Artificial intelligence is gradually set to become main stream with several companies integrating it into their future plans and goals. A major part of software engineering requires staying updated with the ever evolving frameworks paradigms and standards. Software engineers need to constantly strive to learn so as to engage the best tools, methodologies and practices for the project in hand. As Artificial Intelligence finds its foothold in increasing number of applications on a regular basis, it is naturally becoming a field that a lot of engineers want to pick up.
Here is a list of the top 5 skills one must acquire before transitioning to applied Artificial intelligence
- Statistics: If one wants to understand machine learning, a good amount of knowledge of fundamentals of statistics is important. This includes understanding the following important points:
- All the different methods of measuring model success that includes recall, precision, position under the ROC curve, etc. In what way does the choice of loss function and evaluation metric biases the outcomes of the model need to be understood.
- Understanding underfitting, overfitting and the variance tradeoff
- The confidence that you can attribute to your model’s results.
- Machine Learning Theory: When you work on neural networks, you must be aware of what really goes on. One must get training on what tasks are not doable and what can be done. Machine learning can be understood best with graphics and examples even before going deeper into the theory. There are several concepts to understand – they start from how various loss functions work to how to work with computational graphs. One needs in depth understanding for building a functional model and be able to communicate it efficiently to your organizational team.
- Data Wrangling: 90% of the work done by data scientists is data munging. This is crucial for applied AI as the success of the model is proportionate to the quality of your data. Data comes in various aspects –
- a) Data acquisition: Picking good data sources, gauging the quality, inferring and acquiring labels, data taxonomy.
- b) Data pre-processing: Feature engineering, missing data imputation, data augmentation and data normalization.
- c) Data post-processing: Outputs of models made usable, handling outliers and special cases, removing unwanted data
The best way you can learn this is pick real time data and work with all three processes.
- Debugging: Debugging machine learning algorithms need to be done when they fail to give intelligible results. In order to test out different configurations, one must be able to find the best architecture and hyperparameters that make for a strong theoretical fundamental base. This also helps with quality infrastructure work. The methods of debugging are always evolving therefore one needs to be trained in the latest ones. If one has good development skills, then training in this process becomes accelerated.
- Software Engineering: A good amount of machine learning can allow one to leverage software engineering skills. But this is done with a twist –
- Testing all aspects of the pipeline – augmentation, data pre processing, model inference time etc.
- Writing code in a modular, reusable way so as to accelerate speed of experiment.
- Backing up models at various points
- Set up of distributed infrastructure in order to conduct hyperparameter search, run training or be able to inference better.
These skills need intensive hands on training by industry experts who can expose you to various domains of real time data to work with, training you to build effective machine learning and training to come up with viable AI solutions. IIHT’s exclusive AI course prepares students with industry oriented latest material along with hands on experience.