“Machine intelligence is the last invention that humanity will ever need to make.” – Nick Bostrom
As the world enjoys the benefits of technology attracting industries worldwide, artificial intelligence and machine learning are two ground-breaking leaders. Keeping up with the competition, there are multiple tools that have been getting popular in the market and it is a tough choice on which one to take.
The list of machine learning tools is growing and so is the need to analyze them and understand how to choose the right one. Before we go on to evaluate the ML tools, let us have a look at what ML has achieved and what it is.
The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028 at a CAGR of 38.6% in the forecast period.
What Is Machine Learning?
Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. – Wikipedia
Machine learning (ML) is an interdisciplinary area that makes the most of algorithms, probability, and statistics to extract from data and offer detailed insights that can be leveraged to create efficient apps. It involves the study of computer-based algorithms that explore data. As an important subset of Artificial Intelligence, it has been embraced by the world as an excellent tool to increase client experience and enjoy automation.
The years to come shall witness an exponential rise in the adoption of ML and AI in various industry segments all around the globe, be it any size, any location. There are many tools used in machine learning that offer computers the competence to learn without programming explicitly. It imitates the way humans are learning with a better curve in the accuracy levels. It is an evolving branch of data science that discloses key insights into information through predictions, algorithms, and data models. Data scientists are making the best use of ML techniques to expand and increase their business outcomes.
Machine Learning Features:
- Effective automation and predictive analytics
- Automatic data visualization and merging with IoT devices
- Enhanced client engagement
- Easy identification of patterns and trends
- Continual improvement and better decision making
- Improved management of multi-faceted data
- Encompasses a wide range of industry applications
- Pattern classification and recognition
- Seamlessly connect with machine learning libraries and programming languages
- Support for vector machines
Machine Learning (ML) Tools Popular In 2022
- TensorFlow
- IBM Watson Studio
- Amazon Lex
- Microsoft Azure Machine Learning
- OpenNN
- Auto-WEKA
- Datawrapper
- Google Cloud AutoML
- Amazon Machine Learning
- MLJAR
- Tableau
- PyTorch
- Apache Mahout
- Keras
- Shogun
- RapidMiner
- Neural Designer
- Scikit-learn
- KNIME
- Spell
Let’s have a detailed look at the above-mentioned Machine Learning Tools.
Google TensorFlow:
As a popular machine learning tool powered by Google, TensorFlow is an open-source software framework for machine learning. It is a flexible tool for deep neural network research and is meant for large-scale applications. Its simplistic visualization of neural networks is one of its key USPs, liked by the developer community. With TensorFlow, numeric calculations are performed with graphs having edges for data arrays and nodes for mathematical operations.
Key Features:
- Usable across multiple platforms and devices
- Works well for research and machine learning activities
- Helps in creating neural networks and ML model
- Offers multiple abstraction levels for choosing apt resources
- Free to use and easy to deploy and train
- Fast iteration and intuitive debugging
IBM Watson Studio:
IBM Watson Machine Learning is an effective ML framework that leverages data to have ML and deep learning models for production usage. It is the best fit for creating ML apps via API connections. It helps developers to execute two major ML activities – training and scoring. Easy creation and training of models can be done with a faster speed and thereby accelerate the implementation of ML workflows in your daily business activities. It is used for building and scaling trusted AI on any cloud with automation of the AI lifecycle for ModelOps.
Key Features:
- Empowers data scientists to manage and execute AI models
- Syncs well with other open-source frameworks like TensorFlow etc.
- Automates AI lifecycle and fastens the entire process
- Supports NLP, visual recognition and Blockchain processes
- Streamlines the deployment of ML models
- Easy combination of visual data science with open-source libraries
Amazon Lex:
Amazon Lex is a secure, simple, and comprehensive ML framework and fully managed service, to handle deep learning issues like speech and text recognition, language perception, etc. It has strong integration with AWS Lambda for backend integration of business logic. ML-based conversational interfaced chatbots can be directly implemented to IoT devices, chat platforms, and mobile devices. Easy and effective creation, monitoring, and publishing of bots can be done with Lex.
Key Features:
- Good quality speech recognition and natural language processing
- Easy management of context across conversations
- Multi-turn conversations and dialogues
- Single-click deployment for various platforms
- Streaming of natural conversations
- Seamless integration with other technology frameworks
Microsoft Azure Machine Learning:
Azure Machine Learning, powered by Microsoft, is an enterprise-grade ML service for the creation and deployment of AI and ML models in an effective and faster manner. It empowers users in creating business-critical ML models with good quality and efficacy. It fastens the go-to-market value with interoperable features, integrated tools, and machine learning operations (MLOps). ML models can be easily deployed with a single click through the MLOps way of working.
Key Features:
- Well defined pre-trained models
- Free trial versions
- Useful for developers with limited coding experience
- Robust suite of product add-ons
- Flexible and easy building interface
- Fast and simple implementation of web services
OpenNN:
OpenNN stands for Open Neural Networks Library. It is a popular software library and machine learning tool that is effective in implementing neural networks which is a major arm of machine learning. It is written in C++, is open-source, and provides the facility of downloading the whole library for free from authentic sources like GitHub. It is a general-purpose AI and ML tool that is used for resolving predictive analytical activities in various segments like energy, engineering, etc.
Key Features:
- Solves many real-world applications in health, energy, etc.
- Sophisticated algorithms and utilities to manage solutions
- Predictions about the future
- Pattern assignment to different categories
- High-capacity data set loading
- Faster training and higher productivity
Auto-WEKA
Auto-WEKA is an effective data mining tool and ML software tool that handles the selection of learning algorithms simultaneously instead of doing it in isolation. There is a completely automated approach with the latest innovative ways. It has a combination of selecting algorithms and optimizing hyperparameters as against the regression and classification ones in WEKA. Amidst the available datasets, it extracts the different settings and chooses the best possible one for the users that can offer good performance values.
Key Features
- Automized model selection
- Hyperparameter optimization
- Maximized performance and productivity
- Easily accessible to end-users
- Fully automated approach
- Helps novices in effectively identifying ML algorithms
Datawrapper:
Datawrapper is a user-friendly, open-source software tool that is ideal for the creation of interactive charts, graphs, maps, etc. from the heap of data in a fast manner. It helps data scientists in extracting the maximum value out of data that can be availed in any machine learning model. The charts and maps that are availed can be embedded into websites. A variety of charts like pie charts, bar charts, line charts, etc. can be created. It does not need detailed knowledge about programming or coding.
Key Features
- Availability of different color options
- Export facility for different file formats
- Live chart update facility
- Faster creation of interactive charts
- Online access or can be installed on the server
- Data inputs through CSV or direct input
Google Cloud AutoML:
Powered by Google, AutoML is an effective ML software language that offers the power of machine learning with the least programming know-how. As a suite of ML products, it creates effective ML models with the least experience and expertise. These products train customized ML models with the help of Google’s learning expertise and technology. The GUI that it offers is simple, improvised, and effective for data to be stored in the cloud. It is more like a hosted platform that can be availed by developers for executing different machine learning models.
Key Features
- Offers ML model training, predictive modeling, and deep learning
- Can be easily used for training complicated models
- Independent or combined use of the two services
- Ideal for use by enterprises for jobs like faster response to emails etc.
- Utilizes the power of AI in business activities
- Natural language processing, speech to text, and vice versa
Amazon Machine Learning:
Amazon Machine Learning is an AWS product that helps developers in discovering patterns in data via different mathematical models and algorithms, based on which predictive applications can be implemented. It is a managed service to create ML-based apps and it has an automatic data transformation tool that eases the usage further. There is Amazon SageMaker too, which is a completely managed platform for utilizing ML models. AML is a cloud-driven and robust ML software tool that integrates data from disparate sources.
Key Features
- Identifies patterns and creates mathematical models
- Offers support for different types of models
- Helps in retrieving predictions with real-time APIs
- Easy handling of large datasets with many servers
- Customizable and easy to update
- Strong auto-scaling server availability
MLJAR:
MLJAR is an outstanding data science tool that offers the best machine learning capabilities through prototyping, developing, and implementing pattern recognition algorithms. The MLJAR tools are efficient in saving time and offer you the best results. Data must be uploaded with a secure connection; training must be done through ML algorithms and the best models must be shared as results. MLJAR AutoML is a Python package for automated machine learning on tabular data.
Key Features
- Saves time and efforts
- Python-based package
- Works well with tabular data
- Automatic detection of data type
- No pre-processing required
- Handles appropriate missing values
Tableau:
Tableau is a trendy visual analytics platform that transforms the way data is applied to get results out of piles of information. It inspires its users to get the finest out of data. It helps in the discovery and supervision of data with a rapid speed and distribution of perceptive information that can bring in improved productivity. Tableau has a knowledgeable visual analytics platform that makes the finest use of the collective, pioneering, and visual interfaces for illustrative and graphical interpretation of data.
Key Features
- Secure, flexible, and comprehensive
- Helps users recognize and envision data
- Seamless creation of graphs, maps, charts, etc.
- Real-time data analytics
- Robust and interactive graphics and security
- Data blending and collaboration
PyTorch:
PyTorch is a popular machine learning framework that depends on the Torch library. It is free, open-source, and leveraged for various applications such as NLP, computer vision, deep learning research, etc. It is recommended to fasten the procedure between research prototyping and deployment. It has C++ and Python interfaces that help in effectively developing a variety of ML-based applications. There is a Tensor class that contains a multi-dimensional array to perform tensor-based calculations along with GPU support.
Key Features
- Creation of neural networks
- Ideal for deep learning research with faster speed and convenience
- Can be utilized better on cloud platforms
- Easily accessible and offers a dynamic graph
- Provision to alter network performance instantly
- Ample tools, libraries, and documentation to support
Apache Mahout:
Apache Mahout is a scalable machine learning software tool that is apt for creating efficient ML applications. It is a distributed linear algebra framework that helps data scientists and mathematicians implement their own algorithms. As a part of the Apache Software Foundation, it has been offering Java and Scala libraries that perform mathematical procedures that depend on statistics and linear algebra. It is implemented on the top of Apache Hadoop and utilizes the MapReduce paradigm. There are Java libraries for different algorithms and other statistical functions.
Key Features
- Effective for implementing scalable algorithms
- Vector and matrix libraries
- Offers support for different backend frameworks
- Executes on top of Apache Hadoop
- Ready-to-use framework for data mining
- Creates applications with clustering, filtering, etc.
Keras:
Keras is a light weighted, Python-based, deep learning API that is apt for quick prototyping of ML-driven algorithms. It executes on the top of other software tools like Theano, CNTK, TensorFlow, etc. It is best utilized to create deep models and distributed training that can be leveraged on mobile devices. it can execute effectively on GPU and CPU supporting convolutional and recurrent networks. There can be quick prototyping due to the library and its efficient interface, hence offering a faster speed of modeling.
Key Features
- Fast neural network implementation
- High-level interface
- Executes with ease on CPU and GPU
- Supports almost all models of neural network
- Large community support
- User-friendly and faster deployment
Shogun:
Shogun is a free, open-source ML software tool that has a variety of ML methods. It is written in C++ and offers great support for languages like R, Python, etc. with the help of SWIG (Simplified Wrapper and Interface Generator). There are various data structures and algorithms in Shogun that can assist in creating high-end machine learning programs. The USP of Shogun is its kernel machines like support vector machines that are meant to handle regression and classification issues.
Key Features
- Kernel-based algorithms
- Full implementation of Hidden Markov models
- Complete support for the usage of pre-calculated levels
- Designed to handle huge datasets
- Works on interfaces with different languages like C#, Python, etc.
- Well organized implementation of algorithms
RapidMiner:
RapidMiner is an open-source, data science software tool that offers a comprehensive infrastructure to prepare data, deep learning, machine learning, predictive analytics, and text mining. Predictive models are created, in a fast manner, with the help of automatic machine learning and data science best practices. It optimizes data in the finest manner, with the help of machine learning algorithms. Users can choose the balanced model for creating the predictive model.
Key Features
- Visual data science workflow designer
- Easy to use and create varied analytics
- Faster to design ML models
- End-to-end data science platform
- No-code development platform for data mining
- Provision to import own datasets
Neural Designer:
Neural Designer is a well-known code-free AI platform and ML software tool that is based on neural networks and possesses a GUI that can simplify data entry and result interpretation. It is user-friendly and apt for data science and ML. It helps in the creation of AI-enabled applications without the need to code in detail. It is completely programmed in C++ and hence offers great performance. It offers all different types of data analytics like descriptive, predictive, diagnostic, and prescriptive analytics.
Key Features
- Implementation of deep architecture with different layers
- Mathematical expressions to be exported to various computer languages
- Effective memory management for huge data sets
- Great management of data processing outcomes
- Deals effectively with issues related to parameter optimization
- Faster computations with GPU and CPU optimization
Scikit-learn:
Scikit-learn is a popular, open-source, machine learning tool in Python that is meant for everyone and is reusable in different contexts. It has effective and commercially usable tools that are used for predictive data analysis. It is created on SciPy, NumPy, and matplotlib. There are high-level wrappers that empower users to handle different ML-based algorithms and work with different clustering, classification, and regression models. It offers a detailed vision of the models by unwrapping code and following various dependencies.
Key Features
- Apt for solutions having limited time and resources
- Especially meant for machine learning-based solutions
- Part of predictive modeling aspects and solutions
- Linear and logistic regression
- Support Vector Machines (SVM)
- Possesses inbuilt datasets like iris dataset, house prices dataset, etc.
KNIME:
KNIME stands for Konstanz Information Miner. It is an open-source, well-known machine learning tool that is ideal for BI, data reporting, analytics, integration, and data mining. It is largely used in various industry segments like pharmaceuticals, finance, etc. Since it is easy to learn and installable, developers, especially novices and those with less coding experience, find it most preferred for ML-related tasks. It synchronizes well with programming languages like R, Java, Python, etc.
Key Features
- Free, open-source, and easy to learn
- Seamless integration of code of Java, C, R, Python, etc.
- Intuitive user interface
- Sophisticated data handling
- Well-defined API for plug-in extension
- Competence to create visual data flow
Spell:
Spell is one of the first and leading machine learning operations (MLOps) platforms for deep learning applications in dynamic environments. It has a specialization in creating and implementing machine learning projects that are created to cater to the requirements of deep learning stakeholders in different infrastructures – on-premises or cloud-based. It takes into consideration the entire lifecycle of a project right from prototyping to implementation including all processes that come in between.
Key Features
- Effective command-line interface
- Supports simplistic and easy to use commands
- Infrastructure automation with an extensible platform
- Operates on all types of platforms
- Reduces redundancy and improves teamwork
- Lessens costs and time to value
Machine Learning Tools – The Wrap-up
As we observed the major AI and machine learning tools, listed above, it is finally the organization’s call on which one to choose from. The decision lies in the hands of the stakeholders based on different parameters like project deadlines, costing approach, skilled resource availability, ease of use, etc.
The world of machine learning services and solutions has revolutionized the globe and these popular ML tools are acting as the apt catalysts to gain the maximum benefit!
