In nearly every industry, artificial intelligence (AI) is no longer a “nice to have” technology, but an urgent business need that enables companies to remain agile, increase productivity and drive insight. A mission-critical solution for But achieving it and preparing your organization for AI success requires a strong team of technologists, data scientists, and product specialists as a foundation.
Whether you’re building a team from scratch, scaling an existing AI team, or just wanting to improve workflow and cross-functional collaboration, this practical guide covers the key elements you need to put together the right team. analyze some of its components.
Of course, every business is different and may have contrasting needs. However, a few key roles make up a balanced and successful AI team. Key roles to consider hiring include:
1. Engineer: From idea to production
First, you need a machine learning (ML) engineer or researcher to build a model based on a given dataset and the problem you’re trying to solve. You can choose an ML engineer for a well-understood task. For tasks no one has ever solved, you may need an ML researcher.
[Also read Artificial intelligence: 3 ways the pandemic accelerated its adoption. ]
From there, the next big hire is an infrastructure engineer, who creates and runs the supporting functionality and backend infrastructure that ML engineers need to make their AI models work. For example, when building an AI model, it is often necessary to leverage different cloud instances and scale to perform training and evaluation quickly. Infrastructure engineers make it easier for ML engineers to iterate through the model development, training, and evaluation loop.
We also need engineers who can translate models from research to production. This includes building APIs, handling errors, logging, and monitoring. If the product succeeds, optimizing the cost of cloud computing will ultimately become an issue as well. To account for this, the engineer in this role must constantly monitor the drift of the dataset and set up her retraining job to continuously update the model.
2. Data Scientist: From Labeling to Analysis
Consider hiring a data expert who can create dashboards that allow the business team to easily see and understand overall project metrics.
Hiring data scientists for your AI team is also important. In many cases, there is no need to create a new model for a specific problem. Instead, you can clean and analyze your existing data. Data scientists can use SQL to quickly slice and visualize data.
[ Related read Data scientist: A day in the life ]
Many ML tasks require interfaces that allow data labelers to work quickly and accurately. So developers are needed to build native or web interfaces. If you hire a data labeler, you also want her QA engineers to track and review their work to ensure quality.
Additionally, consider hiring a data expert who can create dashboards that allow the business team to easily see and understand overall project metrics. This role could be a business data analyst or a data scientist. Having this role on the team gives the rest of the organization (especially non-technical people) visibility into the amazing results the team is achieving.
3. Product Manager: From technical know-how to marketing solutions
Finally, you need a product manager who understands how to plan and exploit the strengths and weaknesses of AI. For example, a classification model can output a score for the likelihood of an example falling into the positive class. The higher the score, the more confident the model is that the example is positive.
Product managers can help understand how to design great user experiences in the face of these uncertainties. For example, you may find that a search engine is a good solution because even though the top answer is wrong, there is value in having the 2nd and her 3rd answers correct. Individuals in this role ensure that products are designed around the strengths and limitations of ML models.
Take your AI projects to the next level
Employing multidisciplinary teams where collaboration is encouraged and embraced will pay off in the form of employee satisfaction and the ability to build and scale end-to-end AI solutions that efficiently drive business value. There is a possibility that it will be
Your AI product is as powerful as your team. Hiring strategically and focusing on your team’s success allows you to focus on the company’s overall success.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
