For a recent Forbes.com post, I wrote about the required skillsets for hiring AI talent. No doubt, they are quite extensive — and in high demand.
Consider the following from Udacity:
“We’ve seen a tremendous rise in interest and enrollment in AI and machine learning, not just year over year but month over month as well. From 2017 to 2018, we saw over 30% growth in demand for courses on AI and machine learning. In 2018, we saw an even more significant rise with a 70% increase in demand for AI and machine learning courses. We anticipate interest to continue to grow month over month in 2019.”
Despite all this, when hiring AI people, you will still need to do your own training. And it must be ongoing. If not, there is a big risk of failure with a new AI hire.
So let’s see how various top companies are handling training:
Ohad Barnoy,VP of Customer Success, Kryon Systems:
Our AI developers start with an in-depth training itinerary in order to gain a deep understanding of our platforms. They do this via our home-grown on-line Kryon Academy, a program that helps further AI training in parallel with on-the-job training. The developer is assigned a three-week course in each one of our development pods and with QA.
Chris Hausler, Data Science Manager, Zendesk:
Research and technology in AI is moving so quickly that constant learning and upskilling is required to keep up with the state-of-the-art and do your job well. At Zendesk, we run a weekly paper club where we discuss emerging research related to our work and have frequent “lab days” where the team has time to experiment with new ideas.
From Atif Kureishy, Global VP, Emerging Practices at Teradata:
Though more and more people are retooling their skillsets by acquiring deep learning knowledge through avenues like massive open online courses (MOOCs) or Kaggle, it is rare to find people who can do it in practice – and this difference is important. The classroom or competitions are certainly a step in the right direction, but it does not replace real-life experience.
Organizations should deploy and rotate their AI teams across various business units to gain exposure and understand challenges that the line of business is facing in building AI capabilities. This will enable experiential knowledge that can be brought together in a Center of Excellence but carries forward experiences from across their Enterprise.
Guy Caspi, CEO and co-founder at Deep Instinct:
At Deep Instinct, we focus our training primarily on two areas: Comprehensive understanding of deep learning, machine learning and big data, plus one additional area: the domain our product is in. For instance, our cybersecurity experts are consistently sharing their knowledge with our deep learning experts during the training process. The reason is that a deep (or machine) learning expert who is saturated with knowledge specific to the domain (in our case cybersecurity) during training will operate more effectively and be better adapted to real-world use cases.
Yogesh Patel, CTO & Head of AI Research, Callsign:
The line between Data Engineers, Software Engineers and Data Scientists is blurring when it comes to big data. There is a clear pull towards the latter, with more Data Engineers and Software Engineers seeking to become Data Scientists. With the introduction of deep learning, there is less and less need to spend huge amounts of time dealing with data exploration, data cleansing and feature engineering — at least in theory. Correspondingly, we are seeing more people claiming to be Data Scientists, but who are really just applying a brute force approach to machine learning.
Furthermore, we have training companies claiming that no prior knowledge in data curation is required and that no background in statistics is required. While that may be true in some domains, in the domain of cybersecurity we need more people with a solid understanding of the domain, as well as data science concepts. This means understanding the meaning and statistical properties and relationships between data attributes across a variety of data sources. It also means understanding how those data attributes and data sources might impact a given algorithm, especially when dealing with issues such as the imbalanced classification problem. For example, for the task of credit fraud detection, it means having an intuitive grasp about how, when and where a given transaction type occurs — a prerequisite for formulating and testing experimental hypotheses. In the same example, it also means understanding exactly how a given classification algorithm might be impacted when few to no examples of a given transaction type are available, and tuning or adapting the classification algorithm as necessary.
Alex Spinelli, CTO, LivePerson:
Managers and leaders must learn the concepts. They must learn what is and is not an applicable use of AI.
For example, AI is powered by data and examples. Problems that have limited history are often not good examples of ones easily solved by AI tools. This is referred to as the cold start problem.
Outputs from AI are not always predictable. This means that the linear nature of product design and workflows will change. It is not easy to reverse engineer why an AI system provided a specific answer. Another critical component to the training process is to develop new skills on product design that leverages AI. Product designers and leaders must understand statistics and probability in new ways.
Corey Berkey, Director of Human Resources, JazzHR:
Many companies are investing in training their workers to ensure they are staying current with technology and advancements in the industry. While math and computer technology serve as the backbone of AI-focused roles, continuing education in the field is a must. Many online learning solutions today offer a variety of AI-related certifications from top-tier universities to help workers expand their knowledge in areas such as programming, machine learning, graphical modeling, and advanced mathematics. It’s critical that companies focus on providing development opportunities these transformative hires so they are able to fine-tune their skills and learn best practices from peers.