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The 10 best AI tools of 2022 are as follows:
1. Microsoft Azure AI Platform: One of the best-known AI tools
Microsoft Azure AI Platform is the artificial intelligence and machine learning development platform offered within the overall umbrella of Microsoft Azure. It uses the considerable capabilities of the Azure cloud platform.
It offers a wide range of artificial intelligence (AI) capabilities like machine learning (ML), natural language processing (NLP), computer vision, knowledge mining, conversational AI, document process automation, machine translation, and speech transcription. The Azure AI Platform is good for both AI developers and data scientists.
Developers and data scientists can get the following benefits from using the Azure AI Platform:
- They can use specialized AI services to transform business processes and expedite development.
- Azure cognitive services provide access to robust AI models. This AI tool makes it easier to build and deploy AI systems. E.g., you can build portable ML pipelines.
- You can use popular programming languages like Python and R for ML development on the Azure AI Platform. The platform supports popular libraries like TensorFlow and PyTorch. You can create and deploy machine learning models at scale.
- You can access a large-scale AI infrastructure.
2. Vertex AI, which was known as the Google Cloud AI Platform earlier: One of the leading AI platforms
Vertex AI is the new unified ML platform offered by Google. You can think of it as the new and improved version of the Google AI Platform on the Google Cloud. Vertex AI continues to leverage the considerable cloud computing capabilities of the Google Cloud Platform.
You get the following advantages if you use Vertex AI:
- You can build, deploy, and scale ML models quicker.
- Vertex AI provides a unified artificial intelligence platform with custom tooling and pre-trained services.
- You can implement predictive models with fewer lines of code due to custom modeling and advanced tools.
- Developers can use machine learning algorithms and build portable ML pipelines easily, thanks to the robust tools.
- You can use the MLOps tools offered by Vertex AI to manage your huge datasets and models securely.
- The unified UI of Vertex AI helps you to manage the entire ML workflow.
- Vertex AI included pre-trained APIs for computer vision, video analysis, facial recognition, image recognition, natural language process, etc.
- It offers seamless integration with BigQuery, Dataproc, and Spark.
- Vertex AI supports important open-source frameworks like TensorFlow, Scikit-learn, PyTorch, etc.
3. Machine Learning on AWS: One of the prominent ML/AI tools
Among the cloud-based artificial intelligence tools on the market, “Machine Learning on AWS” is a key player. You get the following benefits:
- The capabilities to build, train, and deploy machine learning models quickly;
- A high-performance, cost-effective, and scalable ML infrastructure;
- Robust documentation for using the AWS ML and AI tools;
- AWS pre-trained AI services.
AWS pre-trained AI services enable you to develop intelligent applications without ML skills. The list of artificial intelligence services included in the AWS AI services includes the following:
- Amazon Comprehend: for advanced text analytics;
- Amazon CodeGuru: for automated code reviews;
- Amazon Lex: for chatbots and conversational interfaces;
- Amazon Forecast: for demand forecasting;
- Amazon Textract: for document analysis;
- Amazon Kendra: for natural language search;
- Amazon Fraud Detector: for fraud prevention;
- Amazon Rekognition: for facial recognition, image analysis, and video analysis;
- Amazon Personalize: for personalized recommendations;
- Amazon Translate: for real-time translation;
- Amazon Polly: for text-to-speech conversion;
- Amazon Transcribe: for speech recognition and speech-to-text capabilities.
4. H2O.ai: One of the well-known AI tools for data scientists and AI/ML developers
H2O.ai is an AI cloud platform of considerable reputation. This open-source platform expedites AI and ML projects in numerous ways. It offers the following advantages:
- You can develop AI and ML applications at scale. It offers capabilities like feature transformation and feature determination. You can use machine learning algorithms, and the platform offers explainable AI. It’s a low-code platform.
- H2O.ai offers a flexible architecture.
- You can manage ML workloads with the MLOps tools offered by H2O.ai.
- You can build, deploy, and distribute AI apps using the AI AppStore offered by H2O.ai.
- Developers can use the extensive documentation offered by H2O.ai.
- H2O.ai offers “H2O Driverless AI”, an automatic ML (AutoML) platform.
- It offers H2O Wave, an open-source Python development framework useful in data science and AI/ML projects.
- H2O supports all key statistical and machine learning algorithms like gradient boosted machines, generalized linear models, stochastic gradient descent, etc.
- You can use H2O Sparkling Water, which combines the ML algorithms of H2O and the big data capabilities of Spark.
5. IBM Watson: A leading artificial intelligence tool
IBM Watson is one of the leading artificial intelligence tools. This AI platform offers the following advantages:
- You can use IBM Watson for risk and compliance management. This platform offers robust data privacy and security, and it’s a good choice for highly-regulated industries.
- IBM Watson helps you to gather insights from unstructured data. While you might have several analytics tools at your disposal, such tools can deal with structured data. ML and AI tools like IBM Watson help you to analyze unstructured data too.
- IBM Watson helps you to bring in intelligent automation. Traditional automation tools offer rules-based automation for repetitive tasks. However, IBM Watson helps to transform your business operations with the power of AI and ML.
- You can use IBM Watson with all of the major cloud platforms like Azure, Google Cloud Platform, and AWS. Major corporations and small companies in all major industries have embraced cloud computing. They wouldn’t want to change their cloud provider only to use AI and ML. IBM Watson offers them the flexibility that they need.
6. The DataRobot Enterprise AI Platform: A leading enterprise-scale artificial intelligence tool
The DataRobot Enterprise AI Platform is an enterprise-grade AI platform. You can run it on a cloud platform of your choice. Furthermore, you can run it on-premises. DataRobot offers a fully managed service too.
It offers the following advantages:
- DataRobot is suitable for both AI/ML developers and data scientists.
- It provides comprehensive data engineering capabilities.
- You can use it to develop machine learning systems.
- The platform helps you to prepare data. You can build, deploy, manage, and optimize your AI/ML systems using the DataRobot Enterprise AI Platform.
- DataRobot provided enterprise-grade scalability and stability.
- It offers useful MLOps tools, which help IT and DevOps teams.
- DataRobot offers AutoML and a no-code app builder.
- The platform offers useful collaboration tools and an extensive learning center.
- It supports important use cases like insurance analytics.
7. Scikit-learn: One of the popular frameworks for AI application development
Coded using Python, Cython, C, and C++, Scikit-learn is a well-known machine learning library. It contains tested and standard implementations of numerous important machine learning algorithms, e.g.:
- Support Vector Machines (SVM);
- Random Forest;
- Nearest neighbors.
Creators of Scikit-learn built it on other important Python libraries like NumPy, SciPy, and Matplotlib.
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8. TensorFlow: One of the leading machine learning frameworks
The GoogleBrain team has created TensorFlow, a well-known machine learning framework. It’s built using Python, C++, and CUDA. It offers extensive advantages, e.g.:
- This open-source framework enables you to use ensemble techniques and other advanced capabilities.
- It supports numerical computations requiring a high-performance.
- Web and mobile developers find it useful since it offers robust architecture support.
- TensorFlow makes it easy to build machine learning models.
- It supports distributed training.
- You can deploy machine learning models easily.
9. Caffe2: A well-known framework for developing artificial intelligence applications
Caffe2 was a separate AI/ML framework earlier, however, the project team has since joined hands with the PyTorch project team. You can use PyTorch to access all of the capabilities of Caffe2.
You can use PyTorch to solve several real-life problems involving machine learning and data science. It supports distributed training. Major cloud computing providers support it, and there’s a rich ecosystem of tools.
10. Theano: A prominent tool for developing AI applications
Theano is a Python library. It’s based on NumPy, another popular Python library. You can use Theano to create, evaluate, and optimize mathematical expressions involving multi-dimensional arrays.
Many scientific research teams use Theano since it helps with large-scale numerical computation. Theano includes tools for unit testing and verification.
A brief note about the dynamic nature of the AI tools landscape
While the above-mentioned platforms and tools are important ones, the above list isn’t exhaustive. Innovative work in this field is frequent. Consider the following examples:
- Profound learning: Aisha AI Owais at the Sharjah Center for Astronomy and Space Sciences has researched about using AI to arrange pictures.
- Caffe: It’s a deep learning framework. Researchers in the Berkeley AI Research (BAIR) have created it. Developers at the Berkely Vision community contribute to its development.
- DeepInsight: This framework discovers and characterizes correlations between stimuli and behavior in unprocessed neural data.
Keras is a machine learning library that helps to implement neural networks. A great choice for deep learning, Keras can run on top of Theano and TensorFlow. Developers used Python to write the machine learning models in Keras.
Counterfeit Neural Systems use Artificial Neural Networks (ANNs). They can help in conducting data analysis of nonlinear connections among different sources of information.
TensorFlow, Caffe, Deeplearning4j, PyTorch, ONNX, MXNet, Chainer, and Keras are very good for creating deep learning applications. All of them help you to utilize deep learning algorithms.