From a MSc in Data Analytics to Learning Machine Learning Operations (MLOps) in Real World Projects

Nov 29, 2022
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From a MSc in Data Analytics to Learning Machine Learning Operations (MLOps) in Real World Projects

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Vincent Omondi, a beginner data scientist, has been able to use his theoretical knowledge in Omdena’s challenges ranging from building an AI-powered chatbot to offer 24/7 support services for the university website to detecting fraud and spams in our systems.

What is your background?

I am an economics undergraduate and currently pursuing my master’s degree in data analytics; the major is data engineering at KCA University, Nairobi, Kenya.

What is your achievement?

As a data software engineer, I have been able to apply skills that I have been acquiring from Omdena by working with industry experts on projects to build data pipelines and visual analytics for customer experience team at my workplace. I have also experienced working with the team to detect fraud and spam in our systems. Omdena has also given me the platform to use the theoretical knowledge I acquire through my graduate program.

How has the Omdena experience helped in your work career?

Omdena has helped in my career as it has given me the platform to learn and acquire industry-ready skills, which have helped me in understanding MLOps and data engineering. These are the skills that I apply in my daily job.

Build your portfolio with real-world projects from Omdena

How did it change your worldview, and what was the biggest obstacle you overcame?

When I joined Omdena I was very green on data science. Later, I have learnt techniques of collaborating with very enthusiastic learners as well as industry experts. My skills in data engineering and MLOps have greatly been enhanced by Omdena.

KCA University, Nairobi, Kenya

KCA University, Nairobi, Kenya

Would you like to share any additional thoughts/tips?

Teamwork and collaboration are very important when working on a data science project. Data science is very diverse, and no single person can perfect each stage of a data science pipeline. Teamwork and collaboration, therefore, help a lot in making sure that the model developed follows the required industry standard and is deployed at scale.

Is there anything else you would like to add about your current life and work? 

Since joining Omdena, I have learned a lot of skills, especially about data engineering, and I am currently part of the team that is building a new data architecture and analytics platform at our organization, Focus Mobile. Omdena has also given me the platform to apply most of the theoretical knowledge I learnt in Msc data analytics course in a real-world setting.