Democratization of AI has become viable with no code tools available. Today AI has transitioned from model-centric to data-centric, giving our domain experts more control over accurate ML model building.
What do you consider I’ve misspoken? A data scientist should have been there, given data scientists are the ones who create ML models. They have an edge in model building, but only domain experts can assess how well they have achieved their objectives. Don’t get me wrong: I’m not proposing that any domain experts can take the data scientist title; they both play essential roles in ML model building.
Let’s start with the fundamentals: who are domain experts?
They have extensive knowledge about the particular area in an endeavor, be it CPG, healthcare, education, etc. There is also the possibility that domain experts may not have a technical background. Domain Experts(DE) are knowledgeable about
- What are the problems in business, and how do you overcome them?
- What are the current trends, and how is a particular sector shaping up?
- What are the influencing elements that will influence a business?
Considering you must now understand what Domain Experts are.
Let’s learn Why domain experts should be treated as primary drivers?
The participation of DE is essential; they know data well and, therefore, can fully understand what factors influence the outcome and have the capability to evaluate models’ performance by analyzing the results. With the collaboration of business experts, data scientists can develop a successful ML model.
Answering a few questions will help you understand this fact.
-Who is accountable for selecting ML as a solution?
Domain experts are responsible for identifying challenges, searching for optimal solutions, and determining if they can be best addressed with AI/ML. If yes, this can be investigated further with a data scientist or third-party AI/ML solution providers, or they can experiment independently with a no-code AI tool.
-Who knows the problem better?
The domain expert recognizes the objective to be achieved and the elements that might impact it. For example, if you are producing a product and want to know how many units will be produced? In this scenario, the goal is the number of units produced. It is affected by several factors like product purchases, marketing activities, promotions, events, weather, geographic conditions, economy, trends, etc. As a result, several methods characterize this problem to launch a machine learning model. Your problem formulation here depends on your company’s needs, which only domain experts can gauge.
– What role do the domain experts play in model building?
Domain experts’ insights tailor the training of ML models better. These insights are directed toward capturing the appropriate data for training. Data plays a key role in ML model building. A data scientist can clean, feature engineer, or do any other treatment on data. Understanding the optimal algorithm capable of producing outcomes that domain experts approve of. Today’s paradigm shift from model-centric AI to data-centric AI makes one thing visible: DEs have control over building a successful ML Model.
The continuous feedback from experts helps data scientists to tune ML models. As ML is iterative and evolutionary, every feedback from a DE drives a data scientist toward constructing a model that aids in goal achievement.
Domain experts are — the primary drivers of AI/ML.
“Companies that dedicate funds for AI initiatives at the C-suite level are twice as likely to attain high levels of AI maturity” ~Gartner.
Limitations to fully realizing the potential of AI:
Although AI is ready to disrupt company operations, many organizations remain skeptical of this technology. Understanding the bottleneck preventing businesses from adopting AI is essential. The factors might include
- An organization’s culture does not recognize the need for AI.
- A lack of quality and quantity of data.
- Trouble identifying relevant use cases.
- Technological infrastructure issues.
- A lack of skilled individuals, etc.
This stems from a lack of AI knowledge, which AI education can only remedy.
A wrap-up:
It has become a must-know technology to empower oneself with AI as a tool. Emly learn (releasing soon…) is a game designed by EMLY. Labs to help our business leaders grasp machine learning. A game that will provide scenarios in which you need to create a real ML model to understand various ML processes better.
Expertise leads to better data, leading to better training, and a better model.
Unleash the full potential of AI with domain experts at the forefront. Are you struggling to hire talented data scientists due to scarcity or cost? Then you must look for answers within your organization. Upskill your business experts and watch them transform into AI champions. Technical barriers? It is now a thing of the past with the advent of no-code AI tools. Allow domain experts to experiment and bring their knowledge to life easily and without fear of failure. The road to AI success starts now.