Careers in Data and Engineering
Adopting the latest in ML and innovation to get the value out of data lakes create lucrative careers in ML
Data Analyst
A Data Analyst collects, cleans, stores and organizes data, does a thorough routine analysis using tools like Excel and SQL to generate reports and works towards answering business-related questions and helps in making business decisions.
Data Engineer
A Data Engineer builds and maintains the data infrastructure other data team members use to perform various tasks. They design and maintain the infrastructure needed to collect, process, and store data.
Data Scientist
A Data Scientist develops and implements data-driven solutions to overcome business challenges using advanced technical skills like machine learning and programming languages like Python to create models to predict future trends, often relying on unstructured and ambiguous data.
ML Engineers
ML Engineers typically work as part of a larger data science team and communicate with data scientists, deep learning engineers, administrators, data analysts, data engineers and data architects. Their role is to design and create AI algorithms capable of learning and making predictions that define machine learning.
MLOps
Machine learning operations (MLOps) is a combination of the words "machine learning" and DevOps, a software development practice that focuses on continuous delivery through Experiment tracking, Model deployment, Model monitoring, and Model retraining , thus helping data scientists and engineers improve product deployment efficiency, and organizations improve scalability and reduce regulatory risks.