If you run a website or app on Google Cloud and you’re using their Application Load Balancer to distribute traffic but wish you had a way to: Add or modify HTTP headers: Insert new headers for specific customers. Or re-write client headers on the way to the back end. Implement custom security rules: Add your own logic to block malicious requests or filter sensitive data. Perform custom logging: Log user-defined headers or other custom data into Cloud Logging or other tools. Rewrite HTML on the fly: Dynamically adjust your website content based on user location, device, etc Script Injection: Rewrite HTML for integration with Analytics or reCAPTCHA Traditionally, you’d have to achieve this by setting up separate proxy servers or modifying your app. With Service Extensions, you can do all this directly within your Application Load Balancer. How Service Extensions work: They are mini apps: Service Extensions are written in WebAssembly (Wasm), these are super fast, and secure. They run at the edge: This means they on the load balancer, reducing any potential impact to latency. They’re fully managed: Google Cloud takes care of all the hard parts. Why would anyone use Service Extensions? Flexibility: Tailor your load balancer to your specific needs without complex workarounds. Performance: Improve response times by processing traffic at the edge. Security: Enhance your security posture with custom rules and logic. Efficiency: Reduce operational overhead by offloading tasks to the load balancer. How to get started: Check the docs from Google, start with Service Extensions Overview then Plugins Overview and How to create a plugin finally some Code Samples Also definitely worth checking out WASM if you have not already at https://webassembly.org/ Service Extensions sit in the Cloud Load Balancing processing path. Image to the left shows this.
Video Post: AI is not new
Transcript:So here’s the thing about AI it’s not in any way new most businesses have been using it behind the scenes for years to solve real world business problems things like fraud detection recommendation engine when you’re shopping and it’s suggesting similar products you might be interested in um optimizing Logistics all all different kinds of businesses have been optimized with AI it used to be called ml when I joined Google in 2018 there was a case study from 2016 of a Japanese cucumber farmer who built an AI model on tensorflow ran it on Raspberry pies to optimize his cucumber Farm his mom used to spend 8 hours a day sorting cucumbers and this guy was not a tech background but he developed a solution that would sort the Cucumbers had a motor and would and would sort them automatically saving his mom 8 hours every however often she sorted cucumbers I think when the llms came out there was a lot of realization in the consumer space that AI can achieve business goals but businesses aren’t really going to get any money from having a photo of a cat on the moon or something else like Chuck Norris fighting Dolphins um which is funny but not very profitable I think the good thing about the llm craze is that it’s brought attention from businesses to other ways that they can optimize to save money from Ai and if in 2016 a Japanese cucumber farmer can save 8 hours per day so an entire day of labor for one person with some AI there are definitely ways that most businesses can benefit from this it’s just a matter of uncovering them and then applying these AI solutions to them we’ve been working with a number of customers on things that will have incremental maybe double digit improvements but across the organization lots of these small projects will have a outsize impact across the organization and across the industry love to chat to people who have an issue they think would be solvable and see if we can help solve it thanks [Music]
Vendor Lock-in: We think its a myth.
The Myth Of Vendor Lock-in The cloud has revolutionized how businesses operate, but we often get stuck in weeks-long project delays trying to avoid vendor lock-in. This article highlights whether this is something you should be concerned about, or if your efforts are best focused elsewhere. I guess it is best to start on what vendor lock in actually is. Understanding Vendor Lock-in Vendor lock-in occurs when a customer becomes reliant on a specific vendor’s products or services, making it difficult or expensive to switch vendors. The business risk here is usually either: That one vendor could raise prices, and you would be stuck paying the higher price (VMware/Broadcom comes to mind) Vendor has multiple outages, or poor support (VMware/Broadcom comes to mind) The vendor goes bankrupt, or is acquired by a competitor, and your business along with it The Cloud Hyperscaler Landscape Cloud hyperscalers like AWS, Azure, and Google Cloud have significantly mitigated the risks of vendor lock-in. Here’s why: Open Standards, Open Source, and Interoperability: Hyperscalers increasingly embrace open standards and APIs, Containers, and Kubernetes is one example with every cloud having multiple ways to run standard docker containers, and these can be moved between clouds, with no changes. Each cloud does have proprietary services, especially when we look at databases, but the effort to migrate and modify these is typically way lower than it has been in the past. Using one of these databases to avoid vendor lock-in with AWS/GCP/Azure can also just mean you are locked into MongoDB, or an open source DB that is hard to move from. Bankruptcy: If any of these vendors does go bankrupt it will be a slow process, Google, Microsoft or Amazon are some of the wealthiest companies in the world, so I think we can discount this. Data Portability: Hyperscalers offer tools and services to simplify data migration and portability. While moving large datasets can still be complex, the process is becoming more manageable, hyperscalers will often fully or partially fund the migration from a competitor. In addition highly performant network connections between clouds are available or even physical devices to move the largest of datasets quickly. Market Competition: The intense competition among cloud hyperscalers drives down prices, there has only been a few times where some services increased in cost. This competition is not likely to reduce in the near term. Mitigating Vendor Lock-in Concerns While the risks of vendor lock-in are lower with cloud hyperscalers, if this is a concern there are a few steps to mitigate the effort if you ever do need to migrate: Design for Portability: Architect applications and data structures with portability in mind from the outset Avoid Proprietary Services: Minimize reliance on vendor-specific databases that lack equivalents on other platforms Conclusion The cloud hyperscaler era has resulted in strong competition which has significantly diminished the concerns of vendor lock-in. Open standards, data portability, and market competition have allowed businesses to focus less on lock-in and more on transforming their business. While some level of lock-in will always exist, it is about choosing where you are locked in, if you go all open source, and build your own servers you will be locked in to using this stack. We believe the focus should shift from fearing vendor lock-in to strategically leveraging the cloud’s capabilities to drive innovation and business growth.
Using AI for Document Processing
Artificial intelligence (AI) has captured the world’s imagination with its impressive ability to generate human-like text and engage in conversations, often blurring the lines between human and machine. While these “cool” applications have gained widespread attention, their practical value beyond chatbots has remained somewhat elusive. However, one area where AI is quietly making waves is in the realm of document processing. AI agents, equipped with advanced natural language processing (NLP) capabilities, can read and understand thousands of words in mere seconds. This opens up a world of possibilities for streamlining and automating tasks that previously consumed countless hours of human labor. The potential to reduce the time spent on document processing is enormous. Consider the following fields: Legal: Lengthy contracts summarised, legal precedents found, and arguments summarised. Healthcare: Alayze records, review literature and research to support diagnosis, or simplify text for patient understanding. Finance: Analyse financial statements, reports, and filings to identify risks and inform investment decisions. Beyond these obvious industry specific use cases any organisation that is dealing with documents can benefit from some AI help to improve efficiency and reduce costs. Enter Google Cloud Google Cloud Document AI uses advanced character recognition to extract data from your documents, creating highly accurate document processors to extract, classify and split documents. Googles highly scalable infrastructure can ingest your companies documents and analyze them instantly, this can be used for: Better understanding your customers: Information from clients SMS, Emails, and documents are often siloed, understanding all this data can be used to help gain better understanding of your customers and their behaviors. Reduce Fraud: Most fraudulent documents contain subtle issues that are often not noticeable to the human eye, but AI can detect these things (much like we can detect issues with AI generated images easily) reducing revenue lost to fraudulent documents. Report Writing: Hand writing documents has always been time consuming, and we typically rely on templates, AI takes this to the next level, writing entire reports based on data you have on your clients in seconds. While the “cool” factor of AI chatbots may have captured our initial attention, the true value of AI lies in its ability to transform industries and improve our lives. As AI agents continue to evolve and mature, their impact on document processing and other fields will only grow, ushering in a new era of efficiency and productivity.
Google Cloud Organization Policies: A Comprehensive Guide
In the dynamic landscape of cloud computing, maintaining a robust security posture is paramount. Google Cloud Platform (GCP) offers a powerful tool in its arsenal: Organization Policies. What Are Google Cloud Organization Policies? Organization Policies are a set of hierarchical constraints that you can apply across your entire GCP organization, folders, or projects. They enable you to: Why Organization Policies Matter for Your Security: Org policies let your engineers and developers deploy new services but maintain a compliant and secure environment by ensuring: Key Organization Policy Use Cases: The full list of Org Policies is avaiable on the Google Cloud site but a few examples are: Implementing Organization Policies: Step-by-Step Guide Best Practices for Organization Policies: In summary Google Cloud Organization Policies empower you to elevate your cloud security posture through proactive, centralized controls. If you are worried about your Google Cloud security Aviato offer Google Cloud Security Assesments and can help with the implementation of Org Policies.
Video Post: AI with BigQuery And SQL
Stop waiting to unlock the power of AI! 🤯 You already know SQL… and that’s ALL you need.  Transcript if you have a lot of data stored on Google Cloud for analytics it’s probably going to be stored in B query now everyone’s trying to do Ai and their training models using pre-existing models spending a lot of money on data scientists but I’ve got some great news if you’re using be query be query has be query ml built into it this lets you run AI against your B query data set by using SQL now SQL is the language used by all the people doing queries or database administrators now it’s very simple to use and you probably already have the skills so you don’t need to go and hire expensive data scientists and AI Engineers to gather insights from your data I’m going to break down some of the models that are built into B cre ml to see if these are going to solve business problems for you first one is linear regression this is predicting how much you’ll sell based on past data so if you’re planning stock Staffing trying to run promotions more accurately this is for you it’s built- in can be run with sequel the next one is logistic regression this is sorting things into categories so let’s say you want to sort customers into categories um to see whether they’re going to buy from you again um or if products are faulty putting them into categories saying these products are likely going to be faulty so you can see the use cases for business the next one is K means clustering this finds hidden groups within your customer base so you can Target marketing campaigns towards them the next Matrix factorisation suggesting which customers might be likely to buy an it this is kind of what you’ll see when you get predictive things on websites saying and you might also like to buy X I think we’re up to the fifth one PCA or principal component analysis this is simplifying complex data to find the most important pattern helping you spot Trends and make more informed decisions the last one I’ve got is time series so predicting future sales based on past data helping you predict demand and make sure you have enough stock based on things that have happened in the past so all of these models are already build into big query ml as I said and can be accessed using SQL queries which you likely already have the skills for in your organisation to enabling your organisation to make AI enabled decisions without spending an absolute Fortune if you want help with any of this Reach Out aviato Consulting can help you thanks
Video Post: Google Cloud Vertex AI & Hugging Face
Who Really Owns Your App?  Transcript did you know that most app developers in Australia don’t let you own the IP that they develop for you let me break that down for you you pay someone to write an app for you they let you use it licensed to use it commercialize it change it but you don’t actually own the intellectual property now what would happen if someone else had a similar idea to you went to the same app developer they could sell the code that you paid them to develop this other person doubling their money the next day all they need to do is change the colors and logo maybe make a few slight changes and you’re going to have a competitor now the app developers made a ton of cash on this cuz they’ve sold the same thing twice and only done the work once but you’ve got a competitor that’s going to compete with you and I don’t think that’s really fair and that’s not what we do at aviato at aviato if you pay us to develop your app you own the intellectual property we aren’t going to steal it and use it elsewhere and we’re not going to sell it to a competitor a few days after we finish your project so that then you have someone to compete with in the marketplace if you are looking to get an app developed this is really something that you should be checking with the app developer that you’re going to use reach out to us if ou do want to get an app developed Thanks
Video Post: Who Really Owns Your App?
Cut Costs, Boost Performance with Vertex AI Transcript have you been seeing all of the graphs and tables on LinkedIn comparing all of the current AI models it’s a little bit overwhelming obviously Chad gbt 4 is better than Chad gbt 3.5 but people start comparing Google Gemini and Gemma in versions Pro and Ultra and then they have 1.5 in Pro and Ultra then anthropic come on the scene with Claude 3 which also comes in three different models called Hau son Opus now I’m confused um I’m sure you’re confused and for most businesses they just want to get the best model that’s going to serve their purposes the best what we do is we deploy all of our AI on Google Cloud now Google cloud has access to the Google models Gemini The Meta models from Facebook llama it also has the anthropic models so clawed through in addition to this it has a ton of other different models that you can swap out fairly quickly so you can test and see which model is going to give you the best results for your use case and everyone’s use case is going to get better results from different models now if the models that I’ve spoken about and is about 70 or so on Google Cloud aren’t doing what you want you can go and use models from hugging face now you’ve probably never heard of hugging face hugging face is where people share models that they’ve built or train themselves or fine tuned so someone can take an open source model let’s say llama 3 from meta they can make it do something else for a specific news case and they can share it for anyone to use there’s currently 617,000 models on hugging face and we can very quickly integrate those with Google clouds of vertex AI to solve you all unique use cases if you go to hugging face and look at the models and just go to the categories you’ll be blown away there’s things in there from depth estimation super cool use case image classification video question answering audio classification tabular regression and one that I haven’t played with yet but super interesting robotic if you have a business problem and think you’d like to try and see if we can solve with AI reach out to meet aviato Consulting we happy to help you with this and using the power of Google switching out those models to get the best results for your business at the best price thanksÂ
Navigating the AI Maze
The AI landscape is exploding with a dizzying array of models, from the Large Language Models (LLMs) most of us have experimented with like Llama 2 or 3 from Meta, Claude 2 from Anthropic, and Bard (now Gemini) from Google, and the original ChatGPT. Each boasts unique capabilities, from generating different creative text formats to translating languages and answering your questions in an informative way. To make this even more confusing we have models that excel at robotics, tabular regression, image generation, or depth estimation. Choosing The Right Model For Your Business While this abundance offers exciting possibilities, it also presents a significant challenge: choosing the right model for your specific needs, and doing it within your budget. The pricing models for these vary greatly Gemin 1.0 advanced, to 1.5 Ultra is a 10x cost differential. For businesses, this creates an impossible puzzle: how do you select the optimal model without getting lost in the ever evolving AI arms race? And how do you do this within your budget? The answer lies in flexibility. Instead of locking into a single model, businesses need an adaptable infrastructure that allows them to test their business use case against one model, evaluate the performance and then try another, without rebuilding the solution. Additionally as new models are released, testing these to see if there is an uplift to the value of the model quickly, is going to give you the competitive advantage over others that need to rebuild their solution. This ability to quickly swap out models offers several key benefits: Architecture Architecting a solution that works for your business can be easily acheived on Google Cloud with Vertex AI, but this will exclude you from using ChatGPT or other models not avaiable on Huggingface.co LLMs on Google Cloud Vertex AI Google Cloud’s Vertex AI provides the perfect platform for achieving this flexibility. It allows businesses to seamlessly deploy, manage, and experiment with various LLMs through a unified interface. If you are not happy with the 50 or so models they have, you can deploy one from Huggingface.co which has over 600,000 models to choose from. The alternative solution for the non Google customers could be to write a common API, which would give you the flexibility to swap out models, or use Chat GPT which is one model that you cannot find on either Google or Huggingface.co Any LLM With a Common API Either option empowers you to leverage the strengths of different models, test each of them against your unique problems, and stay ahead of the curve in the rapidly evolving AI landscape. Aviato Consulting, a Google Cloud Partner, specializes in helping businesses navigate the complexities of AI and implement flexible LLM solutions on Vertex AI. Our expertise ensures you harness the full potential of AI, maximizing its value for your specific business needs.
Deep Dive into GCP Security Advanced Controls
A deeper understanding of GCP’s advanced security features and best practices