AI & Machine Learning Development Services

In a nutshell, artificial intelligence is a way to make machines think like humans, and machine learning is an approach to achieve artificial intelligence. Machine learning development is about writing algorithms that can make sense of data – classify data, find patterns and correlations, and learn from data to make predictions – all without human intervention. Deep learning in its turn is one of the most widely used techniques for implementing machine learning. It uses neural network architecture that consists of multiple layers to learn features from the data.

Within our machine learning development services, our firm delivers projects of various complexity from a simple ML-based application to a complex AI-enabled system.

To build a successful deep learning application, we need a large amount of data to train the model and a powerful data processing system.

For projects with advanced image processing needs, we use OpenCV library. We also use Amazon and Azure's Machine Learning services to build predictive models and automate data processing tasks. Other machine learning technologies in our tech stack include TensorFlow, Keras, Caffe/Caffe2, and OpenALPR.

Machine learning tasks we’re best at include:

• Training neural networks to build classifications
• Object recognition
• OCR
• Face recognition
• Human detection
• Predictive analytics
• Natural Language Processing (NLP) for text and voice applications
• Recommender systems

We normally use a standard workflow for machine learning solutions development. First, we need to look at your problem and see if machine learning is the best method to solve it. If yes, then the next step is to collect meaningful data for training purposes. We will classify and analyze these data, and when our dataset is ready, we can start a learning model development. One of the most important stages in this process is training the model on the existing dataset. This usually takes a few design experiments and iterations. Once we get the proof of concept, we’ll integrate our model with your solution and launch it to production.

Not enough data will lead to unsatisfying results. The more data the more accurate the algorithm. If you don’t have the amount of data, we need to build a training model, we will try to collect them from online sources if possible. There are a lot of open source resources that contain databases with categorized data that we can use to train our system. Also, there are some paid resources where we can create a database with the needed information for training purposes.

Artificial intelligence has a really broad range of applications. It is widely used in computer science, aviation, marketing, telecommunications, and logistics. AI is applied for healthcare, educational, financial, and military projects and in many other fields.