The Best Artificial Intelligence Software Development Tools Of 2022
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Which are the best artificial intelligence software development tools?
I will now describe the best artificial intelligence software development tools, which are as follows:
1. Microsoft Azure AI Platform
As a cloud platform, Microsoft Azure hardly needs an introduction. Azure has made significant progress with its capabilities, and the Microsoft Azure AI Platform is a popular choice for AI development.
Features and capabilities of the Microsoft Azure AI Platform
The Azure AI Platform offers all key AI capabilities, e.g.:
- Machine learning (ML);
- Vision capabilities like object recognition;
- Speech capabilities like speech recognition;
- Language capabilities like machine translation;
- Knowledge mining.
The ML capabilities of the Azure AI Platform include the following toolkits:
- Azure ML, which is a Python-based automated ML service;
- Azure Databricks, which is an Apache Spark-based big data service that integrates with Azure ML;
- ONNX, which is an open-source model format and runtime for ML.
Why you might want to the Microsoft Azure AI Platform in your business
You might want to use the Microsoft Azure AI Platform for AI development due to the following reasons:
A. Extensive documentation
Users of the Microsoft Azure AI Platform can access extensive developer resources and documentation. Read the Azure Search documentation here.
B. Customizable apps and agents
The Azure AI Platform offers AI apps and agents, and you can customize them for use in your application. This includes Azure Cognitive services, which offer a wide collection of domain-specific pre-trained AI models. The Azure Cognitive Services include AI models for the following:
· Language; You can find the documentation for the Azure Cognitive services here.
C. The ease with which you can create bots
There is a development environment for creating bots, and the Azure AI Platform has templates for bots. This expedites your development, and you can learn about the Azure bot services in this tutorial.
D. The ease of developing Machine Learning algorithms
It is also very popular for developing new open-source machine learning algorithms and software solutions.
E. Support for popular open-source frameworks
Azure ML works with popular open-source AI frameworks like TensorFlow. You can find the Azure ML service documentation here, moreover, the Azure Databricks documentation is available here.
F. Knowledge-mining capabilities
The Azure AI Platform has knowledge mining capabilities, and you can unlock insights from documents, images, and media using it. This includes the following:
· Azure Search, which is a cloud search service with built-in AI;
· Form Recogniser, which is an AI-powered extraction service to transform your documents and forms into usable data.
G. The speed with which you can deploy the web services that you have developed
The Microsoft Azure AI Platform offers robust tools to implement web services. You can develop your Machine Learning models. Subsequently, you can deploy them as web services.
H. Cross-platform integration
Azure offers cross-platform integration capabilities. You can access your data across a wide range of platforms. The Azure AI platform supports popular programming languages like Python.
You can build enterprise-scale AI and ML systems using the Azure AI platform. The scalability and speed of Azure make life easier for companies.
J. Community support
Developers using the Azure AI Platform can take advantage of its vibrant community support.
2. Google Cloud AI Platform
Google is yet another cloud computing giant that offers its AI platform, and you can access it here.
Features and capabilities of the Google Cloud AI Platform
The Google Cloud AI Platform offers all the key AI capabilities, e.g.:
With the Google Cloud AI Platform, you can easily develop your ML project and deploy it to production. The Google AI platform provides an integrated toolchain for this, which expedites the development and deployment.
With this platform, you can build portable ML pipelines using Kubeflow, which is an open-source platform from Google. You can deploy your ML project either on-premise or in the cloud. Cloud storage and BigQuery are the prominent options to store your data. You can access popular AI frameworks like TensorFlow.
The Google Cloud AI Platform offers pre-configured Virtual Machines (VMs) for creating deep learning applications. You can provision this VM quickly on the Google Cloud, and the Deep Learning VM image contains popular AI frameworks.
You can launch Google Compute Engine instances where TensorFlow, PyTorch, scikit-learn and other popular AI frameworks are already installed. Find out more about the Google Deep Learning VM here.
Natural Language Processing (NLP)
The Google Cloud AI Platform has NLP capabilities, and you can use it to find out the meaning and structure of the text. You can use the Google NLP capabilities to analyze text, and the Google NLP API helps with this. It’s a RESTful API, and you can access the API and its documentation here.
The Google Cloud AI Platform has APIs for speech-to-text and text-to-speech capabilities.
Its speech-to-text API can help you to convert audio to text, and it uses neural network models and vast datasets for this. The speech-to-text API supports 120 languages and their variations.
With its speech recognition capabilities, you can enable voice command-and-control features in your app, moreover, the app can transcribe audio. You can access the Google speech-to-text API here.
On the other hand, the Google text-to-speech API enables you to create a natural-sounding speech from text. You can convert texts into audio files of popular formats like MP3 or LINEAR16. Access this API here.
Vision is another key capability of the Google Cloud AI Platform, and you can use this to derive insights from your images. The Google Cloud AI Platform offers its vision capabilities through REST and RPC APIs, and these APIs use pre-trained ML models.
Your app can detect objects and faces, moreover, it can read printed and handwritten texts using these APIs. You can access the Google Vision AI APIs here.
Why companies and developers might want to use the Google Cloud AI Platform
You might want to use the Google Cloud AI Platform due to the following reasons:
A. The ease with which you can deploy ML models
The Google Cloud AI Platform has the “AI Hub”. This catalog of reusable ML models makes it easy for you to discover and share ML models.
AI Hub has models based on established frameworks like TensorFlow, PyTorch, and Keras. You will find it easy to search this catalog, furthermore, you can deploy these models easily.
B. The speed at which you can deploy Deep Learning VMs (Virtual Machines)
The Google Cloud AI Platform offers Deep Learning VM images. You can easily and quickly deploy a VM image on the cloud, and the VM has the important Deep Learning and Machine Learning frameworks. The Google Cloud AI Platform provides VM images that have the necessary software.
C. The ease of deploying ML workflows
You can use Kubeflow Pipelines to deploy ML workflow easily on Kubernetes. Kubeflow is a tool designed especially for Kubernetes. Your process to deploy ML workflows becomes scalable and simple, thanks to Kubeflow Kubeflow Pipelines helps you to deploy ML workflows based on Docker containers.
D. You find support for popular frameworks
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Developers in your organization might use popular frameworks for various tasks like data analysis, developing training jobs, etc. Frameworks like TensorFlow, Keras, PyTorch, Scikit-Learn, and XGBoost are very popular with developers. Google Cloud AI Platform supports these frameworks, which helps developers focus on their job.
E. The ease of creating enterprise-scale AI systems
You might need to enhance your proposed AI system in the future to incorporate new capabilities. The cloud capabilities of the Google Cloud AI Platform make that easier for you. It offers enterprise-scale performance, scalability, security, and tools. You also get excellent documentation and support.
3. IBM Watson
IBM, the technology giant has advanced AI capabilities, and IBM Watson is quite popular. There are already IBM Watson AI solutions specifically tailored for several industries like healthcare, oil & gas, advertising, financial services, media, Internet of Things (IoT), etc.
Features and capabilities offered by IBM Watson
IBM Watson offers the following features and capabilities:
- It has developer tools like SDKs and detailed documentation for them.
- You can integrate Watson Assistant to build AI-powered conversational interfaces into your app.
- With IBM Watson, you can get Watson Discovery. It’s an AI-powered search technology, and it can help your app to retrieve information that resides in silos.
- IBM Watson has Natural Language Processing (NLP) capabilities, and it’s known as Watson Natural Language Understanding (NLU). The IBM Watson developer platform includes this.
- You can also make use of the IBM Watson Speech to Text capabilities when you build on the Watson developer platform.
Why businesses might develop AI systems using IBM Watson
IBM Watson offers the following to expedite your AI app development:
A. An open platform
A key advantage of IBM Watson is that developers can use this platform to build their AI applications. It’s an open AI for any cloud environment, and it’s
pre-integrated and pre-trained on flexible information architecture. This will expedite the development and deployment of your AI application.
B. Documentation You can access the IBM Watson developer platform here. The starter documentation for this platform is available here, and you can access the APIs and SDKs here.
C. SDKs and developer resources
IBM Watson developer resources can be useful for your AI app development team, and you can access them here. There are SDKs for Swift, Ruby, Java, Python, Node.js, .NET, etc., therefore, you will likely find a suitable SDK for your project.
D. The capabilities to process unstructured data
You can no longer depend on structured data only to get important insights. Organizations increasingly need to derive actionable insights from unstructured data. IBM Watson helps significantly here.
E. The ease of implementing intelligent automation
IBM Watson helps to implement intelligent automation. This helps to eliminate the limitations of humans in the organization.
F. The capabilities to process massive data sets
You can process large data sets at scale with the help of IBM Watson. This can be especially helpful for enterprises.
4. Infosys Nia
Infosys Nia is an AI platform that allows you to build AI-powered apps.
Features and capabilities of Infosys Nia
Infosys Nia offers the following capabilities:
- Machine Learning: Nia Advanced ML offers a broad range of ML algorithms that operate at speed and scale. It makes building high-performing ML models easier, and you can read more about it here.
- Contracts analysis: Nia contracts analysis capability includes ML, semantic modeling, and deep learning, and you can learn more about this here.
- Nia chatbot: You can build AI-powered chatbots with Nia, and your app can provide access to the enterprise knowledge repository. The app can also automate actions through a conversational interface. Find out more about the Nia chatbot here.
- Nia data: Your AI app can integrate Nia data, a robust analytics solution. You can read more about Nia data here.
Access the Nia platform resources here.
Why software engineers and companies might want to create Artificial Intelligence and Machine Learning systems using Infosys Nia
The following strengths of Infosys Nia can help to create and deploy AI and ML systems:
A. Tools to effectively build Machine Learning training models
You can use the tools offered by Infosys Nia to build superior ML training models. The platform helps to conduct researches involving large data sets.
B. The scale and speed of processing data
With Infosys Nia, you get the right set of tools to process large data sets at scale. You get the data-processing speed that you need.
C. Data management capabilities
Infosys Nia provides the right structure, frameworks, and technology to manage data effectively. Data management can be a challenge for many businesses when they undertake AI and ML projects. Infosys Nia makes this task simpler.
Dialogflow is a Natural Language Processing (NLP) tool.
Dialogflow offers the following features:
· “Agents”: It’s a virtual agent to manage conversations with users. It contains a module to understand natural language.
· “Intents”: This feature helps Dialogflow assign different categories to the intention of a user.
· “Entities”: This feature determines how Dialogflow extracts data from expressions of users.
· “Contexts”: Dialogflow uses this feature to control the flow of interaction.
· “Follow-up intents”: “Follow-up intents” are children of “parent intents”, and Dialogflow uses them to add contexts to the “parent intents”.
· “Dialogflow Console”: Users of Dialogflow uses the “Dialogflow Console” to manage “agents”. It’s a web user interface.
Why the decision-makers in the technology industry might want to create AI systems using Dialogflow
You might want to use Dialogflow due to the following reasons:
· It runs on the Google Cloud Platform, therefore, you should be able to scale your AI app easily.
· Dialogflow lets you build voice and text-based conversational interface for your app. Your app can run on web and mobile, moreover, you can connect your users on Google Assistant, Amazon Alexa, Facebook Messenger, etc.
· Dialogflow offers useful SDKs to help developers.
· It’s a user-friendly AI development tool.
· Dialogflow provides integration capabilities for important programming languages.
BigML is highly focused on ML, and its development platform offers powerful ML capabilities.
Features and capabilities offered by BigML:
BigML offers the following features:
Specialized features like “Gallery”, “PredictServer”, Alexa Voice service, and WhizzML programming language;
- Support for concurrent tasks;
- Useful libraries;
- Open-source command-line tools;
- Add-on for Google Sheets.
Why businesses might want to develop Machine Learning (ML) systems using BigML:
BigML offers the following advantages:
· It provides robust ML algorithms, both for supervised and unsupervised learning.
· You can implement instant access to its ML platform using its REST API, and you can do that both on-premises and on the cloud. BigML offers interpretable and exportable ML models, and this is a key advantage. You can access the BigML platform here.
· It’s programmable and repeatable. You can use popular languages like Python, Node.js, Ruby, Java, Swift, etc. to code your app, and BigML supports them.
· BigML helps you to automate your predictive modeling tasks.
· Deployment is flexible since you can deploy your AI app both on-premises or on the cloud. BigML has smart infrastructure solutions that help in scaling your app.
· BigML has robust security and privacy features.
· BigML offers scalability.
· It offers a RESTful API, which allows businesses to integrate it easily with other systems.
· You can implement automation easily with BigML.
· BigML enables you to generate real-time predictions.
You can read the BigML documentation here.
Planning to build your artificial intelligence solution?
Artificial intelligence has significant potential to transform your business, and projects to develop AI solutions that have vital real-world applications and therefore a big market.
AI and ML skills are niches, and it can be hard to form competent DevOps. This increases the complexity of such projects.
I recommend that you engage a reputed software development company with real-time progress reporting for such projects. It can be hard to find such a competent development partner, fortunately, DevTeam.Space can help you to hire the best AI developers.
Our developers are expert in all programming languages and aspects of AI and ML development. We have developed all kinds of ML solutions for all types of ecosystems.
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Frequently Asked Questions
ML stands for Machine Learning while AI stands for Artificial Intelligence. Current software solutions are really only ML systems. With development, we will see the first true AI within a decade or so when the computation power and algorithm development reaches a level where it can make a huge difference.
Artificial intelligence systems are software. They require code to operate. In the future, it is possible is seems certain that they will begin as basic AI programs which will then write new code in order to expand their ability to understand and learn.