Machine learning software is a type of artificial intelligence (AI) that uses data to predict the outcomes of specific situations. Today machine learning technology is implemented across various business sectors to coordinate processes based on predicted outcomes.
If you are looking to improve your organization’s efficiency, consider leveraging machine learning tools. These platforms can help your team build machine learning models that will generate meaningful insights. In turn, this can lead to smarter business decisions and better outcomes across the organization.
Top machine learning tools
The top machine learning tools include:
Vertex AI is Google Cloud’s hosted AI platform that unifies several processes within the machine learning workflow. It enables users to train their machine learning models, host models within the cloud, and use the models to reach conclusions about data.
This product contains ML workflow orchestration features that are helpful for data developers looking to streamline their ML processes.
It comes with pre-trained models, and users can also generate their own tailored models to suit their needs. Vertex AI users can leverage Python-based toolkits to develop their machine learning models, as the solution supports PyTorch, Scikit-learn, and TensorFlow.
Users can customize their settings when training their machine learning models at scale in the platform. This way, they can tune hyperparameters to ensure the accuracy of their models prior to deployment.
Vertex AI enables users to make predictions about new data through HTTP prediction and batch prediction features. After this, they can monitor their predictions and manage them as time goes on.
Vertex AI does not help users with data sourcing or model coding. Instead, it provides features for processes like model training, deployment, predictions, monitoring, and management.
Pricing is calculated based on the included features, and prospective buyers can contact the sales team to receive a custom quote.
PyTorch is an open-source machine learning framework that helps with deep learning and ML model development. It is an ideal solution for users who wish to develop deep learning architecture for large data sets.
PyTorch’s TorchScript tool lets users switch between eager mode and graph mode so they can gain the benefits of each when producing their models. Eager mode prioritizes flexibility and user-friendliness, whereas graph mode is best for speed and functionality needs.
Users can deploy their models at scale with TorchServe, which supports various deployment processes, including multi-model serving, metrics, and logging. It also lets users create RESTful endpoints to allow for application integration.
The C++ frontend is a pure C++ interface to PyTorch that makes it possible to perform research in low latency, high performance, and bare metal C++ applications. Additionally, Python and C++ provide native support for peer-to-peer communication and asynchronous execution of collective operations. This way users can conduct distributed training on neural network models, and PyTorch can support the performance of both research and production efforts.
PyTorch extends the PyTorch API, enabling users to perform workflow processes on iOS and Android. It also covers preprocessing and integration tasks for users to incorporate ML in mobile applications.
PyTorch is supported on major cloud platforms to simplify and streamline model development and scaling. Users can run it through popular cloud platforms and machine learning services like Amazon Web Services, Alibaba Cloud, Microsoft Azure, and Google Cloud Platform.
Users’ hardware must meet specific technology prerequisites in order for them to successfully install and run PyTorch product packages. These requirements may be cost-prohibitive, therefore making PyTorch inaccessible.
PyTorch is a free, open-source tool.
Microsoft Azure Machine Learning Studio
Microsoft Azure Machine Learning Studio is a tool that contains low-code and no-code options for users to develop, deploy, and manage their machine learning models. It is a beneficial software option for less experienced users looking to author machine learning projects on an inclusive data science platform.
This product helps developers throughout each of the machine learning life cycle stages. It comes with features for data exploration preparation, model training and development, model validation and deployment, and continuous monitoring and management.
Azure ML Studio provides built-in tools and support for utilizing open source frameworks and libraries. Users can also access prebuilt container images to help them with their model validation and deployment.
Using the MLOps feature, users are able to deploy ML models at scale and then manage and govern them easily. In addition, Azure ML allows for models to be trained and deployed both on-premises and across hybrid and multi-cloud environments.
Users who are unfamiliar with coding languages can take advantage of the tool’s low-code and no-code options. Their simplistic drag-and-drop interface for model building and deployment makes the product ideal for customers seeking a more accessible machine learning tool.
The hybrid platform includes built-in governance, security, and compliance management features. Users can trace machine learning artifacts and even use custom policies to ensure their compliance.
The tool can detect data drive and debug models through error analysis. Its automated machine learning uses feature engineering and hyperparameter-sweeping to improve the accuracy of users’ models.
The product alone offers only a limited number of algorithms and preprocessing techniques for users to generate their ML pipeline. That being said, it is also costly compared to other options.
The product is offered in two pricing tiers: a free tool with limited capabilities or a standard option with more features. The standard plan is priced at US$9.99 per Machine Learning Studio workspace per month and US$1 per studio experimentation hour.
MLJAR AutoML is a Python package for automated machine learning with tabular data. This product can be incredibly supportive for those who wish to automate their machine learning pipeline to its fullest potential.
MLJAR AutoML automatically checks different combinations of the ML algorithms. This means users don’t need to worry about wasting their time by manually preprocessing their data, converting their data between different data formats, and other tedious processes.
The Automatic Exploratory Data Analysis framework enables users to visualize their data and save their results in Markdown or HTML reports for future reference.
The tool sets the ML experiments and tracks results with automatic feature engineering, hyperparameters tuning, model training, and documentation.
Users can train their models with the software’s advanced feature engineering to support the model’s performance. By utilizing the MLJAR AutoML package, users also gain access to the MLJAR AutoML framework with features to improve accuracy when building and stacking models.
MLJAR comes with advanced model ensembling techniques that users can apply to achieve predictive accuracy. Additionally, users can tune their ML models by selecting hyperparameters for their machine learning algorithms.
The “Compete” mode is a beneficial aspect of the software. It coordinates and stacks many different ML algorithms so as to achieve the best performing ML pipeline with the highest accuracy.
The framework uses “Golden Features” to reveal relationships in your data by applying mathematical operators. After this, the product uses the “Decision Tree” algorithm to assess the predictive power of newly generated features, ensuring that only top new features are included in the training data. This way, users can improve their pipeline accuracy by only using new and relevant numeric features.
Several online reviewers mentioned that the company could improve the product’s beginner-to-advanced path to allow users to progress more easily.
MLJAR AutoML comes in several self-hosted pricing plans. Hobbyists can use the OSS plan, which has limited features, for free. For a Commercial License with more functionality, plans start at US$199 per year for personal users, US$995 per year for small businesses, and US$9,500 per year for enterprise use.
XGBoost stands for Extreme Gradient Boosting and is an open-source machine learning software library. The product is ideal for users who require a scalable and flexible distributed product and are interested in using a gradient-boosted decision tree.
XGBoost provides parallel tree boosting to solve many data science issues quickly and accurately.
The XGBoost distributed software version is portable for use in various environments, including any platform that supports rebit. In addition, users can run it directly on Yarn, and the product can support Mesos and other resource allocation engines.
The XGBoost software library supports several languages so users can utilize it through various interfaces, including C++, Python, R, Java, Scala, and Julia. Because the model and data formats are exchangeable, models that are trained by one language can be loaded in another so that users can port models within their own systems.
Additionally, XGBoost supports classification, regression, ranking, and user-defined objectives, so users can apply the tool to many situations.
The solution can handle problems in data that fit within the user’s computer memory. However, users may need to take extra memory management steps to avoid issues with overfitting when working with larger datasets or more complex models.
XGBoost is a free, open-source tool.
What is the purpose of machine learning software?
Machine learning software enables systems to analyze data and adapt to improve the accuracy of predicted outcomes. Machine learning technology is unique in that it can “learn” about data to improve its operations without being directly programmed by humans. In turn, this technology can reveal insights that the business can use to improve its decision-making processes.
In most cases, machine learning software tools provide features that help users to develop, deploy, and manage ML models that will successfully carry out their intended purposes. These software systems use algorithms and data to build models that can reach conclusions autonomously. This branch of artificial intelligence is capable of supporting various use cases, which is why more and more businesses and organizations are harnessing machine learning software programs to help them develop models to predict future outcomes and improve their operations.
Machine learning software can help users develop their own ML models, simplify the ML life cycle processes, and achieve the intended outcomes.
What are the benefits of using machine learning tools?
These machine learning tools can be quite beneficial for users, by saving time and effort in the model development and deployment processes. Each machine learning tool provides a specific set of advantages to users, as the solutions all come with unique features included to simplify the steps required to create models.
Many solutions offer features that help with all stages of the machine learning life cycle. Machine learning tools with model building and training features help developers ensure the success of their models as they’re created and refined. Some machine learning tools can even help users prepare ML models for deployment and support post-deployment monitoring and management.
Experienced developers and inexperienced non-developers alike can benefit from machine learning tools, as many accessible resources make it possible for users to develop models without needing extensive knowledge of programming languages. This expands the use cases for machine learning and opens doors for users who want to leverage ML models without learning how to code.
Key features of machine learning tools
Key features to look for when selecting a machine learning tool include automation, data modeling, data preparation, model training, model deployment, and data visualization features.
Many machine learning tools have automated capabilities to streamline the ML workflow processes. For example, MLJAR AutoML has drag-and-drop capabilities which can make it easy for developers to automate processes within their ML workflows.
While some products are designed to help users with model training and deployment, products with data modeling capabilities can enable users to create their models directly within the systems. Of the tools covered in this piece, Pytorch, Microsoft Azure Machine Learning Studio, MLJAR AutoML, and XGBoost provide features for data modeling.
One of the ways machine learning tools prepare raw data for model training is by cleaning and pre-processing data so the models will produce accurate results. For example, the Microsoft Azure ML Studio has a Team Data Science Process that involves tasks like exploring, pre-processing, and cleaning data. It is also helpful for machine learning tools to be able to connect to multiple data sources for easy preparation and model training.
Machine learning tools that support model training will provide the optimal environment to allow users to train their ML models prior to their deployment. This can allow users to identify any bottlenecks and resolve issues before their ML model creation has been deployed. Vertex AI, for example, lets users train their machine learning models with custom settings that can improve their accuracy.
These products should support effective ML model deployment so the models can be successfully utilized for their intended purposes within the planned environments. These features help ML model deployment work and ensure that the final product is implemented without a hitch. For example, users can deploy PyTorch ML models at scale in the cloud.
Data visualization features can be helpful when creating machine learning models, as they can help users understand the way their data flows through their models and keep track of patterns and changes within their datasets. For example, MLJAR AutoML has an Automatic Exploratory Data Analysis framework that enables users to visualize and gain a deeper understanding of their data.
How to choose the best machine learning software
There is no one ideal machine learning tool for every situation, and the best method for choosing a machine learning solution depends on the needs of the business and individual users.
Those seeking a machine learning tool should consider their limitations to rule out products that will be unhelpful for their needs. For example, users who are less skilled with technical machine learning coding languages may benefit more from a product with no-code or low-code capabilities.
People who are interested in purchasing a machine learning software tool should consider other potential use limitations to rule out products that would not work for their organization. For example, say the tool they are considering does not have integrations to support the necessary data sources. In that case, that solution would not be able to capture some portion of their organization’s data in the machine learning model, and would therefore provide inaccurate insights.
Once potential buyers have ruled out the solutions that will not work for their machine learning development needs, they can assess products based on how helpful they would be for their ideal use cases. To do so, they should consider which aspects of the machine learning life cycle they would like their product to address, as some solutions contain features that focus on specific processes over others.
By identifying their priorities and finding products that will work with their needs, anyone can easily determine the best software solution for their ML modeling.
Interested in boosting your machine learning credentials? Read our Ultimate Machine Learning Certification Guide next.