Software development is continually evolving. Today, more and more software programmers are involved in the development of AI-powered apps. Scientific research revealed that 84% of businesses choose AI because it gives them a competitive edge! But for creating powerful and multi-featured apps, they need to use machine learning tools, and semi supervised machine learning algorithms that make programming easier and more effective. Let’s review the most popular machine learning tools that will enable programmers to learn new skills and build apps that meet the needs of a modern market.
The main distinctive feature of this tool is a JS library that makes machine learning development easier. Thanks to its APIs, you can build and train new models. Developed by the Google team, this open-source machine learning library allows programmers to build their ML models. It comes with a flexible scheme of tools and various resources that enable coders to develop and deploy ML apps.
The most outstanding features of TensorFlow are as follows:
- It allows for developing and training modules.
- It serves as a model converter that enables developers to run existing models through the use of TensorFlow.
- High-level APIs that you can use to train and build ML models.
- Flexible and open-source software.
- You can use it on different mobile computing platforms.
Being a flexible code completion tool, Kite uses machine learning techniques to fill in the code. When using this tool, the machine learning tool is developed by taking available code on GitHub, but an abstract syntax tree is derived from it. It allows auto-suggestion to be derived from the intention of the code.
Right, this framework is available for Python programmers and supports Windows, Mac OS and Linux.
Google Cloud ML Engine
It’s a must-have tool for programmers working on the development of ML-powered apps. It’s a well known hosted platform where coders develop and run their machine learning models.
We shortlisted the main features of this tool that are really worth your attention:
- Makes machine learning development, training, and predictive modeling easier.
- The services of viz.prediction and training can be used either simultaneously or independently.
- It’s a must-have tool for training complex models.
Amazon Machine Learning
It’s a powerful and cloud-based machine learning tool that is used by mobile app programmers to generate predictions and develop new ML models. Moreover, this tool integrates data from such resources as Amazon S3 or RedShift.
The main options of AML are as follows:
- It comes with a variety of visualisation and wizards tools.
- Support for binary classification, multi-class classification, and regression.
- It allows programmers to develop a data source object using the data stored in Amazon Redshift.
It’s a well-known Python machine learning library that allows programmers to develop neural networks via Autograd Module. PyTorch includes a number of optimization algorithms for creating neural networks. It can also be used on cloud platforms and a must-have tool for designing computational graphs.
This framework is free to use and doesn’t require specific skills to start using it. You can review the basic guidelines and immerse yourself into the development process. It’s very easy to use because PyTorch has a hybrid front-end.
This ML-framework is written in C# and is combined with audio and image processing libraries. It includes a few libraries to meet various needs. Accord.NET features libraries for pattern recognition, processing of statistical data, and even linear algebra.
Here’s a detailed list of the main features:
- It’s a must-have tool for building production-grade computer auditions, signal procession, or building statistics apps.
- It features 35 hypothesis tests such as two-way or one-way ANOVA tests or robust non-parametric tests.
- 38 kernel functions and 40 statistical distributions.
If you are currently searching for a multi-featured tool for data analytics, you will hardly find a better solution than KNIME. It is based on the pipelining concept and comes with a variety of essential components for data mining and machine learning.
It is up to you to decide which tool to use because a lot depends on your requirements, the price that you are ready to pay, and your level of expertise. But if your goal is to provide your clients with robust and effective software development solutions, you have to use ML tools.