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Your API as a Product: Thinking like a Product Manager [VIDEO]

The video for the talk I gave at the 2018 API Conference is now available online.

I have talked about a bit before, as well as sharing the slides, but one of my main take-aways is that we are all (mostly) in the business of building products - on a daily basis, whether we are coding, write docs, tests, change requests, specifications, designs - there is almost always an end product of our work, and the product decisions we make whilst building it has a direct impact on the end-users (people will have to read/amend your code, read your specifications, translate your designs, consume your APIs etc). With that in mind, it seems sensible that we look at what lessons we can take from the discipline of Product Management to help us make smart decisions in our day-to-day.
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API Conf 2018 - Product Management for Engineers

Last week I attended and spoke at the 2018 API Conference in Berlin.

Having written about the topic before, my talk was titled: Your API as a Product - Thinking like a Product Manager (really aimed at engineers/architects/technologists).

It was recorded, so hopefully I will be able to share the video in the future, but the thrust of the talk was based around the concept that we are all building products on some level, and even if we don't have direct input into a commercial product that we might be writing code for, we have some output: code, bug reports, designs etc. So it makes sense, if we are all building products, and those products will all have users and therefore a user experience (your code, bug reports, design docs etc will be used by others, or maybe you yourself will be the user), that we try and learn from the discipline of Product Management, as it is focussed on building better products and better user experiences for the end users.

Slides are below, and really your best bet is to scroll down to the references section at the end and start watching the real Product Managers' talks.
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JAXLondon 2017: Agile Machine Learning [VIDEO]

Last October a colleague and I gave a talk at the JAXLondon Conference about Machine Learning in an agile, commercial environment (I then also gave the talk again in November in Munich at the W-JAX Conference).




The video of the talk is now available - the first half (and end section) is mostly softer stuff, where I talk about lessons learnt from doing ML research in a commercial environment and the middle section is my colleague, Sumanas, talking about how Word2Vec works and some more interesting demos of using it in an interesting application!




In related conference speaking news, I will be in Berlin next month for the API Conference to talk about Product Management and API Design: https://apiconference.net/api-design-documentation/your-api-as-a-product-thinking-like-a-product-manager/
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Conference updates: JAXLondon and W-JAX 2017

I have had the pleasure of closing out this year by speaking at two conferences. The first in October was JAXLondon, the second in November was W-JAX in Munich - the talk was titled "Agile Machine Learning: From theory to production".



As the title might suggest, the talk was about some considerations and challenges of doing Machine Learning(ML) work in a commercial environment - so a lot of softer aspects of ML: when you should adopt it, how you can work as a team on ML.

The talk at JAXLondon was recorded, so hopefully at some point that will be available for me to share as well, but for the time being, here is a brief interview that my co-speaker and I gave ahead of our JAXLondon talk.



See more details and write up here
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Agile Machine Learning: From Theory to Production

Later this year, Sumanas and I will be co-presenting a talk about researching Machine Learning in an agile development environment at the JAXLondon conference. This is a high level overview of some of the topics we will be presenting (we will also try to get some cool ML demos in there too, just to make things a bit more interesting).

So what’s the problem?

Artificial Intelligence(AI) and Machine Learning(ML) are all the rage right now - at Google’s recent I/O 2017 event they really put ML front and center with plans to bake it into all their products, and lots of other large companies are aligning themselves as Machine Learning companies as a natural progression from Big Data.

According to a recent Narrative Science survey, 38% of enterprises surveyed were already using AI, with 62% expecting to be using it by 2018. So it would be understandable if many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve, let alone how it might fit into a traditional engineering delivery team.

We have been working the last 12 months on taking a new product to market and trying to go from a simple idea to a production ML system. Along the way we have had to integrate open ended academic research tasks with our existing agile development process and project planning, as well as working out how to deliver the ML system to a production setting in a repeatable, robust way, with all the considerations expected from a normal software project.

Here are a few things you might consider if you are planning your ML roadmap (and topics we will cover in more detail in the JAXLondon session in October)

Machine Learning != your product

Machine Learning is a powerful tool to enhance a product - whether it be by reducing costs of human curation, or more powerful understanding for your voice/natural language interface - however, Machine Learning shouldn’t be considered the selling point of the product - think of the end result Product First, that is, is there a market for the product regardless of whether it is powered by ML or human oversight. Consider if it makes sense to build a fully non-ML version of the product to start proving the market fit and delivering value to customers.

Start small and build

The Lean Startup principles of MVP and fast iterations still apply here: following on from the point above, starting with a non-ML product, as you start to apply ML techniques, if you are able to start leveraging some of these techniques to get even a small increase in performance (better recommendations, reduced human effort/cost, improved user experience - replacing human process with ML for just 5% of cases can start to realise cost benefits) then that can start adding value straight away. From a small start you are able to start proving the value that can be added whilst also getting the ML infrastructure tested and proven.

Tie into development sprint cycles

You may be hiring a new R&D team, or you may be using members from your existing engineering team, either way, it is helpful to have them working in similar development sprint cycles (if you work in sprints). It will allow both sides of the teams to understand what is happening and how work is progressing - product and engineering changes and issues might be useful in informing the direction of R&D and likewise, there may be data features or feedback from the R&D team that could be easily engineered and would make things simpler for research. Whilst research is ongoing, and often a time consuming task, having fortnightly (or whatever the sprint length is) checkpoints where ideas can be discussed and demoed can be good for the whole team’s understanding as well as being a positive motivator.

Don’t forget Clean Code!

Whilst experimenting and researching different ideas it can be pretty easy to fall into hacking mode, just rattling out rough scripts to prove an initial concept or idea - and there is definitely a place for this, but as your team progresses it will be more beneficial to actually invest in good coding principles. Whilst one-off scripts make sense to be hacked out, as the team works across several ideas, having code that is re-usable and organised sensibly with proper separation of concerns can make the research in the future easier, as well as reducing the cost when it comes to the production-isation. Investing in some machinery to make experiments easily testable (and benchmarking different solutions) will be very beneficial to invest in from the start.

Recommended Reading

While the interwebs are awash with Machine Learning articles, tutorials and click-baitey links guaranteed to reduce the error of your model in 3 quick steps, the following is a small list of resources that we think are worth browsing.



For the academically inclined, the following is a list of papers, both recent and not so recent:




During the session in JAXLondon later this year, we will go into more detail with these ideas, as well as others including technical and architectural considerations for building and deploying an ML stack.
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