The Best Artificial Intelligence Software Development Tools Of 2020
Interested in the best artificial intelligence software development tools of 2020?
This is a massive industry sector that should be explored.
According to a study done by Grand View Research, “Artificial Intelligence Market Size Worth $390.9 Billion By 2025.”
Besides the huge sums of money to be made, innovating in this industry represents a chance to make a positive impact on people’s quality of life. Here’re a few amazing case studies of companies who hired DevTeam.Space to build their healthcare products:
Hit Factor – Machine Learning Image Recognition App
High Speed Vehicle Recognition – Machine Learning Image Recognition Application
Face, Sex, Age, Recognition System – Machine Learning Program
Let’s quickly cover some preliminary questions, e.g., why are we even talking about AI, how can it help, its growth potential, etc.
AI is an interdisciplinary branch of computer science that has also been commercialized as a high-value technology. The objective of this technology is to make computers smart enough to carry out tasks that require human intelligence, and the computers become smart by “learning” from “experience”, i.e., a massive amount of data. Read more about it in “What is artificial intelligence?”.
In addition to having key uses in many sectors, AI can transform many functionalities in businesses and large organizations. E.g., AI can improve customer service, fraud prevention, IT infrastructure security solutions, supply chain management, etc. I have explained them in “5 ways AI will change your business in the near future”.
Given its importance, AI has a large market globally, which will likely reach $77.6 billion in 2022. What’s more, AI will create tremendous business value! Gartner estimates that AI will create business value worth $3.9 trillion in 2022, as you can read in “Roundup of machine learning forecasts and market estimates for 2019”.
Why AI development tools matter?
While AI has significant potential, executing AI software development projects can be hard. You need a great deal of expertise to plan and budget such projects, as I have explained in “How much does it cost to develop an AI solution for your company?”.
Developing an AI solution isn’t a one-dimensional project either since you might need to use several ways to achieve your objectives. E.g., you might need to use machine learning (ML), natural language processing (NLP), vision, speech, and several other AI capabilities. You can read about these capabilities in “Artificial intelligence: definition, types, examples, technologies”.
When you undertake a complex project like this, you need to use the right toolset, therefore, a robust set of AI development tools are important. Ideally, a robust AI development platform should offer the following capabilities:
- ML capabilities like deep learning, supervised algorithms, unsupervised algorithms, etc.;
- NLP capabilities like classification, machine translation, etc.;
- Expert systems;
- Vision capabilities like image recognition;
- Speech capabilities like speech-to-text and text-to-speech;
Such a platform should also offer a robust cloud infrastructure. You can read “Top 15 artificial intelligence platforms” to learn more about the desired features in an AI development platform.
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 AI capabilities, and the Microsoft Azure AI Platform is a popular choice for AI development.
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:
- 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.
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.
Read the Azure Search documentation here.
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:
You can find the documentation for the Azure Cognitive services here.
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 here.
2. Google Cloud AI Platform
Google is yet another cloud computing giant that offers its AI platform, and you can access it here. 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 on 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.
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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 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.
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.
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.
IBM Watson offers the following to expedite your AI app development:
- 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.
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.
4. Infosys Nia
Infosys Nia is an AI platform that allows you to build AI-powered apps. It offers the following AI 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.
Dialogflow uses Google’s infrastructure, moreover, it incorporates Google’s ML capabilities. 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.
The key capabilities offered by Dialogflow are ML, NLP, and speech. You can read the Dialogflow documentation here.
BigML is highly focused on ML, and its development platform offers powerful ML capabilities. 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.
BigML offers the following features:
- 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.
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 have high visibility. AI and ML skills are niches, and it can be hard to form a competent development team. This increases the complexity of such projects.
I recommend that you engage a reputed software development company for such projects. It can be hard to find such a competent development partner, however, we can help!