“Machine learning tends to have a Python flavor because it’s more user friendly than Java”
Artificial Intelligence and Machine Learning are all the rage right now. JAXenter editor Gabriela Motroc caught up with Sumanas Sarma and Rob Hinds at JAX London 2017 to talk about engineering best practices that can be applied to ML, their favorite programming languages and libraries for machine learning, and when it’s wise to jump on the ML bandwagon.
When academics move to an industrial setting from a scientific computing environment, a language like Java might be a little more intimidating and Python is way more user-friendly.
“Machine Learning encourages you to think about problems that previously you might not have considered”
Artificial intelligence and machine learning are all the rage right now. Many companies feel the pressure to invest in an AI strategy before fully understanding what they are aiming to achieve. JAXenter editor Gabriela Motroc caught up with Sumanas Sarma and Rob Hinds at JAX London 2017 to talk about the different types of ML tasks, the most suitable programming language for machine learning, when it’s wise to jump on the ML bandwagon and more.
If you are planning your ML roadmap, take into consideration the following aspects:
- Machine Learning is a powerful tool to enhance a product
- The Lean Startup principles of MVP and fast iterations apply here
- Tie into development sprint cycles
- Having code that is re-usable and organized sensibly with proper separation of concerns can make the research in the future easier
If you want to learn how to incorporate machine learning into your development cycle and how to adapt your process to include machine learning while still staying true to agile and lean principles, check out Rob’s article.
Sumanas Sarma is currently on a work placement at Basement Crowd whilst completing a Masters in Software Engineering from Queen Mary University of London. His focus in the past year has been on Machine Learning applications and before that he spent 4 years at a start-up in the finance industry using Groovy.
Rob Hinds is currently an Engineering Team Lead at legal-tech startup BasementCrowd. Having previously spent 4 years working at an investment management start-up in London, building a cutting edge trading platform and market place for money managers and prior to that, 6+ years working as a technical consultant at Accenture, working on a range of technology-driven, client-facing projects based across Europe.
If you’d like to learn more about the difference(s) between supervised and unsupervised learning, the most suitable programming languages for machine learning, the top three engineering best practices that can be applied to machine learning and the biggest misconception about machine learning, check out this interview.