Management in AI

Tips For Training AI/ML Talent

September 16, 2022
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Training employees internally motivates people to stay on board and tends to create high returns on investment. Training is also necessary for fields like data science and machine learning, where new tools and frameworks are developed rapidly, and new research papers are posted on a weekly basis. In this article, we provide best practices for training ML talent.

Training employees may seem costly at first, because it costs time and resources that are not directly contributing to the bottom line. But when done right, training can offer great returns on investment. Teaching new skills and competencies allows people to work more efficiently, or create something they couldn’t do before. High learning curves also motivates employees to stay onboard, as opposed to simple work that quickly becomes repetitive. In fact, if your work can easily be taught to someone else, you will face job insecurity sooner or later.

Training is not just reserved for large corporations with big budgets. In fact, it’s paramount for startups whose core product is centered around AI to build and grow a team from the beginning.

How to Train Your ML Team?

Companies should match career paths and job titles into the data science lifecycle. This puts your employees on a learning trajectory that corresponds to job growth, and allows you to determine who should learn what. For example, a product data scientists should be skilled at understanding data, learning relations between them and making decisions based on them. Make sure the entire data science cycle is covered by some role, and fold competencies into that role.

Start building a portfolio of ML projects to train your ML team as a whole. Machine learning is driven by trial-and-error, and most models will fail to reach production. What matters is that you can take the lessons from failure and apply them to the next project. This knowledge can be captured and annotated, and remain accessible for future generations of employees.

One successful way to introduce new tools and techniques is to organize hackathons! This is a great way to let your team experiment freely without deadlines or constraints. Hackathons have even led to the creation of successful startups and products with millions of users. While they originated in software development, they can be perfectly applied to machine learning as well. Hackathons can be centered around a new ML models or algorithms, and can offer incentives for teams with the best performance.

Hiring Managers Externally or Promoting Internally?

A common question from data leaders is whether they should focus on promoting data scientists internally to manager roles or if they should hire external help. A good rule of thumb is this: focus on promoting internally whenever you can, but consider hiring externally if you need to bring on new domain expertise, or if you want to introduce fresh ideas into the team.

An effective data scientist doesn’t always make an effective leader. But an effective data leader will need extensive knowledge of the data science process. The best data science leaders posess a good balance of the soft skills and hard skills. One tip from a data science leader is to keep spending some personal time working with data science tools and problems. This is a great way to preserve the “data science mindset” and to stay aware of new tools and libraries.

While leaders are essential, successful companies enable all employees to be “mini-leaders”. They empower them to take initiative on ideas and provide the right resources. In this sense, leadership becomes more of a support role. This can be particularly effective in data science, since it’s an exploratory process and employees working most intimately with the data sets will have a better intuition of which models might be successful.

How can Vectice help?

By building a portfolio of ML projects that can capture the learnings of experimentation. Vectice offers the first solution that embraces the iterative nature of data science workflows and centralizes AI assets into a searchable catalog.

This means you can access previous projects, take the lessons learned from different use cases, and apply them to future projects. In Vectice, knowledge is captured and annotated and can be accessed by current and prospective employees.

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