sanketsarang

I have a slightly different view of this. Here are the key reason why Indian's are succeeding:

1. Speaking the facts does not exist in India. Everyone sugarcoats. The successful see-through sugar coating. They are able to see through the true intentions of their peers, allowing them to be better leaders.

2. Indian's do Jugaad from birth. It is a need, as many don't even have stable electricity to light up a study lamp. They find creative ways to live their lives. This Jugaad at an early age gives them the "Power of Unlimited Thinking". A critical component in being a visionary leader. Finding solutions where no one thought it was possible.

This is a groundbreaking achievement by humankind. Even greater than sending people to the moon. Imagine, every time you ate pork, someone received a kidney transplant.

I did work on making a database myself, and I must say that querying 100TB fast, let alone storing 100TB of data, is a real problem. Some companies (very few) don't have much choice but to use a DB that works on 100TB. If you do have small data, then you have a lot of options. But if your data is large, then you have very few options. So it is correct to be competing on how fast a DB can query 100TB of data; while at the same time being slow if you have just 10GB of data. Some databases are designed only for large data, and should not be used if your data is small.

Thanks Mark. Please do share your experience. Very keen on hearing how you find our project. We are still in beta, so lots of scope for improvement. We are open to any suggestions you might have.

That is not true actually. You must purchase an Azure VM for training a model. Deploying trained models on your own infrastructure is permitted. Reasonably speaking we would have to call this an enterprise solution, as there is no way to get a trained model without paying Azure fees. Most models will require GPU, and it is not like we can run AzureML on the free GPU offered by Google Colab.

Hi HN, we have seen a lot of AutoML frameworks out there. As a Data Scientist myself, I have refrained from using these because at the end of the day, you have to submit complete source code to your clients, not just a functioning model. That is why we created AutoAI. Given data and target (value to predict), it can automatically discover and fully train the best performing AI solution. Still, most importantly, it also goes on to produce high-quality Jupyter Notebook code. AutoAI does Whitebox AutoML. A much-needed feature for Data Scientists. Do give it a try, and let me know what you think.

Hmm... not really found another way of doing this. Would be very keen on seeing what others are doing.

The first thing I would do is check if your Github repository is showing the traffic source. If your primary documentation is on Github, then you would expect most people to visit it in order to figure out how to use your software.

Yes absolutely. Jupyter Notebooks are a classic example of success in this space. Many Data Scientists use Jupyter over the browser for day to day work. Including me.

Advantages: Allows you to use server resources than be limited by your laptop compute capabilities. Log in anywhere, means I don't have to carry my laptop around. As long as I have a browser, I get the same environment to work. Mental peace that my work and data are always safe. I can drop my laptop in a swimming pool and yet I will not even lose the last character I typed on that presentation I was making.

Disadvantages: Yeah, I cannot access my system while on an aeroplane. Can't think of anything else honestly, unless you live in a zone where the internet is as bad as on aeroplanes.

All in all, prefer the browser-based virtual machine any day. The advantages outweigh the single disadvantage of the need for a stable internet connection.