In this article, you will learn Machine Learning (ML) model deployment using Django. We will also discuss the ML Problem Statement which is HR Analytics. I have taken this problem from Analytics Vidhya. A special thank you to them for providing such amazing problem statements. Now before we start, take a look at this website-HR Analytics.

The groundbreaking applications of Artificial intelligence are attracting tech multinationals like Apple, Microsoft, Amazon and Facebook to work on their future projects with more AI focused strategies. The AI effect is influencing the product road map of all such companies having the renowned AI-based applications that are launched at regular intervals in a year to automate their business operations with more promising results. Computer Vision is an important development under AI that has been extensively explored and applied into various industries from outdated to innovative self-driving cars moving on roads without human intervention. Such AI-backed innovative technologies work on such principles that encompass a huge amount of training data for computer vision. All these steps have their own challenges in terms of technical know-how and operational activities, so here we will discuss and help you how to deal with the labeling of training data and other related aspects required to complete this process. Before we start labeling of training data, you need aware where the technology of Computer Vision is effectively used to produce an AI-backed system or machine that can perform without too much human instructions and do their job independently as per the changing situations.

Population-based optimization algorithms, like evolutionary algorithms and swarm intelligence, often describe their dynamics in terms of the interplay between selective pressures and convergence. For example, strong selective pressures result in faster convergence and likely premature convergence. Weaker selective pressures may result in a slower convergence (greater computational cost) although perhaps locate a better or even global optima. An operator with a high selective pressure decreases diversity in the population more rapidly than operators with a low selective pressure, which may lead to premature convergence to suboptimal solutions. A high selective pressure limits the exploration abilities of the population.

Automation is no longer a term confined to the dictionary of technical people only. Common folks are now very much aware of terms like Robotic Process Automation, Automation, Artificial Intelligence, and Machine Learning. To add to this list of leading technologies is Intelligent Process Automation. This article will give you a glimpse into what these technologies are, especially Intelligent Process Automation and Robotic Process Automation (RPA). So, let’s begin!

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We had just entered April 2021 and the daily lives of everyone in India were recovering back to normalcy. I had been working from home in Mumbai for more than a year and was quite happy to have booked a flight back to Bangalore just 10 days from then. Fast forward a week, and the second wave had begun in India with some dangerous signs. A lockdown was imposed immediately and I had to cancel my plans of returning to office. Everyday I would hope for the daily case count to start reducing, and the result would be very opposite.

Microsoft recently announced ZeRO-Infinity, an addition to their open-source DeepSpeed AI training library that optimizes memory use for training very large deep-learning models. Using ZeRO-Infinity, Microsoft trained a model with 32 trillion parameters on a cluster of 32 GPUs, and demonstrated fine-tuning of a 1 trillion parameter model on a single GPU. The DeepSpeed team described the new features in a recent blog post. ZeRO-Infinity is the latest iteration of the Zero Redundancy Optimizer (ZeRO) family of memory optimization techniques. ZeRO-Infinity introduces several new strategies for addressing memory and bandwidth constraints when training large deep-learning models, including: a new offload engine for exploiting CPU and Non-Volatile Memory express (NVMe) memory, memory-centric tiling to handle large operators without model-parallelism, bandwidth-centric partitioning for reducing bandwidth costs, and an overlap-centric design for scheduling data communication.

Ever since my first year of undergraduate studies, I was part of a robotics club, and during that time, Deep Learning was a fairly new buzzword in our university. Everyone was crazy about computer vision in my club because, after a workshop on image processing, we all thought that the world had shown its true potential to us! Now driven by the herd, I also learned and explored the same resources as everyone around me, and they gave me a great foundation. But my very first interview for a computer vision startup put me in my place. I had not understood how vast this field really is, so to help you guys avoid that embarrassment, I am putting together resources to make your journey easier. First of all, take some time to understand whether you really enjoy this field or not.

It's been roughly five years since RPA was anointed to be the next best thing to a slice of IT bread. RPA was, to some extent, an IT dream -- non-invasive as it sat unobtrusively outside our enterprise mission control systems ERPs, HRMS and mainframes, quietly automating our legacy processes, asking for nothing more than access to our systems of record. Expecting customers to automate all their processes within their company, early RPA vendors offered a full suite of bells and whistles. Now in the wake of the pandemic, clearly the market has expressed a bite-sized appetite for change instead of the whole buffet. This is where RPA as a service (RPAaaS) comes to the forefront.

There's been an ongoing trend of people wanting increasingly capable and smaller tech devices. Those desires have spurred progress in a segment of artificial intelligence (AI) called TinyML. Here's a look at how it could enhance future possibilities. It's already widely known that processing data directly on a device speeds things up compared to sending the information to the cloud. TinyML centers on optimizing machine learning models so microcontrollers on endpoint devices can run them.