We all remember the chess clash between grandmaster Gary Kasparov and Supercomputer Deep Blue in 1997. For most of us, it was our first encounter with the power of smart machines, telecasted on another machine, "television". Starting from their invention, terms like Super Computers, Artificial intelligence, or Machine learning have come a long way, from an impossible reality to seizing space in every household as "bots". They face speculations and acceptance both but continue to make human life easier.
This article is an abstract of the white paper “Unlocking the Potential of Data Science, Machine Learning, and Artificial Intelligence". It intends to share a gist of the elaborate experiences and challenges we encountered as a team with AI/ ML and Data Science. The white paper includes practical outcomes of incorporating Data science technologies at the organizational level. It also explains the entities that play key roles in data science practice, i.e., the data science, data engineering, machine learning team, domain experts/ analysts, and stakeholders.
AI, a boon for the 'Four'
The four business verticals where AI/ ML is and will create a world of difference are Operations, Talent Management, Financial Management/Risk Management, and Customer Engagement, to list a few:
- Operations: It identifies the areas of inefficiency and makes the processes smoother
- Talent Management: It helps individualized onboarding experience
- Finance and Risk management: It identifies fraudulent transactions and transactional errors
- Customer Engagement: Everything sums to significant growth, with increased customer loyalty.
The results for every vertical are reassuring but require a series of meticulous efforts and streamlined processes. The domain experts and stakeholders have critical elements of the practices to implement, which includes:
- Collaborating between data science and business stakeholders
- Setting achievable goals with measurable impact
- Understanding the significance of domain knowledge
- Managing the quality of data
- Evaluating the infrastructure required
Taking the Path to Success
Onboarding the right talent is the first step toward good data science practice. The designations divide the approach into two, i.e., Business Centric Approach and the Data-Centric approach.
- The Business-centric approach: It involves the stakeholders and domain experts who focus on the business impact.
- The Data-centric approach: It involves the data scientists and focuses on using data to its fullest.
Setting Achievable goals is important to evaluate the scope of the project and avoid over-ambitious targets.
Domain experts play the critical role of setting the metrics, rating the errors, and checking the evaluation plans.
The quality of data is extremely important, and raw data needs to be converted into a useful form before applying it. In scenarios with a lack of data, it requires techniques like transfer learning and augmentation.
Infrastructure components, like data management, data security, and legal compliance, hold a high priority in every data science. Data lakes and ML Ops are the two methods that enable scientists to develop the right solutions.
Story of Great Client Collaboration
Creating a chatbot for Bluetooth-enabled glucometer manufacturer was an experience we will always cherish. What started as a challenging project ended with the client starting a spinoff company that now helps diabetic patients manage their health. Read more about it in the white paper.
Through this abstract, we tried to give a glimpse of the boundless world of AL/ML and Data Science. The white paper will give an in-depth detail of every aspect. Hope you enjoy reading it and get vital indicators for success with your data science projects.
Click here to receive your copy of the white paper - “Unlocking the Potential of Data Science, Machine Learning, and Artificial Intelligence".
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