A Voyage of MLOps Discovery

So you used the goodness of DevOps and Agile methodologies to release an app that delivers real business value by automating processes, reducing swivel-chair and simplifying the user journeys (process before tools!). You made the app to be container based with microservice goodness, compliant REST APIs and five types of databases (cloud native of course!).

As your users start using this app – you are amazed at all the high quality and timely business data that is being collected thanks to shifting away from manual processes. You also get tons of data about the app that helps you support it and improve its reliability.

Suddenly some bright spark has the idea of processing all this business and app data to answer questions like ‘Can we forecast customer order journey time?’ and ‘Can we predict journeys that are likely to get stuck?’

You just got your first business problem with those magic keywords of ‘forecast’ and ‘predict’ that allows you to take scikit-learn for a spin!

You discuss with the benefits owner how they will measure the benefits of this. Thereafter, on a Friday afternoon you find yourself installing python, sklearn and downloading some data.

Congratulations – you have taken your first steps in MLOps – you are planning to build a model, understanding what features to use and thinking about how to measure its business performance.

Over the next week or so you build some forecasting and classification models that give you good results (business results – not AUC!). The business benefit owner is impressed and gives you the go ahead to generate a report every week so that such orders can be triaged early. Now you need to start thinking about rebuilding the model regularly, checking and comparing its performance with the active model. You don’t mind running the model on your laptop and emailing the report every week.

This is your second step in MLOps – to understand how to train/retrain your model and how to select the best one for use. For this you will need to establish feature pipelines that run as part of the training process and ensure the whole thing is a one-command operation so that you can generate the report easily.

Then someone has a good idea – why not create a dashboard that updates daily and provides a list of order journeys with a high probability of getting stuck so that we further improve our response time AND provide the order completion forecast to the customers for better customer service (and to reduce inbound service calls). 

This puts you in a fix – because till now you were just running the model on your laptop every week and creating a report in MS Excel – now you need to grow your model and make it part of a product (the app).

It should be deployable outside your laptop, in a repeatable and scalable way (e.g. infra-as-code). Integrations also need to be worked on – your model will need access to feature data as well as an API for serving the results. Also you need to stand up your model factory that will retrain models, compare with existing ones (quality control) and deploy as required. You will also need to think about infrastructure and model support. Since it will be used daily and some benefit will depend on it working properly – someone needs to keep an eye on it!

This is the third and biggest step in MLOps that moves you from the realm of ad-hoc ML to an ML Product – with product thinking around it like assurance, support, roadmapĀ and feedback.

Now you have to check in all your code so that others can work on the app in case you are chilling on a beach somewhere.

This third big step brings hidden complexity in monitoring requirements. While the model was on your laptop being used on a weekly-basis you did not have to worry about the ‘model environment’ or automated monitoring. You had time for manual monitoring and validation.

Now that model will be deployed and run daily with the output being used to drive customer interaction we cannot depend on manual monitoring. Once the model is deployed it will continuously give forecasts (as orders are generated). If those forecasts are too optimistic then you will increase the in-bound call pressure as people chase up on orders. If they are too conservative then you will reduce sales as customers might find the wait too long. Also unlike software, your ML model could start misbehaving due to data drift (intended or unintended). If you don’t detect and correct for this the forecast results from the model could stop adding any value (the best case) or worse actually increase customer’s pain (the worst case).

In traditional software we would trap these data issues at the edge through validations and we would log these as error events. But here the data can make perfect business sense just that the model can’t make sense of it to give a forecast or a prediction.

Therefore we need to start checking data for data drift, model for bias, model performance, features for validity and the business performance of the app for continued benefit realisation. 

Also, this step by step approach for AI/ML is old school. We need to deploy a more continuous approach to discover new problems that can either be farmed off to multi-disciplinary teams if the org is mature enough or can be tackled in phases (going from analytics to prediction to prescription) if the org is still developing the right skill sets.

More on this later..

Survival/Demise of the Delivery Service Economy

Services contribute, by far, the largest amount to a countries economic output. Why? Because they are easier to setup, scale-up/down and close than say a manufacturing unit or a farm.

Last few years have seen massive growth in certain services even as other services declined. One example of the former is the ‘delivery service’ (e.g. Deliveroo) that delivers some product (e.g. food). Covid-19 helped accelerate the growth as people could not go to physical locations to access those products.

But how do these services work? Now that the lockdowns are over and people are not afraid to mingle, what will happen to such services? What factors will impact the future prospects of such services? Let us investigate.

Business Model

To answer the above questions we need to figure out how do delivery services interact with the products they deliver in terms of price and value. Now we know the sale price of any product (the price we see as consumers) includes service costs that went into making that product (e.g. chef’s services in cooking a dish). One of these services is product delivery that gives us access to that product (e.g. salary of the waiter in a restaurant or the delivery driver).

Delivery Fleet to Delivery Aggregator

Before delivery aggregators gave access to a large pool of delivery personnel, each producer had their own delivery fleet (e.g. Dominos, local take-aways etc.) cost of which was either included in the product sale price (see equation 1) or added on as a fixed delivery charge or calculated per delivery (e.g. courier).

Product Sale Price = Base Price + Delivery Price (1)

Consumers could only order certain items usually from local businesses. Constraints like minimum spend also were a pain point. Also you could only order from one business.

Now businesses need not maintain their own delivery fleet and for consumers more items can be ordered from a wider range of producers (with the ability to mix and match) sitting in the comfort of your home/office. This can be thought of as decoupling of the product from the delivery channel.

The delivery firms make money out of the perceived value of accessing the product with minimum effort (e.g. walking, driving, parking) where consumers are trading money for time. They save money through economies of scale and by (sometimes) treating employees like they are not employees (which reduces operating costs).

Final Product Price = Price Paid for Delivery + Product Sale Price (2)

We would also expect businesses to reduce the product sale price (see equation 1) because we are now accounting for the delivery price separately (see equation 2). This should lead to benefits all around – as consumers should pay a lower cost to access the product at home (which can be identified by comparing say take-away menu price vs price for home delivery via aggregator). But we know how pricing in general works. More processing some raw material requires before it can be consumed, less is the probability its price will ever fall. Why? Because with processing, cost increases and more factors come into play, read more here: Causes of Inflation

Trouble with this Business Model

There are few issues with this business model and equation (2) points to a big one. The demand for the service provided by delivery aggregators is entirely dependent on the demand for the products they deliver.

Therefore if the products they deliver are ‘luxuries’ such as take-away meals or restaurant dinners then the demand will go down if an economic downturn is expected (such as now). This is one reason you find delivery aggregators like Uber and Deliveroo are diversifying into daily-use groceries (which are not seen as luxury items).

Impact to Demand for the Service

The other thing that can impact demand for the service is the demand for the service! Remember, unless the product is exclusively sold through the delivery aggregator, people can still consume the product without consuming the delivery service. That is why you have exclusive tie-ups between producers and delivery aggregators (e.g. Nando’s and Deliveroo).

Figure 1: Some factors that can impact demand for delivery service.

There could be many reasons why demand for the delivery service changes. I have attempted to illustrate some of the reasons why in Figure 1. But given the seasonal, location and other factors I am sure there are many more.

Imagine a food delivery scenario where we look at two groups of people: one who live near the city centre (with high concentration of restaurants and take-aways) and the other who live in the suburbs (with low concentration of restaurants and take-aways).

For the City Centre group – distance to the eating joints is probably not a big pain point but still delivery is convenient (trading money for time). But for the Suburbs group it is and because delivery service allows them access to food from the City Centre, the Suburbs group is happy to pay a bit more for the delivery as it removes the big pain point of travelling to the City Centre, finding and paying for parking etc.

But this can change very easily. For example if the weather improves then City Centre group might enjoy a walk to the eating joint (even if they get a take-away). Or if the base price of the food increases it might encourage them to walk (especially given the health benefits of exercise).

For the Suburbs group – if the delivery price increases even if the base price of the food remains the same – they may choose to make the effort to get the food themselves. The delivery price can increase for many reasons – e.g. if there is high demand or cost of fuel goes up. Another factor could be the end of lockdown: the prospect of going to the City Centre may not be such a big pain point (especially when the weather is good or during holidays).

Concepts like ‘dark kitchens’, where food from different restaurants is cooked in the same kitchen, located in different parts of the city, are coming to address price variability, improve access and reduce costs.

What Does the Future Hold?

Given the slim margins there is very little room to increase spending without impacting the delivery price. Here are some factors that will decide what direction this space takes:

  1. Regulations: Given that gig workers can be easily left without any cover unlike regular employees there is a big push to reclassify delivery personnel which means giving them paid leave, sick pay and other benefits and reducing profits for the delivery aggregator
  2. Technology: Delivery is human-labour intensive and we will not be able to reduce costs easily. Technology such as drones can provide the next level of cost reduction but that doesn’t look like something around the corner
  3. Income Levels: Delivery Aggregators depend on disposable income of the consumers so they can pay that little bit extra for delivery. If income levels start to fall all these ‘little extra bits’ will start to bite. This can be seen in other areas as well like Content Platforms (e.g. Netflix, Disney+) where people are cutting down spending
  4. Product Experience: Experience around the product is just as important as the product itself. For example when we go to a grocery store we end up buying items not on the list or discovering new products. With delivery aggregators we cannot get that experience easily
  5. Lifestyle Changes: After the Covid-19 lockdowns and large scale work-from-home most companies are exploring different work arrangements. From flexible working arrangements to a 4-day work week. All these things impact the one thing that delivery aggregators are meant to save – time. With changes to work patterns people have more time to spare. Therefore, the value of time goes down and they may not want to ‘buy’ time with money

In general I don’t think we will see skyrocketing growth in this area and given the bleak economic output one can only predict a short term decline and longer term stabilisation.


The word above is on everyone’s mind these days. There is a lot of panic around rising prices. But what is Inflation and how can we understand it? In this post I want to present a simple mental model and work through some sources of Inflation – because not all inflation is the same.

Inflation is defined with respect to the rise in price of a basket of products. These could be consumer products or wholesale products. The key idea is to track the price of that basket. If the price is rising we call it inflation and the rate of change of this price is the often quoted ‘rate of inflation’.

Central Banks usually target an inflation rate slightly greater than 0%. Bank of England for example targets the rate to be in 0-2% range. But why do we need this to be a small positive number? Why can’t we freeze the prices? Won’t that be good for all? Let us try and understand this using a simple model.

The Price Model

To understand inflation we need to first understand how products and services are priced. That will help us understand how it can change.

Production Model with Factors of Input

When we consume anything – be it a service (e.g. Uber ride) or a product (e.g. chocolate bar) we pay a price that includes all the factors that went into production. The common factors are stated in the image above: Land, Capital (money and goods), Labour (skilled and unskilled), Raw Materials and Energy.

The price also includes the perceived value of the product or service, margins, cost of hidden services (e.g. HR, Legal, Finance) and tax costs. This is above the cost of production.

Input factors underlined in green can change very quickly due to various factors. It is said that current bout of inflation is due to rising energy prices (oil and gas) caused by the Ukraine war.

There are also hidden Services that are part of production (e.g. logistics) that are impacted by rising factors such as fuel costs.


Price = Perceived Value + Cost of Input Factors + Cost of Hidden Services + Margins + Taxes

Furthermore, Demand for a product along with its Price gives us one part of the Profit equation. After all aim of a selling a product or service is to make a Profit – which means the Demand and the Price should provide enough money to not only cover the outflows in terms of costs, margins and taxes but also to generate a return for the shareholders.

Origins of Inflation: Supply Side

Now we start looking at the Supply Side (from producers point of view) sources of price rise (inflation). A common thread in all these is ‘expectation’. If as a producer I expect people can pay a bit more I will try and raise the price before the competition catches on and make that bit of profit. Similarly, if as a seller I expect my input costs to rise (e.g. rise in interest rate, raw material costs or salary increases to retain talent).

Profit Motive and Perceived Value

Price can increase if I as a producer I feel:

  • there is extra money for people to spend (e.g. during a lockdown) and
  • the Perceived Value of my product is significant


  • supply is reducing
  • the Perceived Value of my product is significant

then I know increasing the price should not badly impact demand. And since the above information is not secret if many producers increase their prices it can lead to wider price rise as knock on effects kick in. Perfect case in mind: hand sanitisers during the early days of Covid-19 pandemic.

While not a driver for inflation – one example where we can see two different producers copying each other is the pricing of Apple smartphones compared with Samsung smartphones. In the beginning Apple devices were more expensive than Samsung Galaxy S devices. Now Samsung Galaxy S series is more expensive given that they have no ‘iPhone 13 mini’ equivalent and are therefore firmly aimed at the value buyer.

Factor of Production Price Increase/Supply Decrease

This is where the web of inputs and outputs transmits price rise from source to target (i.e. our pockets). It is also lot easier to understand. For example: fuel gets more expensive (e.g. due to a war) that leads to wholesale food price increase (as logistics depends on diesel) which leads to an increase in retail price of food and eating out. This leads to people demanding higher salaries to compensate which in turn starts impacting other industries (as their skilled / unskilled labour cost increases). Because salaries don’t rise as quickly as prices people end up cutting expenditure.

Rising Cost of Capital

If the cost of capital increases (i.e. rise of interest rates) then not only do producers find it hard to borrow money to expand, the consumers find it hard to borrow money to buy things (e.g. houses, cars, phones). Producers find it harder to repay debts (e.g. commercial loans, mortgages) and are forced to raise prices.


If there is an expectation for prices to rise they will rise. For example, in the current scenario where there is massive press coverage of pending energy price rise, businesses will start raising prices because energy bills and other costs are difficult to trim down quickly. Cost of living increases will also force labour costs to go up which will accelerate price rise.

Origin of Inflation: Demand Side

The other side of inflation is Demand Side (from the Consumers point of view). The standard story is when there is too much money in the market the ‘value of money goes down’ therefore you have to pay more for the same product. But I see that the same as expectation driven. Consumers don’t set the price, they set the demand where possible. I say where possible because you can only reduce expenses to an extent. The way money floods the market is also asymmetric.

The ‘right’ price is not a single thing. As I explained with my pricing model various objective and subjective factors go into setting the price.

Therefore Demand Side inflation can be thought of as a game. Producers and Consumers are trying to find what is the ‘right’ price that maximises the return for Producers and benefit for Consumers (or so called utility). The rules of the game are simple:

  • If the price falls below a certain level where Producers don’t generate any surplus at that level of Demand (i.e. their income) then there is no point in continuing to Produce otherwise the Producer won’t be able to Consume anything (unless they have other sources of income)
  • if the price rises above a certain level where it impacts more and more people thereby Demand reduces to an extent that Producers no longer generate any surplus (at that price level) then there is no point in continuing to Produce
  • therefore the game is to maintain the price between the two extremes while compensating for external influence (e.g. cost of capital, perceived value etc.)

Why a small positive number for Rate of Inflation?

This is because if there is expected growth in demand only then will there be a real growth in supply (no one will Produce excess goods unless they can be sold). With that in mind, for there to be expectation of growth in demand either the prices must fall or more money must be made available to buy the excess goods. Since it is difficult for prices to fall (especially given the Profit Motive and the fact that excess demand for Input Factors is likely to increase those prices) more money must be made available. With more money being made available we are seeding inflation.

What if we kept strict price controls?

What if there was a product P which didn’t depend on any imports. Not even for energy or logistics (no diesel trucks) or any outsourced service. I bet you are finding it difficult to think of many (any?) such products?

But let us assume there are products like that and we can keep prices of all the input factors constant (and the input factors going into producing those input factors and so on constant as well – it is a web at the end of the day).

So with that magic in place if we kept producing P at a fixed price then to still avoid inflation we must keep demand the same. Why? Because if demand is increasing (say due to population increase or expectation of shortage) and we don’t increase supply then there is an opportunity for arbitrage. People can buy and fixed price but sell at higher price to someone with greater need. Now if we increase supply to match the demand – we will transmit that demand down the supply web (i.e. we will need more input factors etc.) and at the end we will end up with someone owning raw materials (like oil, ore, IPR etc.) who will need to spend more money (e.g. wages, capital) to extract more. They won’t be able to transfer the increased demand down the web because they are at the edge. Now at that point these edge producers have an option to not raise prices: either get additional labour, output without increasing salary, interest bill or to reduce the profit margin. Assuming they reduce the profit margin (as generally people won’t work more for the same salary or accept less interest for a loan) they will avoid a price rise.

But what happens when the demand rises again (population keeps on increasing). There will come a time when profit cannot be reduced any further – and it will not be worth the edge producers while to remain in business. This is what happened during Covid-19 where the edge producers (in China) stopped producing at same levels – leading to price shocks around the world.


Hopefully I have peeled back some of the confusion around Inflation and its sources.

Some key points:

  1. Inflation is driven more by expectation than anything else
  2. Shocks can kick start inflation but it is the expectation that those shocks give rise to that really ramps up the price rise
  3. Shock -> Expectation -> Panic is the perfect storm for price rise
  4. Early inflation can lead to arbitrage opportunities where people buy cheap, hold and sell at a higher price

Thank you for reading!