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Great explanation of the process. Many thanks.

🎯 Key points for quick navigation: 00:00:05 *📄 Google’s Document AI transforms physical and emailed documents into structured, actionable data, revolutionizing how businesses handle paperwork.* 00:00:29 *⚡ The demo setup took just three hours, highlighting the speed and efficiency of using Document AI for automating document processing tasks.* 00:00:58 *🧾 Document AI is useful for automating invoice processing, improving billing accuracy, and speeding up financial operations.* 00:01:27 *⚖️ It enhances contract management by extracting critical terms and conditions, aiding compliance and risk management.* 00:01:57 *👥 Customer onboarding is another key use case, as Document AI speeds up the extraction of identity data from forms, improving efficiency and accuracy.* 00:02:26 *🗃️ The video uses an invoice dataset from Kaggle to demonstrate how to work with PDF invoices in Document AI.* 00:02:55 *☁️ Users need to upload their documents to a cloud storage bucket that is aligned with their document parser location for processing.* 00:03:51 *🏷️ Document AI allows manual and automatic labeling of documents to train the model, improving future document tagging.* 00:05:17 *🔄 Once enough documents are labeled, users can retrain the model to create more accurate versions for their specific document types.* 00:06:14 *🔍 The model’s performance can be tested with real documents, and users can refine the model over time by addressing any inconsistencies.* 00:06:44 *🛠️ Different model versions can be managed, tested, and deployed as improvements are made, allowing for ongoing optimization.* 00:07:42 *💾 Exported data is structured in JSON format, making it easy to integrate into business applications for streamlined workflows.* 00:08:11 *📊 Properly integrating Document AI into high-volume document processes helps businesses improve accuracy and make better data-driven decisions.* Made with HARPA AI

Would benefit from mention of the very substantial regulatory requirements around HR uses of AI. Just deploying AI without considerable planning and thought at the corporate risk level is brave indeed.

You’re welcome! 😊

Thank you sir

Thank you

How do deal with the rules around not uploading PII? As I understand it all PII must be redacted before sending docs up to document AI or any google cloud service.

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how we can only select relevant text from json

As a web application developer of 17 years, I've been replaced entirely. There's less than 1/10th the number of jobs available than there were 4 years ago where I live. I now work at Aldi and want to die.

A.I. will (and already has in certain sectors) replaced a team down to one person. It is mostly creative sectors being hit first, but with companies like Nvidia using it to make entirely new chips that were thought impossible (while downsizing) is indicitive that all companies will follow suit and be a 1-2 person team over a 5-15 sized team in the very near future. Bye to automechanics. Everyone's going to the dealership that can afford am AI all-in-one Car assessor and repair bot. Lot of blue collar/ trade school jobs are going to go away. Who needs a welder when an AI bot can to tig weld perfectly, every time.

thanks for the video man ;]

❤❤❤

This is really informative and everything is well explained, I don’t know why it doesn’t have more likes!

🎯 Key Takeaways for quick navigation: 00:00 📚 Kubernetes was designed to address resource allocation challenges, allowing multiple applications to share server resources efficiently. 01:00 🏭 Kubernetes originated from Google's need to manage resource allocation for various applications within their infrastructure. 03:00 🌐 Kubernetes evolved from Google's internal tool Borg, designed to orchestrate and manage applications across vast server estates. 05:00 💡 Kubernetes offers fine-grained control and is suitable for complex use cases, providing scalability, stability, and automation features. 08:00 💼 Choose Kubernetes based on your business challenges and requirements, as other options like serverless, event-driven setups, or traditional VMs may be more suitable. 13:00 🧠 Overcoming the complexity of Kubernetes involves starting small, focusing on scheduling requirements, and gradually exploring additional features based on your application's needs. 16:00 🔄 Kubernetes challenges include handling stateful data, managing caching, and addressing assets generated during runtime. Design considerations like monolithic vs. microservices are crucial. 21:00 🚀 Choosing between monolithic, microservices, or modular monolith architectures depends on the specific use case and business requirements. 21:45 🏗 Selecting the right architecture tool for the job is crucial. Avoid going too far into microservices if it's not necessary. 22:17 🔄 Testing in complex microservice architectures can be challenging, and managing release processes comes with overhead. Striking a balance is essential. 23:15 🧩 Implementing the 12-factor app methodology can prepare an application for Kubernetes and scalable architecture, emphasizing component separation and secret management. 24:16 🔄 Keep your code DRY (Don't Repeat Yourself) by separating configuration and environment details from code, enabling flexibility across different environments. 25:45 📈 Kubernetes offers features like rolling updates, blue-green deployments, and canary releases out of the box, improving application reliability and performance. 28:48 🚚 Containers provide isolation, ensuring applications don't conflict due to library versions, and offer portability across different environments and cloud providers. 30:48 🔁 Reusability of parent and child container images enhances efficiency and helps maintain consistent project environments. 32:21 📚 Recommended resources for getting started with Kubernetes include CNCF courses, Cloud Skills Boost, and various educational content on RUclips and Pluralsight. 34:18 🛠 Tools like Terraform, Helm, and Costcan help simplify Kubernetes deployment, configuration, and resource optimization. 36:18 🚀 The future of Kubernetes may involve more automation tools, machine learning for resource optimisation, and increased adoption of serverless solutions like Cloud Run.

🎯 Key Takeaways for quick navigation: 00:06 📈 The demand for cloud engineering and DevOps roles has evolved significantly, with a focus on innovation and leveraging cloud technologies such as machine learning and generative AI. 01:06 🌐 The cloud landscape is shifting from merely adopting cloud technologies to innovating within them, creating a surge in specialized roles and technical expertise requirements. 02:08 🚀 Businesses are increasingly investing in cloud technologies to stay competitive and innovative, leading to a high demand for cloud engineers. 02:38 💼 Agencies face challenges in cloud engineering resourcing, particularly in balancing costs and flexibility due to the rapid demand growth and the high cost of skilled engineers. 03:42 🔄 Cloud engineers are often required more intensely at certain project stages, creating challenges in resource allocation and utilization. 04:43 💰 The rising cost of cloud engineering resources makes profitability increasingly challenging for agencies and consultative businesses. 06:12 📚 There is a growing diversification and specialisation within cloud-related skill sets, leading to a skills gap in the industry. 07:42 🛠️ Different backgrounds (IT vs. software engineering) in cloud engineering bring varied strengths and gaps in expertise. 09:16 ⚙️ Implementing automation and standardisation in cloud projects is crucial for efficiency and managing multiple projects effectively. 11:16 🔍 Emphasising governance and best practices in cloud engineering through technologies like TerraForm is key to preventing mistakes and ensuring quality. 13:19 💸 While salary is an initial attractor for cloud engineers, opportunities for innovation, culture, and learning are vital for retention. 15:53 🤝 Team camaraderie and support are crucial in cloud engineering to promote learning and problem-solving. 17:28 🏠 Balancing remote and in-person work is important for cloud engineers, especially to accommodate deep-focus work and collaboration needs. 19:30 🧠 Creating an environment that minimises disruptions allows cloud engineers to maintain focus and efficiency. 21:05 📅 Implementing 'meeting-free' time blocks can significantly enhance productivity and focus for engineers. 23:10 🔧 Site Reliability Engineers (SREs) play a unique role, requiring both infrastructure and coding skills to address complex issues. 25:44 🚀 Encouraging innovation and ownership in cloud engineering teams is key to keeping them engaged and effective. 28:22 🌐 Outsourcing cloud services can be more cost-effective due to the high expense of maintaining in-house expertise. 31:30 🌍 Offshoring can be an alternative strategy for resource management in cloud services but comes with its own set of challenges. 34:06 🔑 Effective onboarding and understanding the specific gaps that need filling are crucial when working with third-party cloud vendors. 37:43 🤝 Finding a cloud partner with a compatible work culture and values is as important as their technical expertise. 41:24 📈 Continuous training and development are essential in cloud engineering due to the rapid pace of technological change. 44:07 🎯 Clear goals and measurements aligned with business objectives are crucial in guiding cloud engineering teams. 47:11 📚 Creating a culture of learning and knowledge sharing is key to staying abreast of evolving cloud technologies. 50:14 🛠️ Developing detailed playbooks and guides can aid in effective troubleshooting and skill development. 53:21 🏗️ Balancing innovation with stability and security in cloud environments is a critical challenge that needs careful management. 58:02 🌟 Flexible cloud support can provide crucial scalability and expertise, particularly in project-intensive environments like agencies.

🎯 Key Takeaways for quick navigation: 06:40 💡 The evolution of generative AI has significantly improved ease of access to information within businesses, combining chatbots and advanced search capabilities. 11:59 📄 Second use case: Making global documents searchable through AI, enhancing the ability to process large volumes of unstructured data like contracts and invoices. 16:26 🎨 Creative assistance for product and marketing is another use case where generative AI speeds up the process of creating marketing materials, enabling rapid prototyping and personalization. 21:09 🏷️ Addressing product catalogue management, generative AI can categorise products and enhance product descriptions, images, and specifications efficiently. 23:52 🔧 Discussing engineering and automation, highlighting how generative AI aids in detecting software performance issues and integrating with engineers' playbooks for efficient problem-solving. 34:18 📈 A future use case: Automating stock replenishment on shelves, which is still in development due to real-life challenges like varying camera angles and product packaging changes. 36:38 🚀 Final segment discussing getting started with AI, emphasising the importance of selecting the right challenges and use cases, and integrating AI into business processes for effective solutions.

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If there are particular aspects of *Managed Cloud Services* or *SRE* you would like us to dive into in detail please let us know here in the comments!

If there are particular aspects of *Managed Cloud Services* or *SRE* you would like us to dive into in detail please let us know here in the comments!

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