10 Popular Machine Learning Tools

Are you interested in machine learning tools?

The global machine learning market is expected to reach $209.91 billion by 2029. Such statistics show that businesses are exceedingly relying on machine learning algorithms to get a better understanding of their customers and market trends. 

Machine learning algorithms for various tasks, like predictive analytics, sentiment data analysis, etc., can be run efficiently on machine learning software tools.

Most of these machine learning software do the complex backend processing of building and running complicated neural networks. Users can input their dataset, instantly build ML models using built-in libraries, and do the required processing.

Let’s explore the top machine learning software tools in the market.

Popular machine learning Tools

Following are the 10 most popular machine learning tools in the market available today.

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit or CNTK is an open-source software tool kit for implementing distributed deep learning algorithms. 

Popular machine learning models, like recurrent neural networks, feed-forward neural networks, convolution neural networks, etc., can easily be realized in the form of directed graphs. 

Microsoft Cognitive Toolkit can be included as a library in popular programming languages like C++, Python, C#, etc. Microsft provides an extensive API library to use CNTK for the composition, training, and evaluation of machine learning models.

IBM Machine Learning

IBM machine learning suite offers multiple machine learning tools like IBM Watson Studio, IBM Machine Learning, IBM Watson OpenScale, etc. AI developers can build complex machine learning models using these open-source tools, deploy them on user devices, and monitor their performance. 

IBM Watson Studio and IBM Cloud Pak for Data help users run end-to-end machine learning models on an AI platform.

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IBM offers interactive learning tutorials to get started with their extensive machine learning software toolkit.

Amazon Machine Learning

Amazon offers multiple machine learning tools to implement, deploy, and analyze complex machine learning models in the cloud Amazon web services (AWS) setup. 

AWS SageMaker helps to quickly run machine learning algorithms via an interactive user interface. Users can also use other popular machine learning toolkits like Tensorflow and Keras and datasets like IMDB.

AWS Transcribe is a deep learning-based automatic speech recognition system for efficient speech-to-text conversion. 

Similarly, Amazon Rekognition can be used to automate the analysis of image and video data. Amazon comprehend helps in natural language processing, like finding relationships in the text data.

Amazon machine learning is best for users already investing in the Amazon cloud infrastructure to implement AI models on natural language processing, image recognition, etc.

 Large datasets can be used on multiple Amazon servers with easy scalability. Amazon user console helps to visually track machine learning models and adjust them as required. 

Amazon offers extensive documentation to start using their machine learning software toolkits for building, deploying, and maintaining sophisticated machine learning models efficiently.

Google Vertex AI Platform

Vertex AI by Google provides a unified platform consisting of Google AutoML and AI Platform through an API, client library, and a user interface. 

AutoML assists in running machine learning models on text, video, and image datasets with minimum AI expertise and without writing complex machine learning code. Custom coding of ML models is possible through the Google AI platform. 

Vertex AI combines both these services as a unified API. Users can build custom models, utilize automated modeling, make predictions, etc. 

Google provides various tools to interact with Vertex AI like Google Cloud Console with cloud logging and monitoring features to deploy ML models on the cloud and manage datasets, models, etc., through a console. 

Vertex AI also provides cloud client libraries to connect with Vertex AI in different programming languages. 

Deep learning VM images by Google are optimized virtual machine images for advanced machine learning tasks. Developers can use them with GPUs to accelerate extensive data processing tasks. 

Google also provides deep learning Docker containers with pre-installed ML frameworks, libraries, and development tools. Such containers provide an optimized and consistent environment for implementing machine learning models quickly.

Google provides detailed documentation on setting up development environments and getting started with Vertex AI.

H20.ai

H2O.ai is a leading AI platform with extensive support for big data processing. It was created by three researchers from the California university. Today, there is a large developer community supporting this open-source AI platform.

Businesses can use H2O.ai to efficiently build ML models and apps, monitor and manage deployed models, innovate efficiently according to the changing conditions, etc.

 Flexible architecture helps AI engineers and data scientists in distributed processing, optimized computations, and model deployment in multiple production environments. All ML models can be easily customized through easy integrations. 

H2O.ai is offering remarkable services in numerous industries, including Government, Health, Marketing, etc. Common data analytics models for price optimization, anomaly detection, etc. can be built and monitored using the H2O.ai platform.

H2O sparkling water solution offers a quick solution to integrating with Sparks. Data queries can be made to Spark SQL, results can be input into H2O.ai for creating and training ML models, and final results can again be returned to Spark.

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H2O.ai provides a comprehensive introduction to their machine learning software toolkit on their website. However, developers feel their development documentation can be improved, and further support for more complex machine learning algorithms will make this AI platform more suitable for every industry.

Azure Machine Learning

Azure machine learning is popular among AI professionals and data scientists to handle machine learning workflows like building and deploying models and managing MLOps.

ML engineers can build custom machine learning models and also make use of pre-built models from open-source software like Tensorflow, sci-kit learn, etc., using the Azure ML platform.

Azure machine learning software toolkit is for businesses looking to deploy machine learning models in their organization workflows and production environments. 

Engineers can automate several tasks and also integrate ML models into their services. Azure provides a number of tools to achieve this, like Azure resource manager APIs, Azure machine learning studio, Azure CLI, etc.

Azure machine learning studio is a graphical user interface that helps ML engineers to manage and monitor MLOps. Users can view logs, and metrics, manage computation environments, visualize results and ML pipelines, etc. There is also a drag-and-drop interface to build and train machine learning models efficiently.

There is also an option to integrate Azure machine learning with Azure cloud for advanced security in ML operations. You can save your credentials in the Azure key vault. 

Other Azure services to support an end-to-end machine learning lifecycle include: Azure Synapse Analytics to process data with Spark, Azure Arc to process models in Kubernetes environment, various storage options like Azure SQL, Azure App to manage machine learning applications, etc.

Azure also provides detailed documentation on how to use the Azure machine learning tool for your ML project.

Tensorflow

TensorFlow is a software toolkit for end-to-end machine learning lifecycle projects. It is an open-source core library to build, run, and analyze machine learning models. TensorFlow Lite makes it easy to deploy models on mobile and IoT devices. 

You can create a new model or use an existing one, convert them into a compressed flat buffer, and deploy the compressed file into an embedded device. There are also options to optimize model training and execution, like converting 32-bit floats to 8-bit integers, etc.

Image classification, object detection, natural language processing tasks like question answering, etc. can be performed efficiently using TensorFlow and TensorFlowLite. 

TensorFlow also offers a TensorFlow.js library to deploy and manage machine learning models using popular JavaScript language. TensorFlow Extended (TFX) provides production-ready machine learning pipelines to deploy and train models quickly in production environments.

TensorFlow offers a free ML toolkit for streamlined and user-friendly model training and inference processes. It also provides GPU support, and extensive library support for many machine learning tasks for image, video, and text datasets. 

TensorFlow is available for Python, C++, Java, etc., and provides tutorials on getting started with machine learning models using TensorFlow resources.

Keras.io

Keras is an open-source library running on machine learning backend platforms like TensorFlow, Google Cognitive Toolkit, Theano, etc. Keras helps to build deep learning models quickly. Various optimization features of Keras enable building high performant and complex deep neural networks efficiently. 

Keras offers a simple and extensible API, supports several platforms, and works well with GPUs providing high computation scalability when required.

Keras also has an extensive developers community. Keras neural networks are developed in Python which is the most commonly used programming language for machine learning models. The Keras API is easy to use and can be used for various types of neural networks including, recurrent neural networks and convolution neural networks.

Keras models can be exported to a JavaScript environment to run directly on browsers and to TensorFlow Lite to run on mobile and embedded devices. 

Keras is an important component of the TensorFlow 2 ecosystem and helps with efficient data management, hyperparameter optimization, and deployment of machine learning models. 

API documentation and guides help users quickly get started with the Keras machine learning software toolkit.

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Theano

Theano is a Python machine learning library to help with complex mathematical computations in machine learning modeling. It is built on of Numpy library and provides multiple features to efficiently define, optimize, and analyze mathematical expressions like evaluating multi-dimensional arrays.

It provides optimized GPU support, efficient computation of derivatives for functions with one or more inputs, dynamic generation of C code, unit testing and verification routines, etc.

Apache Spark MLlib

MLlib is an Apache Spark machine learning library. It can be used with Java, Python, Scala, and R. It is also easy to integrate with Hadoop workflows. Spark offers efficient iterative computation which enables MLlib to run ML models faster. 

Apache MLlib can be used against different data sources and in different environments, including Hadoop, Kubernetes, cloud, etc. 

Apache Spark MLlib offers extensive support for machine learning algorithms, including classification, regression, decision trees, recommendation systems, clustering, etc. 

Some machine learning utilities provided by Apache MLlib include feature engineering, machine learning pipeline development, saving and loading of ML models, principal components analysis, model statistics, hypothesis testing, etc. 

 MLlib can be used to deploy machine learning models in distributed mode on clusters and even locally on a multi-core machine without any extra setup.

Moreover, the community for MLIib is huge. Being a part of the Apache Spark project, MLlib gets tested with every Apache Spark release.

You can refer to MLlib documentation to learn more about using these machine learning resources by Apache Spark.

Planning to use a machine learning software toolkit for your next machine learning project?

Machine learning toolkits help to efficiently build and deploy machine learning models and integrate machine learning pipelines into your business applications and services. The benefits of using modern machine learning services are many.

If you need to make better sense of the huge amount of data your business processes are collecting daily, then there is no reason not to invest in machine learning technology.

However, there are many machine learning tools available in the market. Being a developing technology, it requires certain expertise and in-depth knowledge of the underlying technology to effectively customize such tools according to your business requirements and utilize them effectively for your business growth.

Read our blog here for more details on how to build machine learning algorithms and another one on what is the future of machine learning.

Therefore, you will need competent data scientists and professionals with machine learning skills in your project team for building machine learning models successfully and managing them efficiently.

We would advise you to partner with a reputable software development agency with software developers experienced in building and deploying market-competitive machine learning solutions.

DevTeam.Space can help you here with its field-expert software developers community. You can easily outsource these vetted and experienced software developers according to your development project requirements.

Simply write to us your initial project specifications, and one of our account managers will get in touch with you to discuss further details.

Top FAQs on popular machine learning tools