Getting started with Azure Container Apps is relatively simple, using the Azure Portal and working with ARM templates or programmatically via the Azure CLI. In the Azure Portal, start by setting up your app environment and associated monitoring and storage in an Azure resource group. The app environment is the isolation boundary for your services, automatically setting up a local network for deployed containers. Next create a Log Analytics workspace for your environment. Containers are assigned CPU cores and memory, starting with 0.25 cores and 0.5GB of memory per container, up to 2 cores and 4GB of memory. Fractional cores are a result of using shared tenants, where core-based compute is shared between users. This allows Microsoft to run very high-density Azure Container Apps environments, allowing efficient use of Azure resources for small event-driven containers. Containers are loaded from the Azure Container Registry or any other public registry, including Docker Hub.
Taking the work outside the company network reveals a lot of vulnerabilities of the systems. The in-house network and technologies are the best to work on any solution development as they have better security, and every team member would work on keeping it safe. There are no trust issues with your own teams, and it is much simpler to control, track, and communicate right with the members. Here comes the difficult part: Having an in-house team is quite expensive. Let’s consider that the base salary of a developer in India is Rs 6 lakhs per annum. Multiply this for four members, and it comes to Rs 24 lakhs. Of course, this is the least that one has to spend. As the designation increases, so do the salaries. Tech managers are not just expensive but also hard to find. Also, there are added costs of systems, supporting software & tools and training expenses. Since developers need to constantly upskill themselves to stay relevant, that means they will need support to visit conferences and even buy online training courses.
In cloud computing, cost isn’t everything, but it’s pretty darn important. That’s why prospective customers really must look at cost options up front for all key services including compute, storage and networking. Outbound networking charges, in particular, have been a sore point for many, many early cloud customers. These “data egress” charges accrue when data is shipped out of a given cloud to the Internet and beyond. Virtually no cloud player charges for data streaming into its cloud from customers, but one first-generation provider notoriously starts the meter running after one GB of data ships out per month to the internet. Those dollars add up incredibly fast, leaving many customers shell shocked because they probably didn’t realize — or could not predict — how much data they might transfer at some point in the future. OCI, on the other hand, starts charging only after 10 TB of data ships out. This means our customers can transfer 10,000 times as much data with OCI as they could with the other provider, without paying a cent.
The lack of focus on employees is also highlighted in responses related to flexible work, NTT Data said: just 21% of executives rated flexible-working options as a top contributor to employee satisfaction – the lowest of any response. This flies in the face of the numerous reports that suggest that flexible-working options are not only important to employees, but something they would consider leaving their jobs over. According to global management consultancy McKinsey, some 15 million Americans have quit their jobs since April 2021. This trend is predicted to carry over into 2022. Research from analytics platform Qualtrics this month found that 65% of workers plan to remain with their employer next year, compared to 70% in 2021. The research was based on nearly 14,000 full-time employees across 27 countries. Workers in the tech industry appear even more likely to seek new opportunities in the coming months. In an October survey of 1,200 US tech and IT employees by TalentLMS and Workable, 72% said they intended to leave their job within the next 12 months.
NLP in quantum computing is a complex undertaking that moves from the sequential nature of the spoken word to something less one-dimensional. “The point is that humans evolved language after they evolved a mouth-hole for breathing and eating,” Coecke said. “This physical restriction forces us to speak one word at a time in sequence. This is how we write, too. However, the concepts that we express, the stories we tell, the information we convey to each other, form a dependency network whose connectivity is higher than one-dimensional. Even syntax trees … that you learn in school, that encode dependency information inside a sentence, are two-dimensional structures. Going further, connecting sentences together forms a large network of dependencies between meanings. Telling a story means doing a walk over this network, and this time-ordering gives rise to what I call a ‘language circuit.’” Quantum computers are better suited than classical systems for running NLP workloads, he said.
The LotL Classifier uses a supervised machine learning approach to extract features from a dataset of command lines and then creates decision trees that match those features to the human-determined conclusions. The dataset combines "bad" samples from open source data, such as industry threat intel reports, and the "good" samples come from Hubble, an open source security compliance framework, as well as Adobe's own endpoint detection and response tools. The feature extraction process generates tags focused on binaries, keywords, command patterns, directory paths, network information, and the similarity of the command to known patterns of attack. Examples of suspicious tags might include a system-command execution path, a Python command, or instructions that attempt to spawn a terminal shell. "The feature extraction process is inspired by human experts and analysts: When analyzing a command line, people/humans rely on certain cues, such as what binaries are being used and what paths are accessed," Adobe stated in its blog post.
Most data science projects deploy machine learning models as an on-demand prediction service or in batch prediction mode. Some modern applications deploy embedded models in edge and mobile devices. Each model has its own merits. For example, in the batch scenario, optimizations are done to minimize model compute cost. There are fewer dependencies on external data sources and cloud services. The local processing power is sometimes sufficient for computing algorithmically complex models. It is also easy to debug an offline model when failures occur or tune hyperparameters since it runs on powerful servers. On the other hand, web services can provide cheaper and near real-time predictions. Availability of CPU power is less of an issue if the model runs on a cluster or cloud service. The model can be easily made available to other applications through API calls and so on. One of the main benefits of embedded machine learning is that we can customize it to the requirements of a specific device.
AI is no stranger to workplaces today. Fifty-three per cent of global leaders have integrated or are integrating AI into their workforce to enhance their business insights. While IDC has predicted that by 2022, 75% of enterprises will have embedded intelligent automation into technology and process development, the key aspect to consider is how and through which job roles organisations are going about incorporating AI in their workforce. Recent trends point towards broadening the scope of AI-driven roles in various parts of the workforce. This entails data science job roles spanning across the horizontal along with the vertical pillars of an organisation. With the increased integration of AI and Data Science teams in organisations, it is important for organisations and aspiring data science employees to understand the breadth of job roles. Many leaders are under the fallacy that AI and analytics can do with just data scientists, but the field is not limited to them. In fact, it is not even limited to engineers or individuals with a data science background.
Deep learning is a field of research that has skyrocketed in the past few years with the increase in computational power and advances in the architecture of models. Two kinds of networks you’ll often hear when reading about deep learning are fully connected neural nets (FCNN), and convolutional neural nets (CNNs). These two are the basis of deep learning architectures, and almost all other deep learning neural networks stem from these. In this article I’ll first explain how fully connected layers work, then convolutional layers, finally I’ll go through an example of a CNN). ... Neural networks are a set of dependent non-linear functions. Each individual function consists of a neuron (or a perceptron). In fully connected layers, the neuron applies a linear transformation to the input vector through a weights matrix. A non-linear transformation is then applied to the product through a non-linear activation function f.
Quote for the day:
"Go after your dream, no matter how unattainable others think it is." -- Linda Mastandrea