As the world undergoes a tremendous digital revolution, there is a greater need than ever for AI machines. These machines can become intelligent only by a process called machine learning. There are special software called machine learning software for this purpose.
When there are so many options, selecting the best machine learning software may be a daunting task. Moreover, no one has the time to weigh all of the pros and cons of each software before settling on one. To help you out, SaaSworthy has compiled a list of the 5 top machine learning software in 2021.
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What is machine learning software?
Machine learning is a method of data analysis that uses algorithms to discover patterns, allowing systems and software to learn and forecast outcomes without programming. Machine learning enables computers to make smarter judgments at a faster rate and with more accuracy. This approach is low-cost and capable of analyzing big and complicated data sets.
Any specialized tool used for artificial intelligence, data-driven self-iteration, unsupervised learning, and other machine learning categories is referred to as machine learning software. Machine learning software enable users to develop their own machine learning algorithms for any data they want.
5 top machine learning software to use in 2021
The 5 top machine learning tools available in 2021 are given below along with their various features and limitations. Go through the entire machine learning software list thoroughly so that you can make an informed decision.
1. TensorFlow
TensorFlow is a well-known name in the field of machine learning. TensorFlow, currently owned by Google, is a free and open-source machine learning software. It provides an open-source platform designed for large-scale machine learning applications.
It integrates deep neural networks with various machine learning approaches and is extremely beneficial to Python users. TensorFlow is the one to go with if you are looking for a machine learning software free download.
The various features of TensorFlow include –
- Through its broad interface of CUDA GPUs, it enables the development of both statistical machine learning solutions and deep learning.
- It contains a large library of functions for operations involving text, pictures, video, tabular data, etc., as well as multi-GPU support with easy, straightforward documentation.
- It offers support and features for a wide range of machine learning applications, including computer vision, natural language processing, and reinforcement learning.
- It can also compute numerically by utilizing data flow graphs, and you can train and build ML models with ease leveraging high-level APIs like Keras and eager execution. You can use TensorFlow.js, a model converter, to execute your existing models.
The limitations of TensorFlow are –
- It has a steep learning curve.
- A few of the error messages can be complicated for you to comprehend.
2. Amazon Machine Learning
Amazon Machine Learning is a top machine learning software that allows users to create, implement, and execute machine learning applications in the cloud using AWS. It is a top-rated software as well as a leader in its category.
It seeks to put machine learning prospects at the disposal of every developer and data scientist. Companies that already use the AWS environment are at an advantage since it allows them to handle all of their ML needs in one service.
The various features of Amazon Machine Learning include –
- It features a powerful auto-scaling model server and can effectively handle large datasets by utilizing several servers.
- It auto-scales the hosted models based on workload by changing the number of instances supplied for a model. You can use one-click notebooks that can be shared to visualize and recreate models to achieve deep collaboration.
- It is possible for you to visually monitor the progress of machine learning modules, and customizations are simple to tweak and update.
- You can easily create, train, and deploy multiple machine learning services, integrate artificial intelligence into existing applications, or develop tailored solutions depending on the particular demands of your company.
The limitations of Amazon Machine Learning are –
- It can become slow at times, taking longer than expected.
- It is suitable only for those who have sound programming knowledge.
3. Apache Mahout
Apache Mahout is an open-source and free machine learning software from the Apache Software Foundation. It is centered on collaborative filtering and classification. While it is still under development, the number of algorithms it supports is rapidly increasing.
This framework’s primary purpose is to assist data scientists, mathematicians, and statisticians in the execution of their algorithms. It offers a straightforward and versatile programming environment and framework for developing scalable algorithms. It is one of the best machine learning tools for data science.
The various features of Apache Mahout include –
- It includes Java libraries for standard arithmetic operations as well as algorithms for regression, pre-processors, recommenders, clustering, and distributed linear algebra.
- It includes native solutions for CPUs, GPUs, and CUDA accelerators. It comes with matrix and vector libraries. It aids in the implementation of machine learning processes such as clustering, recommendation, and classification.
- It includes an expressive Scala DSL as well as a distributed linear algebra framework for deep learning operations. It includes an extendable framework for developing scalable algorithms.
- It uses the Map/Reduce paradigms to run on top of Apache Hadoop. Thanks to this, you can quickly analyze data from Hadoop file systems.
The limitations of Apache Mahout are –
- It lacks the essential online documentation as it is an open-source software.
- It does not have an intuitive user interface.
4. RapidMiner
RapidMiner is a top machine learning software that also supports deep learning, text mining, data preparation, and predictive analytics. RapidMiner is used by over 40,000 organizations in more than 150 countries.
In the past few years, RapidMiner has been named a category leader by various SaaS review websites including SaaSworthy. In the Forrester Wave report for 2020 Predictive Analytics and Machine Learning, RapidMiner was named a Leader.
The various features of RapidMiner include –
- It provides model validation by removing overfitting while also allowing one-click cross-validation. It is compatible with existing applications and scripts, such as Python and R.
- Its drag-and-drop visual interface in the platform’s Visual Workflow Designer includes a library of 2,000 algorithms and operations, pre-built templates, and step-by-step guidance for newbies.
- With the help of dynamic data visualization and exploration, you can assess the data’s health, validity, and quality. You can collect and reuse data from databases, social media, cloud storage, and other sources.
- It assists in the preparation of data, the presentation of results, the validation and optimization of models.
The limitations of RapidMiner are –
- The documentation provided with the software is not organized and is full of errors.
- It is expensive compared to other machine learning software and has a steep learning curve.
5. Google Cloud AutoML
Google Cloud AutoML is a machine learning product suite that allows developers with a minimal machine learning experience to train high-quality models tailored to their business requirements. This is accomplished via the use of Google’s cutting-edge transfer learning and Neural Architecture Search technologies.
Google AutoML is highly popular among many organizations that are just beginning to explore the potential of artificial intelligence. They use pre-trained models built to support various services such as speech and text recognition.
The various features of Google Cloud AutoML include –
- It enables users with little to no experience to design and train individual ML models. It offers a user-friendly UI with a drag-and-drop feature.
- It offers machine learning model training and development, as well as deep learning and predictive modeling. You can utilize the prediction and training services separately or in tandem.
- Notebooks (based on JupyterLab) can be used by more experienced data scientists and ML developers to communicate, construct, and implement ML models into production.
- It includes a What-If feature within notebooks that allows users to evaluate AI Platform Prediction models via an interactive dashboard.
The limitations of Google Cloud AutoML are –
- It is an expensive choice if you want to make use of the custom training feature.
- It does not work well with some coding languages like Python.
Conclusion
We hope that we were able to provide an insight into the various machine-learning software available. Now that you have gone through the list of the 5 top machine learning software, you can choose the one that meets your requirements. The machine-learning software that you choose depends on your algorithm requirements, your level of skill, and its pricing.
For more informative articles like this one detailing the features and limitations of the top software in each category, visit SaaSworthy Blog.
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