7 Best Machine Learning Software of 2022 (Ultimate Guide)
With an increase in the number of big data tools available on the market, along with more generated data every single day, more businesses are finding that the right machine learning tools are a must.
In addition to choosing the right tool for your day-to-day operations, understanding how machine learning can help in various business aspects is important
For example, machine learning helps when it comes to security information and event management tools (SIEM), as these go hand in hand with machine learning software.
Being designed to help business analysts detect, investigate, and respond to security threats in real-time, SIEM tools ensure your business runs smoothly.
With that in mind and to help you choose the right software, I have compiled a list of the best machine tools available right now.
These tools are designed to meet the needs of different businesses, allowing you to pick the one that’s just right for you.
Let’s get started.
The following is a list of the best machine learning software for businesses of all sizes.
Cnvrg provides you with everything you need to build, train and deploy machine learning models.
A full-stack machine learning operating system makes it easy to get started with machine learning.
You have the option to choose the best infrastructure depending on the task you are working on – whether it’s deploying models in the cloud or on-premises.
You can also mix and match your infrastructure, making it a perfect choice for businesses of all sizes.
You can also connect your storage and data processing systems for faster and easier training of your models.
If you want to maximize workload performance and get the most out of your data, cnvrg is an excellent choice.
- Hybrid & multi-cloud solutions mean that you can use the best of both worlds
- Integrates with lots of other software for an even better experience
- Unify code to make it easier to use and understand
- Combine your computer resources to make the most of your data
- Automate, track and monitor your machine learning for better results
With a free community edition, the pricing plans are then as follows:
- Starter: $249 billed monthly
- Advanced: $349 billed monthly
- Pro: Get in touch with them for a custom plan
KNIME has deep learning extensions that make it perfect for businesses to dive into deep learning.
It also has a wide range of connectors, making it possible to import data from various sources.
These allow users to edit, train, execute and deploy models in just a few clicks.
TensorFlow Integration opens the door to deep learning, making it possible to use pre-trained models or create your own.
Detailed documentation is a perfect choice for businesses that want to get started with machine learning.
- Users can build deep learning networks to train on custom datasets using the Keras layer nodes.
- Pre-trained models can be used to get started immediately.
- Controlling access rights to shared workflows is also possible so that different users can have different access levels.
- Automating tasks through workflows makes it possible to repeat the same job multiple times without doing it manually.
- Create and deploy guided analytics to ensure that the user gets the most out of their data.
You’ll have to contact their sales team to get a quote for your company.
Keras is considered a simple yet flexible and powerful tool for deep learning.
If you want to iterate at fast speeds, it might just be the right toolkit for you.
The ability to run new experiments as quickly as you can code them makes it perfect for businesses that don’t want to waste any time.
Built on top of TensorFlow 2, it provides industry-strength deep learning algorithms that can scale to multiple GPUs or CPUs.
You can also deploy models anywhere, thanks to its full deployment capabilities.
These make it perfect for businesses that want to run their models directly in the browser or even on Android, iOS, and other devices.
With a vast ecosystem of scientific organizations using Keras, you can ensure that support is there when you need it.
- There is a lot of interest in Keras in the industry and research community, meaning that you can always find someone to help when you need it.
- Keras provides excellent multi-GPU support so that you can train your models faster.
- Turn ML models into products with ease by using the Keras API to deploy them in a wide variety of ways.
- Keras provides low-level flexibility to implement any research concept but can also use its vast range of features to increase the number of experiments conducted.
Keras is an open-source machine learning Python library that is free to use.
Anaconda brings data science technology to businesses of all sizes.
Providing a competitive edge with machine learning, predictive analytics, and data engineering, Anaconda can scale to meet the needs of any business.
Anaconda also offers a wide variety of features that one can use to increase productivity and collaboration.
You can work with open-source packages and libraries, which gives you the freedom to use the best tools for the job.
Built by data scientists for other scientists, it can help your business get the most out of machine learning while tackling challenges such as data preparation, modeling, optimization, and more.
- Increase your data science by using a wide range of deep learning libraries and tools.
- Use deep neural networks to outperform traditional machine learning methods.
- Compete with Amazon or Google when implementing machine learning at scale.
- Achieve predictive analytics and data engineering to get an edge on your competition.
- Have your data strategy managed for you, ensuring that you can get the most out of the process.
- Stay safe and secure while observing compliance and regulations.
Contact the team at Anaconda for a quote.
H2O provides an AI Cloud Platform that businesses can use to get the most out of their machine learning.
With a wide range of features, it’s perfect for businesses who want to get started with machine learning without worrying about the infrastructure.
With more than 20,000 global organizations using H2O, you can be sure that you’re in good company.
It also has a wide range of features that one can use to increase productivity and collaboration.
If you want to maintain the proper levels of transparency, speed, and accuracy in your team, H2O is the perfect tool.
- Mitigate your risks by using a platform that has been battle-tested in the real world.
- Research new opportunities with a platform with a wide range of features for data science.
- Streamline performance monitoring, machine learning, and more with a platform built for collaboration.
- Provide end-users with innovative self-service data preparation and modeling tools.
- An AI AppStore offers a range of popular machine learning algorithms. This means that you can be up and running quickly with the right tools at your disposal.
Test out H2O AI Cloud with a 90-day free trial.
Google Cloud AI Platform provides all the tools you need to start machine learning.
Whether your team involves data scientists, developers, engineers, or business users, Google has a solution to fit your needs.
You can use a wide variety of open-source languages and tools on the platform, so you’re not limited in terms of what you can do.
Taking your ideas from prototype to production is easy with the Google Cloud AI Platform so that you can get the most out of your machine learning.
Also, when it comes to scaling, no one can beat Google. With the ability to handle large amounts of data, you can ensure that your machine learning will keep up with your needs.
Various customization options make it easy to set up the platform to best suit your needs.
- Prediction service lets you deploy trained models in the cloud for batch or online predictions.
- Rapidly train and deploy models with managed services and APIs.
- Scale up to handle large amounts of data with ease.
- Use a wide variety of open-source languages and tools on the platform.
- Submit your information and allow the AI Platform to take care of the infrastructure for you.
Check out the Google Cloud pricing list for more information.
Azure Machine Learning is made for data scientists who want to build, test, and deploy machine learning models quickly and confidently.
Time to value is essential, and Azure Machine Learning delivers that with a wide range of features that can get you up and running quickly.
You don’t need to be an expert in machine learning to get started – Azure Machine Learning takes care of the infrastructure for you.
Focusing on security and governance, Azure Machine Learning ensures that you can trust the platform with your data.
With the ability to use studio development experience to train models that can be used in the cloud or on-premises, Azure Machine Learning is an excellent choice for those who want to have the most flexibility.
- Operationalize at scale by deploying to the cloud or on-premises.
- Deliver reliable solutions with built-in security and governance.
- Use model interpretability to diagnose problems and improve results.
- The machine learning framework supports a wide range of languages.
- Manage labeling projects that help with data preparation.
- Automate iterative tasks by using the proper scripts to focus on the model.
You can either try Azure for free or request a quote to get started.
Other machine learning software not mentioned in this article include Apache Spark, Hadoop, Project Jupyter, Apache Mahout, Matlab, and Scikit-learn.
Machine learning software tools help you to build and deploy machine learning models.
These models use artificial intelligence (AI) to predict future events, trends, and relationships.
Machine learning models can be used for various purposes, such as improving customer experience, fraud detection, or predicting stock prices.
The best machine learning software tools offer a wide range of features and are easy to use. They also provide a good level of performance and reliability.
While tons of machine learning tools are available in the market, not all are created equal. Before selecting a machine learning tool, it is essential to consider the following factors:
The tool’s ease of use can vary in terms of how easy they are to use.
Some tools have a steep learning curve and can be challenging to learn.
Others are more user-friendly and easy to understand.
While beginners can use some tools, most machine learning tools need some expertise to use.
If you are a beginner, it is essential to choose a tool with good support and tutorials to get help when needed.
If you are an expert, look for a tool that offers a wide range of features and allows you to create complex models.
Not all machine learning tools allow you to create all kinds of machine learning models.
Some tools focus on creating specific models, such as deep learning models or regression models.
The right machine learning platform for you depends on the type of models you want to create.
Machine learning tools can vary in terms of their performance and reliability.
Some tools are faster and more reliable than others.
It is essential to choose a tool that has a good track record and offers good performance.
The top machine learning tools can vary in terms of the available level of support.
Some tools have a good level of support, while others do not.
Choosing a tool with good customer service is essential to get help when needed.
Machine learning tools can vary in terms of their cost.
Some tools are free to use, while others have a paid subscription model.
It is essential to choose a tool that is affordable for your needs.
As I mentioned, not all machine learning platforms are created equally.
Here are some of the critical factors that the tool of choice should have.
Tools that use natural language processing (NLP) make it easy to work with text data.
They allow you to interact with the tool using regular language, making them easy to use.
Look for a tool that uses NLP if you want an easy-to-use platform.
Machine learning tools can vary in terms of the amount of automation they offer.
Some tools allow you to automate common tasks, such as pre-processing data or scoring models.
It’s a good idea to consider a tool that offers good automation features so you can save time and focus on the model building process.
Machine learning tools can also vary in their data mining and visualization features.
Some tools allow you to explore your data, while others do not efficiently.
Choosing a tool with good data mining and visualization features is essential to understanding your data quickly.
The correct programming language (Java, Python, Scala, etc.) or a version of these languages (such as Pytorch, Weka, Torch, or Numpy) will make it easy to get started with machine learning.
These have a variety of use cases, and each assists the user in various ways.
Tools with a distributed linear algebra framework can use multiple processors or cores to speed up the training process.
If you are working with large data sets, look for a tool that has a distributed linear algebra framework to improve performance.
An integrated development environment (IDE) allows you to write code, debug code and run programs all in the same place.
It makes it easy to develop and test your predictive modeling and signal processing through their user interface.
Look for a tool that has an IDE if you want an easy-to-use platform.
Tools with a deep learning framework allow you to create deep learning models.
This means that you can use the tool to create models based on multiple data layers.
Having suitable data structures and management tools is essential for machine learning.
Choose a tool with good data management features to easily work with your data.
Tools that have GPU support can take advantage of the power of graphics processing units (GPUs) to speed up the training process.
If you want to create computationally intensive models, look for a GPU-supported tool.
Machine learning applications use large data sets.
It is essential to have the right data analytics tools to understand such data.
Machine learning is a complex process, and it can be helpful to have access to a community of experts when needed.
The correct data analysis can only happen when your tool uses the right data.
Make sure the machine learning tool you choose can provide training data so you can get started immediately.
These libraries, which come as standard in the top machine learning tool, can help you quickly build a machine learning model without writing any code.
Look for a tool that has a variety of preset machine learning libraries so you can find the right one for your needs.
Yes, products such as Microsoft Azure, Amazon AWS, and IBM Watson Machine Learning are all examples of machine learning-related products created by big companies.
Yes, some machine learning tools have a graphical user interface to build and test models without writing code quickly.
One can do this through drag and drag functionality.
Computer vision is the ability of a computer to interpret and understand digital images.
One can use this for facial recognition or automatic object detection tasks.
A decision tree is a way of representing a decision problem as a tree-like structure.
The leaves of the tree are the decisions, and the branches represent the conditions that need to be met for a decision to be made.
Pipelines: A pipeline is a set of processing steps that take input data and transform it into output data.
Modules: A module is a self-contained unit of code that performs a specific task.
Graphs: A graph is a data structure that consists of nodes (vertices) and edges.
Machine learning is a fascinating field that is constantly evolving, and the right machine learning tool can help you succeed in this field.
Make sure to choose a tool with the features you need to build robust models quickly and easily.
This will make it easier to get started and achieve higher returns over time.
To recap, the best machine learning software on the market are:
- Cnvrg.io: Best Overall For Machine Learning Models.
- KNIME: Best For Importing Third-Party Data.
- Keras.io: Best For Simplicity And Quickness In Deep Learning.
Have you ever used machine learning?
If so, what tool did you use, and what results did you experience?
Let me know in the comments below.
Further reading on AdamEnfroy.com: Want to learn more about the direction in which machine learning is headed?
Check out this list of machine learning statistics for more information.
Also, this list of the best business intelligence tools can help you get started with data analytics in your organization.