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The journey of the creators of SimpliSmart

Amritanshu grew up in Delhi with parents who encouraged him to pursue engineering. He has been using computers since he was four years old and was fascinated by the ability to draw and color in MS Paint. As he grew older, he experimented with different software and got his first taste of programming in 8th standard. Despite not being able to study computer science at BITS, he joined the Department of Visual Media and found the experience to be life-changing. He became more outgoing and surrounded himself with brilliant individuals who inspired him. The culture of people taking action and delivering motivated them to pursue their passions.

As he did not perform well academically at BITS, which prevented him from sitting for placements. He got an off-campus internship and worked hard to get a pre-placement offer there. He learned about the importance of using data properly and making a real-world impact. He eventually joined Oracle Cloud AI, where he worked on building language services for healthcare. His roommate, Devansh, was also pursuing a career in ML engineering, and they had always talked about starting a business together. Eventually, they did.

They created a new product and realized the potential of their creation when a company offered to buy it. This marked a turning point where they realized they could start a company based on their product. Since then, they have experienced a roller coaster of highs and lows as founders.

Using The Right Tools Can Make a Big Difference

He and his friend Devansh had worked at different companies including Avaamo, Capillary, Oracle Cloud, and Google, and noticed that there is a need for an MLOps platform to manage ML/DL workloads in a standardized and governed manner. They decided to create a platform that allows users to build, deploy, and manage models in production. We realized that current AutoML platforms lack generalizability, transparency, and flexibility. They saw a similarity between the DevOps lifecycle problem in the early 2000s and their current situation, and they aimed to build a declarative platform that can automate the process for both beginners and seasoned engineers. Our goal is to create a deep learning cloud.

The platform is easy to use even for people who don't know much about data science, but it may depend on the size and structure of the organization. If the organization has a data Scientist, that's great, but business users can still understand the demo and get started on the platform. In fact, 35% of initial customers come from non-technical backgrounds.

Focus on Improving while keeping a balance

Work-life balance is not the same for everyone and should not be generalized. However, if you don't find a balance, it can lead to burnout and a cycle of frustration. It's important to find the right balance for yourself, which may differ from others and may keep changing throughout different phases of life. There is no set standard for work-life balance and it's up to individuals to find what works best for them.