We have witnessed the increased growth of machine learning(ML) and AI-based tools, platforms and applications in 2018. These technologies have not only had their impact on the software and hardware industries but also have impacted the healthcare, legal, manufacturing, automobile, and agriculture. We will see more of these improvements in ML and AI in 2019 and much more in the years ahead. AI will be closer to users as global leaders such as Amazon, Apple, Facebook, Microsoft, and IBM are encouraging research and development in these fields. Here are a few trends to watch out for in 2019.
1. AI-enabled Chips
the training of AI models requires fast and advanced CPU. The models need additional hardware to do complex functions of mathematics, detection of objects and facial recognition. The chip manufacturers such as Intel, NVIDIA, AMD, ARM, and Qualcomm will distribute chips in 2019. These chips are specialized and can enhance the speed for the execution of AI-based applications. The next-gen applications related to healthcare and automobile will depend on these chips to deliver data to end-users.
2. The convergence of AI and IoT
AI meets IoT at the edge layer of computing in 2019. Almost all the models that are trained in the public cloud will be released at the edge. For AI, industrial IoT is the top case for practice. IoT will be the biggest drive for AI in the industry. AI chips based on FPGAs and ASICs will be included in all edge devices.
3. Automated machine learning
the trend that is going to change ML-based works for a certain level is Auto-ML. This technology allows business analysts and developers to improve machine learning models that address complex scenarios without undergoing the regular process of ML models training. The AutoML platforms allow business analysts to focus on business problems rather than losing track of the workflow and the process.
4. Automation of DevOps and AIOps
There is a lot of data being generated by modern applications and systems. This data is being captured for the index, search, and analysis. The data collected from the hardware, OS, server software can be approximated and correlated to find motives and patterns. The IT operations are transformed from being reactive and can become predictive when ML models are applied to these data sets.
This is just the beginning of how AI is going to impact our industries. AIOps will be mainstream and many public cloud vendors and enterprises are going to benefit from the combination of AI and DevOps.