Google makes it easy to get started with Google Cloud but at the expense of some of the controls that large Enterprises need to have when they’re running workloads on any public Cloud now Google do this so that developers can very easily get started if they made it really hard to start using Google Cloud people would use one of the other clouds that was a little bit easier to use however when you start putting production workloads on there that might have customers information in them you need to revisit that security and put some controls around it setting this up the right way is not hard Google even released the code to build all the infrastructure and put it on GitHub you can easily find it if you Google Fast fabric the first result will be GitHub result for Google Cloud’s Professional Services team where they’ve put that code that you can run and enforce all of their best practices for you now if you need help running this and it can be a little bit complex or if you want any advice on how to get started with it hit me up I’m always happy to talk about this kind of stuff thanks
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🤯 AI Just Got a HUGE Upgrade (And You Need to Know Why)
for all those AI nerds there’s been some pretty interesting announcements from Google number one anthro pics Claude 3 is now generally available on vertex AI Gemini Pro 1.5 and Gemini 1.5 flash are also generally available we’re over 700,000 models on hugging phe so you can use any of the models on hugging face with vertex AI for those not familiar hugging face is kind of like a repository like git lab but for AI models so people taking off the shelf models or creating their own um modifying them and then uploading them to hug phase the next thing that’s super interesting is context cing so you can use context cing with Gemini Pro 1.5 and Gemini 1.5 flash models and this lets you past some of the tokens that you have uploaded so if you have uploaded um video and you want to ask multiple questions about it you don’t need to upload that video each time which is obviously going to be charged you can upload it once and ask multiple questions same thing if you have chat Bots with very long instructions um or you’ve got a large amount of documents and you’re asking different queries around document um the final use case I think was interesting is if you have a code repository and you’re looking to fix a lot of bugs upload it once C that context and then can do a lot of careers against it reducing both the cost and the latency to get those insights um if you need help with any of this feel free to reach out always happy to have a CH thank you
💸 Is Your Google Cloud Bill Out of Control? 💸
so you started using cloud and your costs keep growing and growing every month it seems to be more and more money than you’re spending on cloud and you’ve realised it’s time to take a look and cut those costs down to something that’s more sensible if you’re using Google Cloud they’ve got the fin ops Hub and the billing manager where you can go and see where these costs are broken down they’re often broken down by project so you can kind of see where some of the hot spots are and to reduce that the next thing you should start looking at is Devon test workloads and people pay me to come in and consult and say hey do you really need your development workloads running 24/7 when your developers are only working 9 to 5 it’s pretty logical get that turned off when it’s not in use even better get those running on spot instances these are substantially cheaper but when Google have low capacity they will take them away from you that will kill the developer workflow but it will ensure that your developers are writing code that can tolerate failures which is key to running anything on cloud the next thing you want to do is you want to enhance the visibility you’re getting into where you’re spending money now this is done with labels so every project or every resource that you have running should have a label on it with an owner and that owner should get an invoice not an invoice but a report at the end of each month showing how much money they’ve spent that will Empower your team to understand that they might be spending money that they don’t know about and have a look and see if they can reduce that by themselves this is really simple with Google creating labels putting them on everything and then exporting all the billing data into B crew so you can slice it dice it run reports and figure out where you need to focus your cost saving another few things that often get missed is Right sizing computer machines so being a computer engine you can individually change your memory and CPU to right size it to your workload now a lot of people do this as a one-time exercise and they kind of guess it they never come back and revisit it there’s tons of reports in Google where you can go through have a look at these things and then save yourself considerable money just by getting rid of unnecessary resources that your machines aren’t using if you donate anyone have a look at this feel free to reach out thank you
How do you know if AI is actually answering your question, and not just spitting out nonsense?
so I’m going to break down a few Concepts you might have been hearing when people are talking about AI the first one is retrieval augmented generation or rag it’s a bit of amouthful but it’s really simple if you ask an LL question so chat GPT or Google’s Gemini it’s going to respond based on what it’s being trained on which is the context of the entire internet but nothing specific to your business rag solves this problem by taking your business data uploading it into a database so that when you ask a question the question can retrieve data from the database based on your business and then formulate a response that’s grounded in that this reduces hallucinations or llms making up nonsense and make sure that it’s using data that is real from your business now the other concept we have is chunking if we’re taking documents and uploading them into the database we don’t want to upload entire documents cuz we’re not going to send entire documents to the llm very expensive so we chunk this you could chunk via paragraph but sometimes you need the paragraph surrounding that paragraph to get the full context or you can chunk via headings now different things are going to work for different businesses depending on how your data is structured by fining the way we chunk and store that in a database so that the LM can retrieve it and swapping out the llm model we can optimize for your business making sure that you get the best results possible for the best price possible whenever a new llm is released we can also test that very rapidly and see if that’s going to give you better results or a better price if you’re interested in learning more about this feel free to reach out happy to have a chat with anyone on these subjects thanks
Video Post: 💸 Stop Wasting Money! 💸 Easy Cost Cutting for Your Business!
Getting Started With Google Cloud Transcript getting started with Google Cloud can seem overwhelming at first as with any cloud there are a lot of services that you can use and each has configuration options that can get you into trouble when I worked at Google Cloud I helped some of the biggest brands in Australia set up their cloud environments and I’ll give you a few tips that I learnt from doing that the first thing you wanna do is enable some structure trading an organisation and then creating folders in the organisation to keep projects organised and allow to give groups of users permissions to do things to those projects for example putting all the development projects in a folder called development and giving developers access to those and then having all of the production projects in a production folder maybe without access for developers or for other groups of people once you have the folder set up you need to set up identity and access management so as I kind of touched on that’s creating a group putting developers in the group and then giving that group access to the folder that contains the projects that developers need to work on to do their jobs we may not wanna give them access to the production folder at all or maybe we only give them read only access this is a super simple example and we can nest folders and get much more complex with it and any environment that we’re talking about is gonna have more complexity than that this is a simple explanation now we wanna start talking about organisational policies we’ve got a group of developers that got access to their projects and development folder that we still don’t wanna do anything silly like putting a cloud storage bucket on the internet so that anyone can see what our files are even if those are development mocked data having a data breach is not gonna be good in the headlines there’s a ton of all policies and each one of them needs to be configured and this one example appear for the cloud storage bucket we may need an exception for the public facing internet to be on the internet once we have all this set up we kinda wanna make sure that we’re managing with code if a developer does request that a cloud storage bucket be put on the internet we wanna see who requested that and why and track those changes the logical step here is using infrastructure as code we use Terraform the same as the best practice at Google Cloud that Google had professional services used when I was there and we can do the same for your business in 5 days excluding any complex networking some people are spending much longer on this and it’s really not that complex if this sounds too complex do reach out we’ve done this when working at Google so we know the best practices and we know how to set you up securely so that your business can scale on Google Cloud thank you
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 factorization 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 organization to enabling your organization to make AI enabled decisions without spending an absolute Fortune if you want help with any of this Reach Out aviat Consulting can help you thanks – Generated with https://kome.ai
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 thanks