In today’s world, there aren’t many fields that are as exciting as Machine Learning. The field is moving at a rapid pace, not just in the field of research, but also in development, management, and operations. In this blog post, I will talk about 4 promising trends in the field of Machine Learning that will make a huge impact, and whose effects will felt well outside the field of Machine Learning. Make sure you too watch for these trends in Machine Learning and keep a track of their latest developments in the coming few months and years.
Machine Learning Operations (MLOps)
Building an end-to-end Machine Learning pipeline and using it to train a model is one thing. But managing, deploying, integrating, monitoring, and using it to drive desired business outcomes is a completely different ball game. Machine Learning Operations, or MLOps is the Machine Learning equivalent of DevOps. MLOps refers to a repeatable and standardized process applied to Machine Learning and Data Science models and pipelines. This process is used for performing end to end Machine Learning and consists of 4 different steps-
- Building – This is the first step in the process, and consists of gathering data, processing it, performing feature engineering to develop new features, training several models on the data, and selecting the best one to be used.
- Managing – Managing refers to managing the various Machine Learning and Data Science resources, such as managing and versioning the training data, model, and training process, but also testing the model, and tracking and governing the model.
- Deploy and Integrate – This refers to the step in which we take the model from the development phase and deploy and integrate it with other production code so that it can bring value to the business.
- Monitor – Last comes the monitoring of the deployed Machine Learning model. The model needs to be checked continuously if it is performing the way it should. It is based on monitoring that important decisions such as retraining and remodelling are made.
MLOps is going to be very important in the years to come as more and more organizations will move to Machine Learning for driving value for their respective businesses.
Self Supervised Learning (SSL)
If you have been following this blog or any of the Artificial Intelligence conferences or even a discussion group, you are probably aware of how hot this topic is among everyone. Supervised Learning makes use of labeled to train the model. However, Supervised Learning is very difficult to scale because of the scarcity of labeled data and the difficulty and cost associated with labelling new data. This is where Self Supervised Learning comes into the picture. Self Supervised Learning is a form of unsupervised learning that overcomes this issue by leveraging the un-labeled data which is present in abundance as compared to labeled data. I have written a blog explaining Self Supervised Learning in a bit more depth. You can check out the blog over here.
Explainable AI (XAI)
Most of the Deep Learning models are Black Box models. It is very difficult to know what a model has learned during training and why it arrived at a particular result during inference. Explainable AI or XAI aims to tackle this issue. Explainable AI consists of 3 very important features, namely Transparency, Interpretability, and Explainability. XAI is very important to know and understand the ‘subconscious’ biases that the model has learned from the data so that suitable actions can be taken to address them. Explainable AI is therefore very important to achieve a Fair AI. In some industries such as Banking and Finance, Explainable AI might also be required from a regulatory standpoint. However, the benefits of XAI will span the entire field of Machine Learning.
Automated Machine Learning (AutoML)
Automated Machine Learning or AutoML refers to automating the tasks associated with Machine Learning. To build a Machine Learning pipeline, there are several specialized skills that need to be applied, such as gathering data, analyzing, feature engineering and pre-processing, training, validation, testing, deploying. All this makes end-to-end Machine Learning very difficult to apply. And this is where AutoML comes in, it simplifies this entire process by automating it. AutoML is a very important step in democratizing Artificial Intelligence and Machine Learning so that people who are not experts in the field can also be a part of it.