What are AI Development Tools?

AI development tools help developers and organizations build artificial intelligence (AI) and machine learning (ML) systems at scale and speed. As the following examples illustrate, AI software development tools help software development teams and data scientists save effort and channel expertise effectively.

1. Microsoft Azure AI Platform

Microsoft Azure AI Platform is an AI development platform with infrastructure management and development tools to expedite your project. The software development tools offered in the Azure AI Platform help with the following:

• Managing the cloud computing resources for AI/ML development;
• Developing apps by utilizing AI capabilities like computer vision, robotic process automation, natural language processing, speech recognition, voice recognition, etc.;
• Creating machine learning workflows using the Azure Machine Learning Studio, an integrated development environment;
• Developing AI/ML systems using popular programming languages like Python and R;
• Building AI apps using popular AI frameworks like TensorFlow and PyTorch.

2. Vertex AI

Known as the Google Cloud AI Platform earlier, Vertex AI expedites artificial intelligence and machine learning development. Software tools offered in this platform help with the following:

• Building, deploying, and scaling machine learning models;
• Developing AI systems in multiple languages;
• Incorporating pre-trained AI services for computer vision, video analysis, optical character recognition, natural language processing, etc.;
• Utilizing distributed computing via Dataproc, BigQuery, and Spark on Vertex AI;
• Self-service provisioning and management of computing resources for AI/ML development.

3. Machine Learning on AWS

“Machine Learning on AWS” is an AI platform that expedites ML development and provides pre-trained AI tools. Software development tools offered by this platform help with the following:

• Developing deep learning systems with artificial neural networks (ANNs);
• Enabling data scientists, business analysts, and data engineers to build, train, and deploy machine learning models;
• Managing computing resources like servers and operating systems easily, thereby allowing software engineers to focus on their work;
• Integrating AI capabilities like text-to-speech conversion, speech recognition, facial recognition, natural language processing, etc.

4. H2O.ai

H2O.ai is one of the artificial intelligence tools that help to build, deploy, distribute, and manage AI software. AI software development tools in this platform help in the following ways:

• You can develop ML systems by using well-known algorithms like stochastic gradient descent, generalized linear models, and neural networks.
• Software engineers can build, deploy, and manage AI/ML systems easily, thanks to the flexibility of the underlying architecture.
• You can develop AI apps in Python, thanks to AI frameworks like H2O Wave.
• Developers can use robust data preparation tools.

5. IBM Watson

IBM Watson is one of the AI development tools that can be used with different cloud platforms. It helps with the following:

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• Building AI/ML-powered systems for highly-regulated industries;
• Gathering important customer intelligence or other insights from unstructured organizational data;
• Implementing intelligent automation;
• Implementing deep machine learning and neural net models.

6. DataRobot Enterprise AI Platform

You can run the DataRobot Enterprise AI Platform on a cloud platform of your choice, alternatively, you can run it on-premises. DataRobot is also suitable for business environments that need AI development tools as fully-managed services. It helps with the following:

• Implementing data engineering and data science expertise in AI/ML projects;
• Streamlining the AI/ML software development process;
• Building, deploying, and optimizing machine learning models;
• Data preparation for ML projects;
• Managing machine learning models with MLOps tools;
• Collaboration via cross-team toolkit;
• Building scalable and stable AI/ML systems.

7. Scikit-learn

A popular library that’s written in Python, Cython, C, and C++, Scikit-learn provides you with tested and standard implementations of machine learning algorithms.

You don’t need to code ML algorithms from scratch. This helps you to channel your efforts to multiple tasks that need expertise. For example, you can easily implement the following well-known machine learning algorithms:

• Naïve Bayes Classifier;
• K-Means Clustering;
• Support Vector Machine (SVM);
• Linear Regression;
• Logistic Regression;
• Decision Tree;
• Random Forest;
• Stochastic Gradient Descent;
• Generalized Linear Models;
• Linear Algebra Routines.

8. TensorFlow

TensorFlow, a machine learning framework helps you to build and deploy machine learning models at scale and speed. TensorFlow supports distributed computing. It helps with the following:

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• Using advanced capabilities like ensemble techniques;
• Running heavy-numerical computations that require a high performance;
• Building machine learning models;
• Incorporating distributed training in ML projects;
• Deploying ML models.

9. Caffe2/PyTorch

Caffe2 and PyTorch were separate machine learning frameworks, however, PyTorch now combines the capabilities of both. It helps you to solve real-life problems with the help of a variety of machine learning methods.

PyTorch supports the following:

• Numerical computations with high-performance requirements;
• Distributed training;
• Building and deploying machine learning models;
• Developing applications with AI capabilities like computer vision and natural language processing;
• Development and deployment on major cloud platforms like AWS, Google Cloud Platform, Azure, etc.;
• Incorporating ML in mobile applications;
• Exporting models in the standard ONNX (Open Neural Network Exchange) format;

10. Theano

A Python library, Theano helps with workloads involving large-scale numerical computation. These could include linear algebra routines, multi-dimensional arrays, etc. Theano provides the following:

• Support for large-scale numerical computations;
• Tools for unit testing and verification;
• Support for stability optimization;
• The ability to optimize the execution speed;
• The capability to symbolic graphs automatically, which helps to compute gradients;
• Ability to define, optimize, and evaluate various mathematical expressions involving multi-dimensional arrays.

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