AI Development Services

DevTeam.Space offers a wide range of AI development services that will allow you to undertake any kind of AI or ML project. Examples include chatbot development, product recommendation systems, AI-based mobile applications, machine learning model development, etc.

In our community of over 1200 developers, we have skilled AI engineers who cover all the major AI development technologies, programming languages, libraries, frameworks, and tools. We have helped clients develop AI applications in every industry segment including education, healthcare, finance, commerce, and others.

You can find some examples of the technologies that our developers love to use below or alternatively, head to our case studies page to check out some of our recent AI projects.

The specific technology stack you choose for your AI application project will depend on your project requirements.

If you need help finding the right AI developers then submit your project request here and get a complimentary discovery call with one of our tech account managers.

Top 10 AI Development Technologies

1. Python
2. AWS SageMaker
3. Azure AI
4. IBM Watson
5. TensorFlow
6. Keras
7. Google AutoML
8. Theano
9. Caffe
10. Deeplearning4j

Here are a few technologies that our AI engineers use on a daily basis with a brief explanation of why they love to use them.

Python

Python is one of the most popular languages for AI and ML development as well as a huge range of other software development projects that include web and mobile applications.

Despite being started as a hobby project, today, Python is one of the most popular programming languages around the globe.

A few fun facts our developers love are that, in Python, underscores have memory power, Python is able to define infinite numbers, and when you type “import antigravity” into Python, it gives you an easter egg!

However, it is the following advantages that are the reason why our developers love to use Python to build powerful projects:

1. Python has a huge number of supporting libraries that aid machine learning development. Examples include PyTorch, Keras, and NLTK, all of which help simplify machine learning model development and produce performant and reliable applications.
2. Python is a portable language, meaning it can be used on a range of different platforms and operating systems including Linux, macOS, mobile OS, etc.
3. Python offers a strong level of flexibility thanks to it allowing developers to choose between different programming styles, i.e. procedural, functional, and object-oriented programming.

AWS SageMaker

AWS SageMaker is another highly popular AI development tool that our developers use all the time. It is a fully managed artificial intelligence development service offered by Amazon Web Services (AWS), the world’s leading cloud services provider.

The key advantages of using AWS SageMaker for AI development are as follows:

1. Sheer size. AWS is the world’s leading cloud provider. As part of this offering, it has a huge number of tools and an almost infinite amount of processing and data storage capacity, both of which are essential for AI.
2. AWS SageMaker offers its AutoML jobs tool that helps automatically train advanced AI models in SageMaker pipelines. This saves an enormous amount of time and money.
3. Thanks to a huge development pool, AI developers have access to a large range of pre-built machine learning algorithms or models. Examples include regression, classification, dimensionality reduction, etc. AWS SageMaker also offers a UI-based model training tool called SageMaker JumpStart, which our developers find very handy indeed.

Azure AI

Microsoft Azure AI is a comprehensive AI development platform that is currently regarded as one of the world’s best places to develop AI and ML software solutions.

Here are some of the key features of Azure’s AI offering:

1. Like AWS, developers can build, test, deploy, and maintain AI applications using Azure, all under one roof.
2. Azure AI offers several low-code tools such as automated machine learning and a drag-and-drop interface. These cut down on development time and allow developers to concentrate on building and training the AI models.
3. Azure now offers access to a wide range of powerful AI technologies that can be painlessly integrated into your application. Examples that our developers recently used include Azure Machine Learning, Azure AI Custom Vision, Azure Databricks, and Azure AI Bot Service.

In the battle to become the go-to AI cloud development platform, AWS and Azure are doing their very best to outdo each other. This is great news for developers. Both have their strengths and weaknesses, however, it might be fairer to put it like this. Which one you ultimately decide to use depends on your specific project requirements.

Choosing a specific framework or platform for your AI software project will depend on your unique application requirements.

If you have any questions or need help with your AI project, you only need to submit a project request via the button below and one of our account managers will get in touch to arrange a complimentary discovery call.