Guru Mantras – What works for a successful AI, ML & Data Science implementation

Value of the problem - Before you start solving your analytics use case, ask yourself – how significant the change will be for the business if you get the perfect answer to your question. If the change is not significant enough don’t even bother to start solving it. With enough data most questions can be solved but is it worth the effort ?

You get paid for the business solution not the technical engineering - The objective of your project should be a business problem or a strategic solution. If you see yourself solving a tactical or IT problem, remember you are impacting the means to an end but not the end.

We ourselves are the most advanced intelligence - Whenever you are thinking of a problem try to think how our brain would have solved it. Although our solutions are not as sophisticated as the brain they were all inspired by them Like when you basket a ball think how our brain considers different things like our height, distance from the basket, strength of the wind, our angle from the basket etc. and then determines the strength of our throw and how it gets better with time. So when you build the machine how it should process the same things and get better with time.

What is success ? Understand from your customer what success means to them. Define success as part of the project scope. Try not to promise any particular number that you will achieve as part of your algorithm outcome like 95% accuracy as part of the scope. Explain the algorithm outcomes. Try to explain what the algorithm does in a simplistic manner. The business will be much more open to include algorithmic outcomes as part of their decision making processes if they have some intuition of what the algorithm does.

Some MLs are black boxes. Understand that some ML models given enough data to train works. We actually tell it how to understand and process the data. We actually don’t know how internally it is differentiating. For example we are currently running a project to differentiate between a clean and messy room. And we have tremendous success but we really don’t know how it internally differentiates between the two.

Remember AI winter. AI winter is a period of reduced funding and interest in artificial intelligence research. The AI winter was a result of hype, due to over-inflated promises by developers, unnaturally high expectations from end-users, and extensive promotion in the media The term was coined by analogy to the idea of a nuclear winter. It has happened in 70 and 80s. So do not be pressurized to say yes to something which the business says they have read about or seen somewhere else. Understand there is a lot of false hype around and you should be sure what you are promising can be build in a feasible manner.

Cannot build castle on air Algorithms are almost as good as the data – in terms of quality and size, and the infra on which it runs. So before you engage that data scientist ask your self do I have the required data in terms of both size and format and in consistent quality available on my repository to run the algorithm on. And also do I have the processing power and big data infra set up to handle that processing. If you don’t have these, building the algo should not be your first priority. Do remember, many of the concepts we are using now were always there it’s our processing power which have brought them back to spotlight.

• Don’t fight the big techs Use domain knowledge that you have gathered over years of being in business i.e. tricks of trade or domain business knowledge. It can be years of building cars, manufacturing things or making software products.

1. · Avoid direct competition with the tech giants. If the product is too generic in nature they will build it faster and better with their deep resources.
2. · Integrate data science closely with business products. Analytics should be out of the box and intuitive. If required collaborate with the tech giants’ offerings but never give away domain expertise.
3. · Enable data science teams with domain knowledge. Celebrate people who are domain experts and make them part of the data science team.

And we know big tech 4 – (Facebook, Amazon, Microsoft, Google) strengths.

1. Deep funds available to do experimental innovation. Significantly less pressure to go profitable.
2. Army of engineers and scientists.
3. Vast computer infra. So vast that a company can rent their unused infra and it can become one of world’s biggest business (read AWS)

• Systems are difficult to bring together I often see people talking about planning to bring systems together like taking walk on a park. It is rather like swimming across Arctic ocean. Think about bringing Twitter and Facebook together and identifying both are same person.In most cases the PII data cannot be used due to data security.Data level is a great challenge.
• Few things are difficult to predict and action Stock market is one such example.

1. It highly depends on the principle of game theory. That is you are very much dependent what others are doing. In a way you are trying to predict other’s behavior rather than the market.
2. Lot of fake and misleading news in the net
3. Influencing factors keep changing. We never knew Trump tweets can change the course of the market. But you can appreciate the trend in the long run.