07.12.2020Blog

Top 3 most valuable skills for Machine Learning Engineer

In this blog our ML Engineers Miia Niemelä and Anniina Sallinen reveal the top 3 skills of Machine Learning Engineer. If this sounds like your cup of tea, our Data Science team in Finland is currently looking for ML Engineers to join the crew!

What is Machine Learning (ML) Engineer and why is this title needed?

Increasing amount of data available for analytics purposes has burst the development of machine learning models in the majority of companies, which hire more and more data scientists, and more and more machine learning applications are being built as we speak. A typical data scientist has deep knowledge in mathematics and statistics and can build state of the art machine learning models with those skills. However, building production ready machine learning systems requires understanding from software development as well, though a generic software developer might not understand the details in statistics. Machine learning engineer acts as a bridge between these two worlds: ideally an ML engineer understands both worlds and is able to bring those two together in order to build production ready ML applications.

How to become an Machine Learning Engineer – top 3 skills:

Since ML engineer is an emerging role, not many have direct experience with it. A data scientist with a programming background could become an ML engineer, or a programmer with strong data skills could become an ML engineer. These are top three skills relevant to anyone desiring to become an ML engineer.

1. Machine learning

To develop machine learning systems, basic understanding of most common machine learning algorithms is essential. Some statistics and mathematics skills are helpful in understanding how algorithms work and choosing the best approach. If you are coming from a programming background, you might want to start with familiarizing yourself with the different kind of nature and characteristics of data science and machine learning development compared to traditional software development.

In data science development, data is the most important asset and it could be for example coming from sources in a semi structured format or reside a database in relational format. An ML engineer should be able to understand different data structures and a bit of data modeling in order to be able to solve complex problems on different data sets.

2. Software development and automation

The job of a ML engineer includes lots of programming. Data is fetched from external API’s and might need transformation before saving to the database. First steps of model development typically includes exploration of the data, and preprocessing it, and it is usually done with either Python or R. Understanding best practices helps to build a modular system with clean and maintainable code.

In addition to coding, understanding of DevOps and automation in deployments are important. MLOps is a set of practices extending DevOps that combine machine learning development and operations. The purpose of the practices is to have high quality, production-ready, automated systems. Practices include for example having everything, including infrastructure, as a code (IaaC). The code needs to be versioned, and for continuous deployment purposes there needs to be a CI/CD pipeline. In many cases machine learning systems are containerized, so containers and orchestrating them are highly appreciated skills. Read from our blog post what MLOps is and how it extends DevOps.

3. Cloud services

Many companies are operating their machine learning systems in cloud, which makes cloud experience also beneficial for an ML engineer. Cloud platforms have services specifically designed for machine learning development, which speeds up the development cycle. Data scientists and machine learning engineers can easily provision the needed infrastructure to run their model training jobs based on the computation power needed and only pay for usage. For example not many companies have their own GPU machines, but cloud makes GPU machines accessible to all. Most common cloud platforms used at Solita are AWS and Azure, but basically experience in any cloud platform is beneficial and previous experience helps learning new cloud platforms.

In relation to cloud services, it is good to understand microservices architectures in designing ML systems. Microservices means less dependencies between different parts of the system and improved change management. Having microservices also requires understanding about architecture: which cloud services to use for different purposes and how to orchestrate the services.

Did you got interested? 🙂

We are currently seeking for Machine Learning Engineers to join our data-community in Finland. Check the job ad (in Finnish) and apply, if this could be your cup of tea!