So when the advantages and the need for adopting artificial intelligence technology in your business are clear, you have to think about building AI expertise in your company. These days, forming artificial intelligence expertise is a challenge due to both soaring demand for qualified AI experts and a significant talent shortage worldwide. So how should you build a professional AI team? There are two possible options:
Forming an in-house AI development team;Choosing a software development partner with proven AI expertise.
Let’s take a closer look at each strategy.
Building an in-house AI development team
If you want to develop your own AI capabilities, you need to bring in strong technical skills in machine learning. Since recruiting experts in this area is not an easy task, upskilling your in-house developers may be an option. You should know that successful implementation of AI requires a variety of professionals with different sets of skills.
Experts for building AI expertise in your company
Big Data engineers. Before analyzing data, you need you collect, organize, and process it. AI is usually based on big data, so you need the engineers who have experience working with structured and unstructured data, and can build a secure data platform. They should have sound knowledge of Hadoop, Spark, R, Hive, Pig, and other Big Data technologies.Data scientists. Data scientists are a vital part of your AI team. They work their magic with data, building the models, investigating, analyzing, and interpreting it. They leverage data mining and other techniques to surface hidden insights and solve business problems.NLP specialists. A lot of AI projects involve Natural Language Processing, so you will probably need NLP specialists. NLP allows computers to understand and translate human language serving as a bridge between human communication and machine interpretation.Machine learning engineers. These specialists utilize machine learning libraries, deploying ML solutions into production. They take care of the maintainability and scalability of data science code.Computer vision engineers. They specialize in imagery recognition, correlating image to a particular metric instead of correlating metrics to metrics. For example, computer vision is used for modeling objects or environments (medical image analysis), identification tasks (a species identification system), and processes controlling (industrial robots).Speech recognition engineers. You will need these experts if you want to build your speech recognition system. Speech recognition can be very useful in telecommunication services, in-car systems, medical documentation, and education. For instance, it is used in language learning for practicing pronunciation.
Top languages and frameworks for machine learning development
The most widely used programming languages among Machine Learning engineers and Data Scientists are R and Python. R has a substantial collection of libraries, it is easy to deploy and it enables you to build prototypes fast. When it comes to Python, this programming language has plenty of libraries for machine learning. Also, there are many frameworks, libraries, and toolkits for machine learning development. For instance, Spark is good for big data processing. It gives you access to a variety of machine learning libraries and makes the distribution to different servers possible. Meanwhile, Hadoop is good for storing large volumes of data.
Partnering with an AI solution provider
If you realize that recruiting and building your own in-house AI team is too difficult and expensive, you can engage with an external AI provider. Such an approach helps companies keep the focus on their core expertise and avoid the headache of recruiting the engineers and setting up the team. Also, it allows them to kick off the project much faster and thus gain a competitive advantage.
Factors to consider when choosing an artificial intelligence solution provider
AI engineering experience
Due to the huge popularity of AI these days, many companies claim to be professional AI development providers without practical experience. So it’s extremely important to do extensive research. Firstly, you should study the portfolio and case studies of the company. Find out which AI, machine learning or data science project your potential vendor worked on and what kind of artificial intelligence solutions the company delivered. For instance, you may check out these European AI development companies and the products they developed. Also, make sure a provider has experience in different types of machine learning (supervised, unsupervised, and reinforcement), data structures and algorithms, computer vision, NLP, etc.
Expertise in AI technologies
Artificial Intelligence covers a multitude of different technologies, frameworks, and tools. Make sure your external engineering team consists of professional data scientists and data engineers able to solve your business problems. Building the AI team and selecting the necessary skill set might be challenging for businesses that have no internal AI expertise. Therefore, ask a vendor to provide tech experts or delivery managers who will advise you on the team composition and help you hire the right people.
Capacities to scale a team
When choosing a team, you should consider not only your primary needs but also the potential growth of your business. If you expect your company to scale up, you’ll need more engineering capacities. Therefore, take into account your partner’s ability to ramp up the team in the future. Also, consider such factors as the vendor’s employer image and retention rate since your ability to attract top AI talent and keep them on your project will largely depend on it.
Suitable cooperation model
It is essential to choose the AI company with a cooperation model that fits your business requirements. The most popular cooperation models are Fixed Price, Time and Material, and Dedicated Development Team. Within the fixed price model all the requirements and the scope of work are set from the start, and you as a customer need to have them described to the smallest detail as it will be extremely difficult to make change requests during the project. However, it is not the best option for AI projects since they involve a lot of R&D and it is too hard to define everything at the initial stage. Time and material model is the best for small projects when you don’t need the specialists to be fully dedicated to your project. This is not the best choice for AI development as the hourly rates of AI engineers are extremely high and the whole project would cost you a fortune with this type of contract. In order to add more flexibility yet keep control over the project budget, it is better to choose a dedicated development team model or staff augmentation. It will allow you to change the requirements when needed and have control over your team. With this type of engagement, you will be able to keep the knowledge within your team and develop your AI expertise as developers will work exclusively for you.
Conclusion
If you have to deal with the challenge of building AI expertise in your company, there are two possible ways to go. First off, you can attract local AI talent and build the expertise in-house. Then you have to assemble the team of data scientists, data engineers, and other specialists depending on your needs. However, developing AI expertise in-house is always time- and cost-consuming taking into account the shortage of well-qualified machine learning specialists and over-high salary expectations. If so, there is another option. You can partner with the AI development vendor and hire an extended team of engineers. In this case, you have to consider a number of factors such as the company’s experience in delivering AI solutions, the ability to allocate the necessary resources, the technological expertise, and its capabilities to satisfy your business requirements.
