative filtering, and frequent item-set mining.
Microsoft Azure Machine Learning
Use must already be aware of Microsoft Azure, a popular cloud services provider. As the name suggests, Azure offers machine learning as a ‘service’ that can be used by data scientists, machine learning engineers, and others in their workflows. It is designed for accelerating and managing the machine learning project lifecycle. Those organizations that wish to bring ML models into production in an auditable and secure production environment can use Azure Machine Learning. The engineers can run the training script either in the cloud or build an ML model from scratch.
RapidMiner
RapidMiner is an enterprise-ready data science platform that supports the data team across the full analytics lifecycle. When looking in terms of machine learning, you can use RapidMiner to operationalize your models faster and monitor their performance over time so that it generates maximum long-term value. Its containerized architecture and code-free model ops help you avoid deployment friction so that they can make accurate predictions. The platform also facilitates the identification of problematic trends in the initial stages so that you can address them before they create a negative impact on your projects.
Over to You
Now that you are aware of the top ML tools, you can get started with any one of them. If an independent study isn’t something that you would prefer, then there are a variety of machine learning courses online to help you gain expertise in using ML tools. You can either go for a complete machine learning course that deals with all the concepts from scratch or take a course that is specific to a tool. The first option is better for a beginner and the second one is suitable for those who already have a foundational knowledge of machine learning. Happy Learning!
