About Me
I have over 20 years of experience in helping enterprises manage data, and more than half of this in building scalable platforms for analytics and machine learning.
Call it timing, luck or destiny, I was able to gain exposure to a variety of areas like Linux, Unix, Networking, Data Integration, Analytics, Big Data, and Machine Learning. My exposure to such varied set of technology areas, helps me craft solution architecture, that's not only easy to maintain but also economical.
I'm originally from Mumbai, India, but got the opportunity to work in Malaysia, Singapore, Indonesia, Thailand, Sri Lanka, Finland, Sweden, Denmark, Norway, UK, and Germany, If we speak about my customers, they have been virtually in every country. I have gained immensely from this international exposure which not only reflects in my work but also in my personality.
Currently I'm based in Munich, and when I'm not working, I enjoy hiking and snowboarding in the mountains nearby.
If you like my courses, you are welcome to follow me on Linkedin where I regularly post
About The LearnDataArchitecture.com
Machine learning models are only as good as the data they are trained on, which is why understanding data architecture is critical for data scientists building machine learning models.
This course will teach you:
- The fundamentals of data architecture
- A refresher on data types, including structured, unstructured, and semi-structured data
- DataWarehouse Fundamentals
- Data Lake Fundamentals
- The differences between data warehouses and data lakes
- DataLakehouse Fundamentals
- Data Mesh fundamentals for decentralized governance of data
- The challenges of incorporating streaming data in data science
- Some machine learning-specific data infrastructure, such as feature stores and vector databases
- The course will help you:
- Make informed decisions about the architecture of your data infrastructure to improve the accuracy and effectiveness of your models
- Adopt modern technologies and practices to improve workflows
- Develop a better understanding and empathy for data engineers
- Improve your reputation as an all-around data scientist
Think of data architecture as the framework that supports the construction of a machine learning model. Just as a building needs a strong framework to support its structure, a machine learning model needs a solid data architecture to support its accuracy and effectiveness. Without a strong framework, the building is at risk of collapsing, and without a strong data architecture, machine learning models are at risk of producing inaccurate or biased results. By understanding the principles of data architecture, data scientists can ensure that their data infrastructure is robust, reliable, and capable of supporting the training and deployment of accurate and effective machine learning models.
By the end of this course, you'll have the knowledge to help guide your team and organization in creating the right data architecture for deploying data science use cases.
My Skills and Experiences
Visit my Linkedin Profile to learn more.
Udemy Course - Lowest Price
Avail the lowest price for my BESTSELLING course "Data Architecture for Data Scientists" on Udemy by clicking on the course thumbnail below.
