While machine learning is a comprehensive topic, here are five trends that help us manage, optimize and extract the best results from our machine learning projects.
Trend #1: Auto Machine Learning (Auto ML)
An important aspect of data processing for Machine Learning is data labeling. In most cases, this is outsourced. But the risk lies in the human error of data labeling. Imagine you set out to repair a broken refrigerator and the instructions you have in hand is the recipe to cook lasagna. Auto ML automates manual data labeling, and therefore eliminates human error. While human intervention cannot be ignored to understand and label data, many enterprises choose the best of both worlds—human-in-the-loop data labeling and automation.
Which includes human and machine intelligence to create ML models. This helps streamline the data process and provides for a cost-effective, faster solution.
Trend #2: General Adversarial Networks
General Adversarial Networks comprise two neural networks that generate synthetic instances of data such as images, video, and voice samples. Now out of these, not all can pass for real data. Hence they are checked by discriminative networks to toss out unwanted generated content. So, how can these aid machine learning? General Adversarial Networks or GANs are discriminative models and hence cannot describe the categories.
Its identification and discriminative features are now used in AR to generate data in incomplete environment maps, image to data, or image to image translations, in the healthcare industry for detection of tumors, drug discovery, and so on.
Trend #3: Full Stack Deep Learning
Here’s an occasion to understand the importance of full-stack deep learning. A team of highly qualified deep learning engineers that you have outsourced created a deep learning model for your organization. But the end result that you see are a few files that are not connected to the outer world where your users live. So, how do we bridge this gap from models to deploying AI systems in the real world? If you haven't taken a guess yet, it’s Full Stack Deep learning.
The deployment step of these models-to-applications includes engineers having to wrap this model into infrastructure such as a mobile application, backend on a cloud, and so on.
A large demand for “full-stack deep learning” results in the creation of frameworks and libraries that can help other engineers automate tasks for their benefit or educational courses that can help them learn and adapt to newer tasks.
Trend #4: Machine Learning Operationalization Management (MLOps)
The amount of data that you will be handling will increase over time. This requires greater degrees of automation, sponsored by MLOps. It provides a new formula that combines ML systems development and deployment into a single consistent method.
MLOps provide many advantages, especially by reducing variability and ensuring consistency and reliability. It provides a mode of communication between data scientists and the operations or production team.
If this were a business pitch, MLOps would be the contestant assuring efficient collaboration in your teams, eliminating waste, automating as much as possible, and providing richer, consistent insights with machine learning. As an efficient tool, MLOps can be a great solution for enterprises at scale.
Trend #5: Tiny ML
Imagine you have a smart device for answering your queries on the regular, but the next time you say "Hey Siri" it does not respond back, only to generate a response 5 minutes later. It can take time for a web request to be processed by an ML algorithm and be sent back. Hence, the need for localized responses through smaller-scale ML programs arises.
With TinyML, smaller-scale ML programs are run through IoT edge devices. This helps achieve lower latency, power consumption, and required bandwidth. This is a major advantage when it comes to privacy, as the computations are localized with Tiny ML.