Machine Learning Development Services

ML or Machine Learning is an area of computer science and AI-related technologies. In short, it deals with teaching a machine to analyze huge amounts of data and give certain results, such as suspicious activity detection, error correction, image recognition, etc. ML developers use different Machine Learning algorithms to pursue these goals, and it takes knowledge and experience to select the right algorithm and teach the system. The use of Machine Learning in tech allows software to be more precise in making predictions about certain outcomes, which is the main goal of teaching.

Machine Learning development can significantly help companies from the number of industries, such as:

• Cybersecurity;
• System Integration;
• Social networking;
• FinTech;
• Retail;
• Healthcare;
• Education, and more.

There are the 4 basics of machine learning that include reinforcement learning, as well as semi-supervised, unsupervised and supervised Machine Learning. These four approaches are also known as types of Machine Learning models. They allow specialists to cover most tasks and problems, including pattern discovery and predictions. As a Machine Learning development company, we use supervised Machine Learning and other types to solve challenges for our clients’ projects in various industries.

AI development services include ML, but there is a significant difference between the two. Artificial Intelligence focuses on the idea that computer systems can mimic human thinking to solve problems faster and error-free. Machine Learning is about teaching computer systems how to perform certain tasks, identify patterns, and always give accurate results.

A model is a kind of file that ML specialists create and teach to recognize specific behavior or elements. Training data is very important because it can make a model correct and fast in analyzing. A data set for training should be big and diverse enough, especially when your aim is to deal with complicated problems like fraud detection.

As their names suggest, the first type doesn’t include much human involvement while the second type depends on certain actions from a specialist. There are different types of data sets in ML. If you are an experienced Machine Learning developer you know that you need labeled data to build supervised learning algorithms. Correspondingly, unsupervised learning will require only unlabeled data.

Specialists use three primary ML techniques: regression, clustering, and classification. The first one means the continuous response from the system. In the second and third one, you can see specific clusters and classes.

There are lots of ML algorithms related to techniques and methods like regression, clustering, and classification. For example, a neural network that resembles the human brain can relate both to clustering and regression. Other widely used algorithms include linear regression, binary classification, K-means, dimensionality reduction, and more.

There are many factors that may influence this choice. The most important one is the type of your project. Next factor is how much time you have – experts in Machine Learning modeling need to quickly evaluate the speed of training and decide on the right model for this particular project. Performance of the chosen algorithm is also a critical factor, because it should bring as correct results as possible.

Python is the most widely used programming language for AI and ML. Moreover, data scientists often work with Java, C++, Haskell, Prolog, and other programming languages. The choice of languages and tools depends on your goals and sometimes on the history of what developers working on this project applied previously.

Cloud computing and ML development are the areas that can be very beneficial for each other. Cloud environments provide almost unlimited opportunities to store large volumes of information. How is it connected to ML? Typically, you need a lot of training data to start a new Machine Learning project – and the best way to have access to this data is to keep it in the cloud. The reason why so many companies opt for AWS and Google Cloud migration is because they want to use all the advantages of AI and ML. On the other hand, different types of machine learning can provide more opportunities for cloud software development and testing – predictive analytics, neural networks, computer vision, and other technologies may increase efficiency, reduce bugs, and help specialists’ work.

If you want to become a Machine learning specialist, there are some good ML projects that you can try to develop. We can give you a few examples:

1. Movie recommendation app;
2. Software that turns handwritten texts into digital documents of various formats;
3. TensorFlow project, which uses resources from an open-source AI/ML library;
4. Stock market forecast software;
5. Activity detection mobile app;
6. Illness prediction app;
7. Sales forecast app, and more.

The cost of ML engineering may depend on three main factors:

1. Project type and complexity. This includes the goal of your project, how complicated architecture you need, how much data you need to reach the goal and how well this information is organized, how many algorithms developers use, estimated time to teach the model, and so on.
2. ML engineers’ level. You can choose open-source ML solutions and Middle-level developers for simple projects that don't require experiments and innovation. However, if your company needs to cover complex problems and find new solutions, you should invest in hiring Senior ML engineers that are able to use advanced approaches to working with models and algorithms.
3. Cooperation details. This usually includes the team’s size, location, and employment model, for example, outsourcing or in-house.

The ideal team for ML project development should include:

• Business analyst;
• Chief analytics officer;
• Solution architect;
• Data analyst;
• Data scientist;
• Data architect;
• Data engineer;
• Domain specialist;
• Database administrator;
• Some other specialists from related fields.

However, real world situations may differ – for example, a smaller and less complicated project won’t require such a diverse team. When you search for a ML technology partner, it is important to start with business analytics and define the core features that should be in your solution. Understanding the real complexity of the project, you will be able to select the right team for it. If you are not sure how to estimate the scope of work or choose the ML specialist level, just reach out and talk to us.