SMX Services & Consulting, Inc.

𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 ☁️ Choosing the right cloud service can be tricky. Here's a quick comparison to help you out! AWS (Amazon Web Services) 🌐 Strengths: Wide range of services, global reach, strong security. Use Case: Best for large enterprises and diverse workloads. Azure (Microsoft) 🔵 Strengths: Seamless integration with Microsoft products, great for hybrid solutions. Use Case: Ideal for businesses using Microsoft software. Google Cloud Platform (GCP) 🔍 Strengths: Excellent data analytics and machine learning capabilities. Use Case: Perfect for AI and data-driven projects. IBM Cloud 💻 Strengths: Strong in AI, blockchain, and enterprise solutions. Use Case: Suitable for innovative and high-tech solutions. Oracle Cloud 🏢 Strengths: Robust database services, good for enterprise applications. Use Case: Great for businesses relying on Oracle software. Alibaba Cloud 🌏 Strengths: Strong in Asia, great for e-commerce and big data. Use Case: Best for businesses targeting Asian markets. ✳️ Do Like and Comment to share your thoughts 🔁 Repost to share with your networks Follow Bharat M for more helpful AI content.

Over the past years few technologies have transformed #finance as much as the #cloud has done. Let’s take a look. 📚𝐓𝐡𝐢𝐬 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐥𝐚𝐬𝐭 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐭𝐨 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐏𝐃𝐅 𝐛𝐫𝐨𝐜𝐡𝐮𝐫𝐬: https://bit.ly/46nQFc9 Today #financialservices have become synonymous with apps, running on the cloud. But what exactly is the cloud? The cloud is nothing else but #software and services accessible on the internet through a network of remote servers. It is so ubiquitous in everything we do that one could say that the internet is running on the cloud. This makes a difference for one key reason: in the past computing power used to be synonymous with servers running (literally) locally. Banks would have entire rooms filled with computers and adding services meant adding more hardware. On the other hand, cloud computing is on-demand and based on a pay-as-you-go pricing, meaning that adding extra services now depends on software. And this is what we call a paradigm shift. One that has enabled the ascent of #fintech by essentially decoupling infrastructure from the customer experience. From a market competition perspective this means a fundamental change of the rules in just a few years: the possession of hardware quickly became from being the single main source of (oligopolistic) power for incumbents to their main drawback. The cloud would enable new agile players, called FinTechs, to go on and launch services competing on a different universe: the customer experience. Better services, directly addressing the pain points of niche segments without the costs, trouble and complexity of having to care for huge, antiquated, clunky IT systems. Suddenly financial services transformed from a hardware business to a software one. Amazon was the first company to understand the tremendous potential of the cloud, launching Amazon Web Services (AWS) in 2006. Fast forward to today and 74% of Amazon's operating profit comes from AWS with Amazon leading a $200 billion market (33% market share). In other words: Amazon, the world's most successful e-retailer generates from hashtag #ecommerce only a quarter of their revenues, with the rest coming from the cloud! For FS players of all kinds adjusting to the cloud is a one-way journey. Look behind the multi-million transformation roadmaps of big banks and it is all about transitioning to the cloud. According to a Google Cloud Study, 83% of surveyed FS leaders in FS across the globe, said that cloud is part of their primary strategy. Bank of England has estimated that the cloud could reduce infrastructure costs by up to 50%, whereas McKinsey found that Fortune 500 financial institutions alone could generate as much as $60 -$80 billion in run-rate EBITDA in 2030 through cloud use cases.

AI Explained ! Here’s What’s Included :- 1. Artificial Intelligence (AI): Creating machines capable of intelligent behavior, including reasoning, learning, and perception. 2. Machine Learning (ML): Algorithms that allow computers to learn from data and make predictions. - Supervised Learning: Trains models on labeled data for tasks like classification. - Unsupervised Learning: Models identify patterns and relationships in unlabeled data, like clustering. - Reinforcement Learning: Agents learn by performing actions and receiving rewards or penalties. 3. Deep Learning: Utilizes multi-layered neural networks to model complex patterns in large datasets. 4. Deep Neural Networks (DNNs): Multi-layered networks essential for deep learning, modeling intricate patterns. 5. Neural Networks: Brain-inspired models used for pattern recognition and predictions in deep learning. 6. Generative AI: Creates new data similar to training data, using GANs and VAEs. 7. Transformer-Based Language Models: Specialized architectures for natural language processing, like ChatGPT. 8. Convolutional Neural Networks (CNNs): Recognizes images by detecting patterns, crucial for visual recognition tasks. [Explore more in the post]

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