Artificial Intelligence as a whole is fast growing and, in that space, Machine Learning as a career is booming.
Today, companies collect huge amounts of data, especially about their customers. Machine Learning takes that information, analysis it with the help of computer algorithm to make data-driven recommendations and decisions. The data could be text based but also location, image or voice based. Whenever Google, YouTube, Netflix or Amazon recommend you something, they use Machine Learning.
Behind this technology are people who can build, repair and maintain these systems. Demand for them is at an all-time high. Where a data scientist will analyze collected data to identify valuable, actionable insights from a database, a machine learning engineer will design the self-running software that makes use of that data and automates predictive models.
Since machine learning engineers sit between different disciplines of IT, when trained correctly, they have a foundational knowledge in software engineering principles which is combined with data science in order to produce models that become valuable software. This means that machine learning engineers need to have a slate of skills that span both data science and software engineering.
This post looks at what essential skills every Machine Learning engineer will need for success in their career field.
Technical skills needed:
- Software engineering skills. Some of the computer science fundamentals that machine learning engineering rely on writing algorithms that can search, sort, and optimize; familiarity with approximate algorithms.
- Data science skills. Some of the data science fundamentals that machine learning engineers rely on include familiarity with programming languages such as Python, SQL, and Java.
- Advanced machine learning skills. Many machine learning engineers are also trained in deep learning, dynamic programming, neural network architectures.
Soft Skills needed:
- Communication skills
- Problem-solving skills
- Time management
- Teamwork
- Thirst for learning
Now let’s take a look at the kind of tools that your typical machine learning engineer would use. Amongst the programming languages used are Python and SQL.
South Africa is making efforts to stay at the forefront of developments in Machine Learning, as well as working to solve some of the challenges that we face in this space.
One of the local drivers of change in the industry is Mr Vukosi Marivate, the Chair of Data Science at the University of Pretoria and co-founder of the Deep Learning Indaba. Marivate has been working on projects to improve tools for and availability of data for local languages.
Its purpose is to monitor and analyze the use of African languages. The goal is to train AI to convert English to African languages and vice versa more successfully and accurately, as well as using this AI in other ways to make the internet as a whole more accessible to African language speakers.
In 2013, a local group of industry practitioners and researchers began Data Science Africa, an annual workshop for sharing resources and ideas.
The shift to making Africa a location and participant in AI conversation is a positive one and will ensure that local content and languages are considered and job opportunities created in this dynamic space.
