Actually. My role, I’ll call, has two major focuses in two areas. One in all them is I lead the machine learning engineering operations of the corporate globally. And however, I provide the entire analytical platforms that the corporate is using also on a worldwide basis. So in role primary in my machine learning engineering and operations, what my team does is we grab all of those models that our community of knowledge scientists which can be working globally are coming up with, and we grabbed them and we strengthened it. Our major mission here is the very first thing we’d like to do is we’d like to be sure that we’re applying engineering practices to make them production ready and so they can scale, they may also run in a cheap manner, and from there we make sure that in my operations hat they’re there when needed.

So a number of these models, because they turn into a part of our day-to-day operations, they’ll include certain specific service level commitments that we’d like to make, so my team makes sure that we’re delivering on those with the proper expectations. And on my other hand, which is the analytical platforms, is that we do a number of descriptive, predictive, and prescriptive work when it comes to analytics. The descriptive portion where you are talking about just the regular dashboarding, summarization piece around our data and where the information lives, all of those analytical platforms that the corporate is using are also something that I deal with. And with that, you’ll think that I actually have a really broad base of shoppers in the corporate each when it comes to geographies where they’re from a few of our businesses in Asia, all of the method to North America, but in addition across the organization from marketing to HR and every thing in between.

Going into your other query about how machine learning helps our consumers within the grocery aisle, I’ll probably summarize that for a CPG it’s all about having the proper product at the proper price, at the proper location for you. What which means is on the proper product, their machine learning may help a number of our marketing teams, for instance, even after they are actually with the most recent generative AI capabilities are showing up like brainstorming and creating recent content to R&D, what we’re attempting to determine what’s one of the best formulas for our products, there’s definitely now ML is making inroads in that space, the proper price, all about cost efficiencies throughout from our plans to our distribution centers, ensuring that we’re eliminating waste. Leveraging machine learning capabilities is something that we’re doing across the board from our revenue management, which is the proper price for people to purchase our products.

After which last but not least is the proper location. So we’d like to be sure that when our consumers are going into their stores or are buying our products online that the product is there for you and you are going to search out the product you want, the flavour you want immediately. And so there is a big effort around predicting our demand, organizing our supply chain, our distribution, scheduling our plans to be sure that we’re producing the proper quantities and delivering them to the proper places so our consumers can find our products.

Well, that actually is smart since data does play such a vital role in deploying advanced technologies, especially machine learning. So how does Kraft Heinz make sure the accessibility, quality and security of all of that data at the proper place at the proper time to drive effective machine learning operations or MLOps? Are there specific best practices that you have discovered?

Well, one of the best practice that I can probably advise people on is unquestionably data is the fuel of machine learning. So without data, there isn’t a modeling. And data, organizing your data, each the information that you’ve internally and externally takes time. Ensuring that it isn’t only accessible and you might be organizing it in a way that you simply do not have a gazillion technologies to cope with is essential, but in addition I’d say the curation of it. That may be a long-term commitment. So I strongly advise anyone that’s listening without delay to grasp that your data journey, because it is, is a journey, it doesn’t have an end destination, and in addition it should take time.

And the more you might be successful when it comes to getting all the information that you simply need organized and ensuring that is obtainable, the more successful you are going to be leveraging all of that with models in machine learning and great things which can be there to really then accomplish a selected business end result. So an excellent metaphor that I prefer to say is there’s a number of researchers, and MIT is understood for its research, however the researchers cannot do anything without the librarians, with all of the those that’s organizing the knowledge around so you possibly can go and truly do what you’ll want to do, which is on this case research. Always remember that data is the fuel, and data, it takes effort, it’s a journey, it never ends, because that is what is admittedly what I’d call what differentiates a number of successful efforts in comparison with unsuccessful ones.

Getting back to that right place at the proper time mentality, inside the previous few years, the buyer packaged goods, otherwise you mentioned earlier, the CPG sector, has seen such major shifts from changing customer demands to the proliferation of e-commerce channels. So how can AI and machine learning tools help influence business outcomes or improve operational efficiency?