From playing checkers in 1952 machine learning has grown to become part of our daily lives and the technology to adopt for data-driven businesses. You interact with ML technology when your search on Google yields thousands of related results, if your account automatically spams unwanted emails, or if you talk to your smartphone and it responds to your query accordingly.
- In education, ML is helping learning providers to offer personalized learning paths based on learner profiles and engagements as well as to effectively deliver guided learning. For instance, if a learner is taking machine learning certification training, chatbots can be employed to answer queries, assign projects, and track a learner’s progress with his/her project or course.
- Emerging fintech companies have leveraged the power of ML for credit rating, automation of processes to allow customers 24 hours of digital banking and loan access services, as well as in offering self-help and voice-assisted solutions.
- In transportation, ML is used to offer self-parking services and manage traffic.
The global machine learning market as of 2017 was worth $1.41 Billion and is expected to grow to $8.81 Billion by 2022 at a CAGR of 44.1%. More and more businesses across the globe are adopting ML-powered solutions to drive business growth.
The high adoption rate of ML can be attributed to:
- The wide adoption of AI solutions
- The emergence of quantum computing
- Improved computer algorithms
- Availability of large data sets
- Increased awareness and demand for smart services and products
- growth of ML-powered applications
- The emergence of IoT and robotics
The future of machine learning
There is no denying that AI and machine learning are redefining industries like healthcare, banking and financial services, manufacturing and retail, IT, and transportation among others. It is unearthing better sales and marketing opportunities, improving operations, enhancing customer service, and digital security for businesses. While ML may not be new in technology, its full potential is yet to be realized.
Both large and small businesses will find it necessary to integrate ML in their systems and what will follow is mass adoption. No business will have second thoughts about using ML in its operations. Businesses are driving towards harnessing more machine learning power with cloud computing in what is known as the intelligent cloud.
We should expect more enhanced unsupervised machine learning algorithms for real-time data analysis of complex data sets because this is certainly where we are headed to and as humans, time and capacity will be the constraints that we will gladly hand over to ML.
Cloud today is limited to networking, computing, and storage but ML will definitely take it to another level when cloud service providers offer machine learning as a service (MLaaS) which allows businesses to take advantage of the cost-effective ML solutions to scale their operations.
There is so much more in the pipeline to the future in terms of security, emerging skills requirements, and more ML-powered innovations after the automotive cars.
The machine learning developer
Machine learning is a subset of artificial intelligence. Being that computers can today analyze raw data to discover hidden patterns and structures within them, machine learning involves feeding machines with data that they learn and improve from experience without being explicitly programmed.
A machine learning developer is a professional who designs and develops ML models and uses data to train these models. Models are usually used to automate processes for businesses to achieve efficiency and productivity.
Thanks to the wide adoption of AI and ML across industries there is a high demand for machine learning skills.
Roles of a machine learning developer
- A machine learning developer is responsible for designing, developing, and training ML models and implementing the right algorithms for them.
- In charge of data acquisition where more data sets are required.
- Works with data sets through the collection, cleaning, and preprocessing to be used for training models and deploying the models to production.
- Define preprocessing and feature engineering processes for given datasets.
- Analyzing ML algorithms, calculating their success probability, and aligning them with the business problems that they solve based on the calculated success probability.
- Testing machine learning models for errors and coming up with strategies to address these errors.
- Come up with strategies for data validation and design data augmentation pipelines
10 important Machine learning developer skills
While skills requirements for developers will vary from industry to industry, these 10 skills are general skills that an ML developer should have regardless.
- Good knowledge of mathematics, statistics, and probability
- Knowledge of the different types of machine learning including supervised and unsupervised
- Knowledge of programming languages like Python, *** and where applicable use of open source ML libraries like NumPy, Scikit-learn, and Pandas and frameworks
- Knowledge of data evaluation elements like visualization, manipulation, clustering, and modeling
- Data science and software engineering
- Deep learning frameworks like TensorFlow, PyTorch, Keras
- Proficiency in backend platforms like Linux, Windows, and iOS
- Knowledge of software design and engineering
- Knowledge of databases like Oracle, SQL, and others
How to become a machine learning developer
ML is a relatively new and complex field in technology. Opting to pursue a career as an ML developer can be very rewarding as the demand for skilled professionals is high. However, there has to be a starting point.
Having a formal education background in computer science, although not an express requirement, will give you an added advantage in your career path.
- Learn to code
Familiarizing yourself with coding libraries and frameworks for these languages will be an advantage as developers are today relying more on these frameworks as they offer tools and resources to ease development.
- You need math and statistics knowledge
In mathematics, you’ll need some knowledge on linear algebra, probability, and calculus while in statistics you need a good grasp of descriptive and inferential statistics. Learning statistics will help you lay your foundation on working with datasets.
- Lay a foundation in data exploration
Learning how to work with datasets is not only vital to building a successful career as a machine learning developer, data collection, cleaning, and preparation are the foundations of machine learning models. In fact, this is what developers spend most of their time doing. Access free datasets of your choice from Kaggle and start practicing some data preparation techniques.
- Introduction to machine learning- the theory
You have acquired some basic skills required to learn machine learning. It is now time to study the theory behind machine learning models. It is advisable, at this point, to consider an introductory course in machine learning where you will learn about creating ML algorithms, different types of ML, neural networks, and developing ML applications.
Course providers like Simplilearn have packaged their Machine Learning course with free Data Science with Python, Math refresher, and statistics courses and thereafter award an industry-recognized certification.
- Gain some practical knowledge
Once you have the theory basics of machine learning, it is now time to put your knowledge into practice. This you can do by:
- Try working on some personal or small projects for the sake of gaining experience. Scikit-learn and PredictionIO are great resources to start with. You could come up with a personal project or Google some project ideas that you can work on.
- Consider applying for internships. Internships give you real-life industry experience and help you acquire business-specific skills that will take your career to the next level.
- Competitions are a great way of testing your knowledge against others in the field. Kaggle usually posts some ML challenges that you can take part in to gain experience.
- Read books like Deep Learning by Ian Goodfellow and Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurelien Geron. These will add to your theoretical knowledge as not everything is learned in the classroom.