Interview with a Machine Learning Scientist: An Overview of the Field

Everybody speaks about ML nowadays. People think it's something amazing. In fact, the majority of work is spent on clearing, labelling, and structuring the data. In this post, I wish not to speak about the advantages of AI but name the pitfalls of ML implementation.

In the next section, I intend to share with you some tips for ML science beginners. Finally, there will be represented machine learning industry trends for 2021.

Interview with a Machine Learning Scientist An Overview of the Field

The success of machine learning

We can train the ML model to recognize dependencies in the large data sets. The volume of analyzed data can be so huge that human brains need much more time to investigate them. Quite often, the dependencies are not evident for the man. The machine collects multiple versions, calculates dependencies and similarities, and provides more precise deviation like clustering, categorization, or multi factoring analysis. It is not because the machine begins to think, it’s just the proper computation of dependencies between the data arrays. Just it's easy for the big calculator to recognize the patterns in large datasets.

The pitfalls of ML implementation

The most common misconception about ML is the assumption that we can build the correct machine model. Some guys want to spend hours creating a multi-faced model. Those who can program that kind of dream are rare, at least in the technology companies. At the same time, people talk about the impact of ML on human jobs. However, most ML algorithms are only matching rules, for example, applying a product's warranty, calculating taxes, categorizing the candidates, etc. As the algorithms just ally rank rules, and there is no relationship with the decision. That is the problem with many ML algorithms today that we can get false-positive and false-negative results.

Another pitfall lay in the cleanliness of the initial data. The manual correction of the database, dismissing some repetitions, cutting out anomalies lead to simplifying the calculation. But at the same time, it's changing the accuracy of the model training sets. As a result, work with fresh data brings us unsatisfied results.

Machine learning in 2021

Data's future in ML implementation: The scale of data mining is the essential factor that decides which ML technique to choose. But it is not sufficient to make the right choice. According to research done by IBM, only 4% of the data is appropriate for AI applications. The major limitation is the processing power: while most of the current GPUs' processing power exceeds the power of the human brain, it is not adequate for AI implementations. So, among the trends, I wish to mention the development of research-focused hardware, which can perform several deep learning tasks simultaneously.

ML for beginners

Machine learning can be implemented using C++, C#, Java, .NET, Python, R or Matlab. These languages are widely used to process a large amount of data, and ML model programming is one of them's standard tasks.

Alternatively, there are many frameworks, like TensorFlow, PyTorch, Keras, Spark ML, which have pre-trained models and can be utilized even with minimum programming knowledge. We call them no-code applications.

Additionally, there are many sources where you can gain access to the labelled datasets. Some of them are even totally free. A growing number of people prefer them, and it is becoming a common language in AI research. There is also a diverse community developing tools for each language. ML research is only becoming more popular. This is especially true for complex tasks. So, if you are interested in ML, you will be motivated to get the most significant source of information on the topic. 

Just you need to make the decision about what part of ML is the most interesting for you.
They are:
- programming languages for ML model constructions;
- no-code applications to work with pretraided data sets;
- mathematical statistics and analysis for the strong theoretical basis for ML modelling;
- work with deep learning nets, training them on the row data. 

You can find anything from beginner guides to advanced tutorials on youTube and in the social communities. I hope, I have managed to provide you with some information to help recognize the alternatives for getting started in this exciting field.