I'm excited to bring you this conversation with Sayanti Ghosh. Sayanti is a Sr. AI/ ML product manager at Teck Resources — one of Canada's leading mining companies. Sayanti manages a recommender systems product at Teck to support clean coal processing. During this conversation, she shared her thoughts on assembling an AI/ ML team, build vs. buy decisions, and the types of risks/ KPIs she monitors.

Links

Sayanti On LinkedIn

Transcript

[00:00:00 ] Sayanthi Ghosh: if you wanna go for build, very important to see where the company stands in AI product, maturity level. Is it just starting? Is just in an experimentation phase? Is it in the level of using AI in few of the products? Or it is in a phase, or it is in a phase where it is into the DNA of the organization.

[00:00:21 ] Himakara Pieris: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate and learn from industry leaders who are solving real-world problems. Using AI.

[00:00:33 ] Himakara Pieris: I'm excited to bring you this conversation. Shanti gauche Shanti is a senior AI ML product manager at tech resources. One of Canada's leading mining companies. Shout the managers recommended systems product at tech to support clean coal processing. During this conversation, she shared her thoughts on putting together an AI ML team build was this by. 

[00:00:53 ] Himakara Pieris: Types of risks and KPS. She monitors. Check the show notes for links. Enjoy the show.[00:01:00 ]

[00:01:00 ]

[00:01:01 ] Himakara Pieris: Shanti, welcome to the Smart Product Show. 

[00:01:03 ] Sayanthi Ghosh: Thanks, Hima. Thanks for giving me this opportunity 

[00:01:07 ] Himakara Pieris: tell us a bit about what Tech Resources does and also how you use AI and ml. 

[00:01:14 ] Sayanthi Ghosh: Tech Resources is a mining company. Tech is a hundred plus years old company who works in copper, zinc, and , steel making coal.

[00:01:25 ] Sayanthi Ghosh: Tech resources also has another wing, which is race. And tech digital. So that's the part where they work with all the AI and ML products. The whole idea is to increase production and there are various other problems in supply chain, in mining, in blending. So there are various aspects and opportunities at Tech where AI and ML and other software engineering products help them solve these critical problems to, , grow their mining, to grow their production, and make it much more sellable product for their customers.

[00:01:59 ] Himakara Pieris: [00:02:00 ] What kind of AI and non-AI products are you involved in, and how do you draw that line? 

[00:02:08 ] Sayanthi Ghosh: It's an interesting question . So before we jump into the kind of AI and non-AI products, let's just, In one line, just give an idea of what we mean by AI and what we mean by non-ai. So anything that you would have a machine to train to and a machine could learn, we broadly put them into ai and anything that is rule-based, which doesn't have any learning capacity those type of things, we broadly put them into non-ai.

[00:02:38 ] Sayanthi Ghosh: So at Tech, what I do specifically with ai, we run recommendation systems. So think about it as a factory and I am in coal processing, so my work is in the domain of coal cleaning the coal. So think about there is a factory, you mine some coal. You need to clean that coal before you sell it to [00:03:00 ] your customers.

[00:03:01 ] Sayanthi Ghosh: So when you are mining that coal, And when you are cleaning that coal, your goal is to maximize the production of the coal. So you do not wanna lose clean material while you are cleaning it. So, If there is a factory to do that, there are several machines, ? And you want those operators to run the machine in a, in its most efficient way, so that you clean and get the maximum amount of coal.

[00:03:28 ] Sayanthi Ghosh: Here you have a digital product which recommends these operators. What should be specific? Set points or parameters that you would put in each of these machines so that your machines are optimized. There are trade offs. I'm not going into too much technical detail, but there are trade offs, and then at the ultimate goal is to get maximum amount of clean coal.

[00:03:55 ] Sayanthi Ghosh: There are a few parameters. Also, we have to meet few [00:04:00 ] specifications, so the idea is to meet those specification and also maximize the coal amount. So that's where my AI product comes in. So it's a recommendation system. So it has got a bunch of machine learning programs underneath and an optimizer on top, and then it sends out recommendations.

[00:04:19 ] Sayanthi Ghosh: So this is one of the AI product, and to your question, the non-AI product. Now think about you clean the coal. So your machine learning recommendation system did great job. You cleaned the coal, you have lots of clean coal, now you need to send it to your customer. So there is a whole supply chain method running so you put it in a train, you first load it into the train. Train goes into the port. From the port, it goes to your customer. So there is a chain of events going on, and there is. Non-AI software engineering based product, which helps us optimize the amount of coal that we put into our trains.

[00:04:58 ] Sayanthi Ghosh: So this is a very [00:05:00 ] high level though, but this is an example of my AI and non-AI product that I work with. 

[00:05:06 ] Himakara Pieris: How do you decide when to use AI and when to use a traditional or rule-based system? 

[00:05:12 ] Sayanthi Ghosh: The first thing I would always say, if you see that it can be solved without ai, don't overkill with ai. If it can be rule-based, go for rule-based solution. 

[00:05:24 ] Sayanthi Ghosh: Then the second thing you need to look into is data. It's very, very critical. You need a lot of amount of good training data because ai, without good data, it's like garbage in, garbage out. So you need to make sure you have relevant data, good amount of data, and the third important pillar is, Is your organization and your user ready for it, the cultural readiness to have an AI solution.

[00:05:51 ] Himakara Pieris: I also wanna start at the. Point of recognizing whether you need AI for something, is that based [00:06:00 ] on inability to describe , the outcome effectively using a set of rules, what kind of criteria goes into making that determination?

[00:06:11 ] Sayanthi Ghosh: It depends. So what is the problem that you are solving and what is the goal that you wanna achieve? Now, it could be that the goal that you wanna achieve is not at all possible by a rule-based system. Why it is not possible. If you would have a lot, if you have a data and you want your system to learn.

[00:06:33 ] Sayanthi Ghosh: Get trained and then behave in a certain way and provide an outcome. In that case, I don't think you can end up writing so many rules, but you can also think of like there were chat bots in past, or even now they have rule set up and the chat bot is working fine, but then you need. Much more advanced. So now with modernization, with time, AI is a lot more [00:07:00 ] understanding and adapting as well, 

[00:07:02 ] Sayanthi Ghosh: so if you need that system to learn, Then probably a rule-based solution is not an ideal way. So it depends upon what is the problem, and what do you have? What kind of data do you have? It could happen that you know that you need ai, you know that you need a system which should learn, but then you don't have the data, or it is extremely expensive to get to that data, and you need a lot more time to even acquire the data. In that case, even if you want an ai, probably you have to think it in a different way. That you probably need more time to find the AI solution till you reach that solution.

[00:07:44 ] Sayanthi Ghosh: Until you gather that data, you need a non-AI solution to sustain. 

[00:07:49 ] Himakara Pieris: (lets discuss your framework ) Let's go into the framework , , love to learn the process that you, use and follow.

[00:07:55 ] Sayanthi Ghosh: As I mentioned, starting with the problem, so always. You understand who [00:08:00 ] are your user, customer segment, and then you go deep dive into the problem,

[00:08:04 ] Sayanthi Ghosh: you need to check if you have enough information or enough data available in case your team has suggested that AI is the only solution or the best solution.

[00:08:15 ] Sayanthi Ghosh: If you see there is an option or a solution that can go without AI fulfills the business needs. Fulfills the value or solve the customer pain point. Go for non-ai 

[00:08:28 ] Sayanthi Ghosh: once you do that, now you are in a space where you know about the problem. You have your vision ready. Try to figure out if, how easy or difficult it is to access the data. And how expensive it is. 

[00:08:44 ] Sayanthi Ghosh: Understand how can you access the data? How can you integrate with your current system? That's the second checkpoint. 

[00:08:51 ] Sayanthi Ghosh: Third is checking the current state of the data. So what do you have right now, what amount of data that you have, and if you need [00:09:00 ] more information, is it an open source information that you can find?<