 Music Good morning everyone and welcome to the workshop on building applications in the AI era under Google Cloud AI for Impact APAC hackathon powered by Hack2Skill. I'm Uzma and I'll be your host for today. So before we start the session, I would first like to give you an overview of the hackathon. AI for Impact Hackathon is a unique opportunity for people to collaborate, innovate, and create groundbreaking solutions using AI to address pressing social challenges across the APAC region. By utilizing Google's GenAI tools, including Gemma, Gemini, Vertex AI, and Gemini Quotasist, you have the chance to create innovative solutions that can drive meaningful change and improve lives. So whether you're a developer, a freelancer, or an IT practitioner based in the APAC region, we encourage you to join and build for a better tomorrow with AI. So this is the timeline for the hackathon. And we are currently in the registration and idea submission phase. And here's everything you need to know. It's pretty simple. The first step is to register yourself. And after that, you'll form a team. The team can be of up to four members. After that, you need to submit your idea. The deadline for registration and idea submission is november 17th which is two days away so please don't miss out by the end of november we'll also announce the initial shortlisted teams who will move on to the prototype phase and then we have the grand finale in the later half of december now let's talk about what's in it for you. Well apart from getting a chance to create an impact we have got some incredible rewards lined up and trust me they are worth the hustle. The first thing that you can expect is there's a cash pool price of twenty thousand US dollars. Second, the real win is the chance to be mentored by some of the best minds at Google Cloud. The top teams of this hackathon will get a chance to receive feedback and mentorship from the Google for Startups team. For all the top teams and valid idea submissions, we've got certificates of recognition as well. Now the winners of the hackathon will also have the chance to present their solutions at a Google event. And apart from all of this, you also are getting a chance to network with like-minded individuals and gain hands-on experience on Google Cloud's AI tools through several workshops. Now we have several themes for you to choose from and these are empowering healthcare workers, creating a greener world, forecasting national disasters tech for better education helping people with disabilities transforming public service solutions for community empowerment and if you have observed any other challenges besides the ones that i just mentioned you can select the open innovation and social good and submit your solution under that now moving on to today's session i am thrilled to have Richie Mishra, a customer engineer at Google with us today to start the workshop. We'll also have a Q&A session in the end. So make sure to type your questions in the comments during the presentation. So without further ado, over to you Richie. Thank you for being here. RICCHIE MISHRAH KHANNAHARANI- Thank you, Uzma. Good morning, everyone. So like Uzma mentioned i am richie i'm a customer engineer with google cloud uh which means that i work with clients uh to address their uh needs on google cloud i advise them on the solutions that uh on how they build these solutions on cloud right um i have been a software developer for about three years and post that I've been with Google for around one and a half years as a customer engineer now. In today's session, I think we will be diving deep into how you can build using Google AI, right? So in the last session, my colleague Aditi had walked you through the Vertex portfolio and how you can use agent builders. And today's session will be more around how you can streamline your everyday operations, how developers can be assisted through our AI. Right. Now, as we move ahead in the session, I will be switching between the slides and demos please let me know if i'm going slow or fast and if i need to control the pace i also have with me my colleague nikita jays wal who is also customer engineer with google cloud she'll be here to answer some of her queries during the session and i'll be taking up the rest of the questions post the session and i'll be taking up the rest of the questions uh post the session uh so i'll just start presenting my screen uh uzma maybe you can confirm once it's visible uh i need to add the slides, Reduzma. Yes, you can just share this. Okay. Let me just quickly share my screen. Let me know if it's visible. It's visible, right? Yes, it's visible. Okay. So today we'll be seeing how we can build applications in the AI era. But before we do that, here's a concise view of what all we'll be covering. The first will be understanding the role of Gemini, which is our multimod it can be used to generate and complete code right so these are the topics so before we dive dive deep into gemini for developers we'll do a quick recap of Vertex just so that you have an understanding of where Gemini for workspace or Gemini code assist sits in the entire Google portfolio. Right. Moving on. Now, Google has been investing significantly in AI. Right. And that is clearly visible from the portfolio that we have in every layer and every stack. We have something to offer uh right so this is our ai portfolio uh just to uh recap uh we have our infrastructure which has all the leading gpus in the market be it nvidia a100 h100s of the world uh or our own tpus using which our own uh models are trained right uh we you have that available through the uh vertex platform uh we also have a layer of model garden which means that there's a marketplace for different models out there while gemini is our most capable llm model with a 2 million multimodal context window uh in case there are some more other open or partner models like llama or claude mistral any of those that suit your use cases and you want to try them out then that would be available through our models marketplace which is the vertex ai model garden we also have a number of tools to support you to build your own models, be it experimentation or be it taking your MLOps workloads from experimentation to production. We have everything to support you, right? Be it with prompting, serving, tuning, or your pipeline monitoring, your feature stores, or notebooks to help you quickly experiment with python codes right on top of that we have our agent builder which is essentially our out of the box rag solution that was covered in detail in the previous session right how you can build search agents or chat agents or recommendation agents just by the click of a few buttons right um and on top of that we have gemini for google cloud which means that in every step of your google cloud journey gemini gemini will be there to assist you right you have any questions you just open up gemini you ask it those questions uh it will respond to you with summarized answers with all the sources cited as to from where it got all this information all grounded on google's public documentation right we have then we then have gemini for google workspace which means that gemini is in the very dna of everything that workspace has to offer right be it gmail slides or docs or sheets everywhere gemini is there to help assist you in every step of the way in the upcoming slides we will be taking a look at these two products primarily gemini for google cloud and gemini for google workspace right and on top of that we have out of the box like AI solutions which are tailored for very specific use cases as you can see contact center AI which is for contact center solutions right which can be used directly off the shelf we have direct AI search for retail media and healthcare as well which have like customized fine-tuned LLMs for very specific use cases in these industries. Right. Now that we have taken a look at what the Vertex AI portfolio, what the Gen AI portfolio is of Google Cloud, we'll take a deep dive into how this is present in Google Cloud and Code Assist and in workspace specifically uh right introduction to gemini uh is so gemini is an ai powered collaborator in this case uh it can assist with uh workspace collaboration be it writing mails be planning uh writing plan documents uh it can help you with security management, which means that you can just go through, I mean, the process of handling security events, cases, going through the logs. All of this has been simplified using Gemini. It's also present for data analytics, databases and developments and operations to make the lives of data analysts or developers easier right we'll have a look at how how it does that in the next few steps so Gemini for workspace has essentially three pillars emails documents and slides now in emails uh uh it's it's often um often while replying to mails uh we get to thinking right that okay uh what should be a formal tone of replying to this mail uh how should how casual should the tone be uh what is the context of the thread that has been going on for so long right like there are long email threads going on and maybe you're not aware of what has happened earlier and you want to summarize context of that and you want to draft a reply while summarizing all of it, right? And you want to polish your tone style. You want to check for grammatical errors rephrase it in a very nice way, right? So all of these we have often thought of this right that okay if there was a tool to help us with all of this it would make our life so much more easier right writing emails and all and doing all these operational tasks they take up so much of our time but if there were a tool to help us with this it would be so great right so that's where gemini comes in it will help you summarize email threads It'll help you write emails up from the scratch. It will help you polish your emails. It'll help you elaborate your emails, shorten your emails, change the tone of your mails, make it does that in the next slides. And similarly in documents, it can help you like create, it can be basically a creative aid for you in creating outlines or some draft paragraphs or summarize some meeting or bulletize some meeting points, points from a meeting right it can also refer to your other documents in that are there in your workspace in your drive and from those documents it can bring in information and put it all in front of you in one single place similarly it can also help us help you craft like a really good slide it can help you generate images for your slides which are relevant summarize the content and slides summarize or bulletize these content or create entire slides with images and text as well so this is an example of how Gemini helps with writing an email as you can see there's a small prompt saying that need meeting re events Wednesday am and we have refined the selected text using and elaborated it and we have a ready-made draft of an email right there in a matter of few seconds in a click of a few buttons right similarly writing documentation we have given a prompt that okay create a high-level planning document for an events application written in JavaScript using so and so for authorization and a fire based data firestore database it has given us a very detailed bulleted point wise documentation along with like placeholders right it gives you interesting chips and placeholders for you to fill in and create like very good documents in a matter of few seconds right similarly creating images for presentation if you see this particular image this has been created using Gemini in slides you can select from a number of art representation forms you can give your own prompts and uh fine tune it to get the sort of images that you want that are relevant to your slides right uh before we move to gemini for security operations i would just want to show a quick demo of uhini in Workspace while we have it open. Uzma, let me know if my entire screen is visible, please. I hope this is visible. Uzma, you there? Yeah, it's visible. We can see your screen. Yeah. Okay. Thank you. So coming to mails, this is a mail thread that I've sent to myself from my other account for demo purposes. So this is a mail that states that okay there has been an invitation to a ai hackathon event uh to which uh there's a reply that okay there are some replies and there's a there's a thread going on basically now let's say i want to reply to this mail right um i can use gemini to keep the conversation like, okay, just tell Gemini that inform them that you have registered for the event. That's it. I'll just write whatever I'm thinking and I'll quickly create and in a matter of few seconds, I should have a mail polished ready to send right yeah so I have this ready now if I want to refine this further I can elaborate this I can also formalize this more. Yeah, it was already formalized, so it did not change it much. But yeah, this is how it helps you draft replies. We also have a Gemini bar here, which will help you sort of summarize mails. So if I just click on summarize this mail, it should give me a very quick summary of whatever has gone on in the past threads. It's given me all points in a very detailed point wise manner so that I don't have to go through the entire thread right similarly if I were to ask what are the action items for me and tell me what I need to do but okay I need to register for the event and has to wait for the event times to be confirmed a confirmed via the via mail after registration right but it has given me the action items along with the sources that okay this is the mail and from here these are your action items right um so that's how uh gemini is integrated into the gmail aspect of it um similarly uh in docs like i said help if i ask it to help me write this is the gemini uh button if i ask it to uh bug tracker it's given me a sample an example bug trapper tracker with priority level assignment description right this can help us like create create planning documents real quick right just given me some uh dummy data so i can just insert this and i can replace this with my own data similarly here also you can ask gemini a summary a summary of the content right it has given me a summary i can ask it to refine this document as a list of key points by using an informal tone i will do that yeah um similarly I can also reference other documents, right? For example, I have a slide, let's say census household, this is some census data that I just picked up from the internet. And if I ask it to summarize the insights from this sheet in bullet points. It should give me a very detailed summary again of the comprehensive so this sheet was essentially had a census data on the number of districts, of districts, households within each sub district and things like that. And it's given me a very comprehensive summary of the, of what exactly the sheet encompasses. Right. So what this helps in is basically when you have a lot of interlinked documents and you want to summarize all of your uh uh uh you know documents in one place from from sheets from slides from uh mail everything and in one place it this will this feature will help you basically do this and you can ask gemini a number of other things but yeah this is a very high level uh view of what all things can be done right similarly in Gemini is also present in slash sheets right here also you can use Gemini to create formulas so let's say I have this table of students with scores in different subjects right I want to create a formula of let's say to find the mean score in the math in the subject maths right Gemini should be able to generate a formula and give it to me and this is a simple example but it can generate like much complex formulas we look up formulas and all of that so it's given me a formula that just average B2 to B20, which is from here to here under maths and I'll have my mean score, right? It can also help me generate insights like from the given sheet, given table, let's say I ask that give me some insights on the score patterns in English, right? And it should be able to give me some meaningful insights. yeah so it's given me like a statistic score that, okay, the average and median score is this, it ranges from 76 to 94. 75th percentile is at 80 and 87.5. And like, overall English scores show a decent distribution. So basically, you can use it for your data analytics purposes to have like, you don't have to be a data analyst uh to be able to make sense of the data that is there on the sheets right you can just ask gemini natural language and you can get the information that you want out of it um similarly for slides if you see uh gemini is present here as well uh and you can just ask it to give you again similar uh summary of the content that is there right give me and automatic generated a summary so this is a three slider uh sample product gtm uh for a headphone variable uh brand which is symbol and it's given a summary of this content that okay that this is the product this is the gtm this is the ideal customer um that for the product and this is something of the content uh if i were to create an image for this presentation uh i can just give in a beach setting. For, you know, photographic purposes or marketing, to create marketing assets and to create presentations real quick. You don't have to go to the internet, search for pictures, and then put it up here. You can just do it directly here and insert it in the slides it's given us some yeah it's given us some pretty good images as you can see right pretty good pictures for a presentation um so yeah so that was google for workspace and let me switch back to my screen and show you will walk you through the slides now I hope we're back on the same slides that we left off at. Uzma, can you confirm? Yes, Richie. That's correct. Similarly, Gemini is there for your security operations as well, which means that it gives you contextual recommendations even before an analyst goes into the details and digs deeper and finds all these events cases without drilling deep into them you Gemini summarizes these cases for you it creates queries for you to automatically run and it also gives you recommendations as to okay so this was the event that happened uh it could be happening due to so and so resources please check your iam permissions uh are they in place or not uh and these are the recommended actions along with links on best practices and tips and tricks right uh so it's there like it's not in one place it's there throughout google cloud along with integration from integration with Chronicle, which is Google Cloud security analytics platform. Right. So it gives a summarized view, a bird's eye view of everything that is happening without you having to dig deep into the cases. Right. So it just guides you step by step. Okay, this is what has happened. These are the recommended actions for you to take and this is the queries that this is the query that you can run to see what has gone wrong where it has gone wrong and things like that similarly Gemini is also there for data analytics the same features are there the same natural language search which means asking questions in natural language getting summarized replies getting quick start templates all of their all of this is there in the databases as well um similarly it has integrated uh with big query also which means that you big queries essentially uh google cloud's data warehousing solution where you can uh like store and process petabytes of data at blazing fast speed right um so there to write sql queries let's say you're not a sql developer so and you want to write some query you you are a business person you want to uh look at some have some uh look at some tables and get some insights out of it so uh you don't need to wait for a developer to come and assist you with writing a sql query for your task right you can just explain it in natural language and gem and i will be able to give it to you uh in in a matter of few seconds i'll just quickly show how that works now this is the gcp console for those of you have not seen this before, I'm in the BigQuery section where I have a dataset called Movie Lens, which has a few tables. It has few tables related to movies, their genre, and more meta tags related to a movie. So if I want to, let's say, find some movies which are in the adventure genre, right? So I'll just, let's say I do not know how to write a sql query so i'll ask gemini right okay uh help write me a query to find movies in the uh adventure genre adventure let's see I have just note that I have not given it any information on which database it needs to look into or which table it needs to look into but it has gone through the data sets that I have it has selected the correct data set which is movie length and within movie length the correct table which is movies right and it's given me a query and this is a simple query but it can write much more complex queries uh than than this right uh in case the movies the more table source is not correct that it has selected you want to look in a different table you can always go and like edit the table source you can find the table using natural language, right? Okay. That, so this is a correct movie. I can just select a different table if the table that was not correct. Right. And just click on apply close and I insert this query. I run, I run my query and I have my answer and just what, like in a matter of five seconds. Right. answer in just what like in a matter of five seconds right uh similarly if there's sql query then and uh you are a new developer uh who is learning sql right or or somebody else has written it and you need to understand it or to get started or take it forward um so gemini gives you the capability to explain that as well so if i just select this and click on explain query it should give me the intent so yeah it's uh given me like all the details of by breaking it down line by line that okay first line is selected does so and so from does this and this this is a conditional statement which does so and so and has given me a very detailed explanation of all of this right um so yeah so that's how uh that's an overview of how uh gemini is present in bigquery as well uh this is again a sample of what i just showed you that uh okay here we have specified the table and we have asked it to like find us that uh a query to find a number of articles in Google with this title. Right. And it's generated this query for us. Similarly, Gemini for development, this was Gemini in all our products, BigQuery databases or in Workspace. This was in Gemini integrated on the products. Now we have Gemini only for developers which is called gemini code assist uh right gemini code assist is something that is present in your ids it is available as an extension uh and it it's present as an assistant to help you uh understand write explain code generate text test cases and more such things and we also have something called gemini cloud assist which is present in the cloud console itself uh if i were just to just show you let me go to the dashboard and if i click on here gemini uh yeah the button here right uh this is basically gemini in cloud code so if i ask it any question question that, OK, I am a starter to Google Cloud. I do not know how to set up a load balancer. So I will just ask it, OK, how to set up a regional load balancer. Right. I'll ask it this question and it should be able to give me like detailed step by step questions of yeah. Setting up regional load balancer. It's given me the types. It told me what I need to do and it's given me a step by step detail along with the resources that. OK, so these are the resources that you can refer to if you want to set up. This is the tutorial and these are the steps. It can also give you commands like what command should I run to set maybe IAM permissions. So basically what I'm trying to say here is that you can ask anything and everything related to Google google cloud doing things in google cloud you want to understand your billing you want to uh open cloud chill and run some code you you can ask anything and everything to gemini and should be able to assist you so it's given me some commands that i can just directly copy paste and run in the cloud chill right yeah so this is gemini in google cloud uh and similarly uh gemini in code assist is present in ide i'll walk you through um how it looks like um in cloud shell right um so i just open there so this is my cloud shell terminal i have already logged into it uh so there are three steps to this the first step is making sure that the Gemini API is enabled if it is enabled you will see a diamond shaped the Gemini logo here on the right bottom corner then you need to log in select a project for which the Gemini API is enabled and then you have your code here and you're ready to get started with Gemini. Right. So this is a sample code of a library of a librarian, library management system. Right. So this is a DAO class. And if I were to, let's say, understand that what this piece of code is right so I'll just ask Gemini that explain this code to me and it will open up this panel on the left hand side and explain to me step by step what yeah so it's given us a detailed explanation that okay like even a very uh non uh i mean not of any experienced coder somebody who is learning uh this newly can also understand it's it's that simple to understand right it's given us very detailed step-by-step explanation of everything that's happening in here in this particular segment of code right um so yeah it's given like point wise in point wise explanations um similarly if i wish to uh you know write or write wish to write some code, let's say I put this in comments that write a method to update librarian contact. I just select this. Select this. And I'll ask gemini to generate code so it will generate some suggestions and yeah it's here so if you can look at the suggested code you can accept it word by word you can accept it completely if you want to accept word by word you just select the arrow button here and you go next to one by one and see if the code is correct uh if not you can just select tab and accept whatever code gemini has generated right uh sometimes uh like most of the times actually it will cite sources for whenever uh it's generating this code. If it's available publicly, any open source documentation or somewhere, it will cite those sources from where it's taking the code. But since this is LLM generated, it is always recommended to check the code that is generated and not just blindly trust it to accept whatever it's giving. So it's always recommended that you look at Gemini's suggestions and you accept, like, have a look at what it has generated and then decide that if this is a code you want to, if this is what you were looking for, right? Similarly, if you are just writing a code, if I were not writing a comment here i was just writing a code let's say um i am just writing some code that okay uh if i just do control enter then it will start generating suggestions again so yeah so while you are in between the code even then you can uh generate suggestions by just pressing ctrl enter and then there we here there's one suggestion but there can be multiple suggestions as well through which you can iterate and see which one you want to uh which one suits your purpose better right um similarly for generating test cases let's say this is the method i want to which one suits your purpose better right similarly for generating test cases let's say this is the method i want to generate test cases for i again select this i select the uh bulb here and i click on generate unit test cases on the left hand side i will see that it's working gemini code assist is working yeah and it has generated a test scenario right or you can choose to insert this copy paste or whatever you want to do with this right so it's generated just to show here it's generated me test cases for this particular method uh right apart from that you can ask your other questions also similar to how uh gemini in cloud assist uh how you were asking questions there you can ask questions here also okay okay um i want to write a code on uh let's say uh to insert some data into my database how do i get started so that's that's there as well um moving on yeah uh so we looked at gemini cloud assist we looked at gemini code assist code assistance is nothing but uh there will be real-time recommendations while you are writing code there will be uh automatic suggestions uh during the code writing process and um for you know uh accepting or declining those suggestions, it's up to you. So this is what I showed right now, Cloud Code. It's an IDE extension. It's available in Visual Code Studio, JetBrains, and the one that I showed was in the Cloud Shell Editor. There are tools to facilitate all development, deployment, everything in Cloud, be it Kubernetes, Cloud Run or Cloud Functions, computing in anywhere you want to run and deploy code. All of that is streamlined through the cloud code operations and integration of Gemini in there. all right this is this again i already showed this uh how do i as you can see here there's a simple prompt that okay how do i use gemini it's nothing uh related to explaining or generating code it's a simple question how do i use gemini here even gemini should be able to help you with that as well um so yeah so when you open just when you open to a cloud shell editor you will see these steps so like you'll see this screen first the one on the left which says walkthroughs and if you just click on code with gemini it will take you to this screen where you can click on connect with google cloud and then your gemini icon will be enabled and you'll go you'll be good to go to you know start coding with gemini these are the three steps that i showed gemini is enabled uh your project step number two your project is selected where you enable gemini and step number three of the you just start chatting with your queries and questions right right um yeah so these are the uh three code assist basic features you can use a do natural language chat you can have answers to your coding questions you can have answers to your best practices questions on cloud um you can have a powered smart actions such as uh test generation code generation code explanation um and many more shortcuts so as you start using you will uh you won't even realize it but uh it starts the uh like showing up in uh while you are writing these codes and while you are doing your tasks uh it automates so much of the tasks that you you won't feel the um like developers would definitely understand when they move from a local development environment to one uh to the cloud code environment uh with gemini enabled you would notice the difference because it's so smooth it automates your tasks so easily that you wouldn't even realize that you're doing these tasks so quickly right um similarly for code assistance, we already saw the demo on how code completion works and how you can generate functions just by adding comments within the lines of code, right? So Gemini supports code generation in 20 plus languages. These are some of the very popular languages languages Java, Python, Ruby, SQL. It understands all of these, it's trained on all of these languages. So it can help you in code generation for all these languages to look at, have a look at all the 20 plus languages. So it's available in the public documentation. So you can take a look at that to see all the 20 languages that are supported. This, we have already seen the demo for this of how you can generate code by selecting the comment lines. And yeah, this is what I was talking about earlier when I said that there can be multiple suggestions. The one that I was generating was a very simple code. So there was only one suggestion, but if you're writing some complex endpoint method services, microservices, you will see more than one suggestions through which you can iterate and then see what to what works best, right. And like I said, with any generative AI out there, generated content is not always correct it is prone to hallucinations uh so it's always always recommended to uh have a human uh in the loop always uh for any llm use cases at all and that is true for gemini with code assist or gemini with cloud assist as well always check your sources always check your references uh before deploying anything to production right um so yeah i showed how you can accept word by word code changes or accept entire suggestions and carefully check all the inserted code and code completion also we took a look at and this is an example of enhanced suggestions which means that i have just written three lines of code and uh gemini basically understood what i am trying to do and it has given its own suggestions right uh just from the first three lines of code uh that i have written right you can just click on control plus enter and it will give you enhanced suggestions on how you can improve your code and that was it uh for today's presentation uh thank you i hope this was useful and we'll take a look at any questions over to you thank you you can see there are several questions that we have in the comment section so i'm just going to go ahead and display them on screen for you to answer. Sure. The first question we have is how can you optimize costs when using Vertex AI for training and inference? Okay. So this is not related to this session, but i'll still answer this question um so there are a number of features that uh vertex uh has uh provided to help you optimize your costs uh one would be uh something called provision throughputs which means that you can uh like reserve your capacity and make sure that uh your uh trainings remain within that right right you can also create there's something called spot like sharing of the vm spot vms and another number of features which allow basically sharing of your compute across projects and across training operations so that way you can optimize costs now i'll not go into a lot of details, but there are a number of things that can be done there. Okay, thank you. The next question we have is, what are the best practices for deploying and monitoring AI models on Vertex AI? So best practices, I think you'll find it in the public documentation, but there are tools to deploy and monitor your ai models in terms of this model registry there are pipelines that you can monitor pipelines for monitoring your jobs right you can look at your drill down on your artifacts and look at your pipelines and optimize by reserving compute and things like that. Yeah. The next question we have is can Gemini be used for creative writing tasks like writing poem scripts or musical pieces? If so, how can we guide the model to generate desired outputs? Okay. So creativity is subjective uh so uh there are a number of ways you can do uh like let's say you have a particular style or tone that you want the output to be in right uh the first step obviously is always to prompt tune uh tune your prompt accordingly how you want it to be but let's say you have a few examples that you would want the model to learn and understand your tone your style then you would then do go for a few short prompting right you give it a few examples of how the input should be how the output looks like and you basically train the model to somewhat work like you want it to, right? In your tone and your style. So it's a prompt tuning, then a few short prompting. And if not, if you want to build your own models, then you can always take the base model and supervise, like fine tune that model through supervised fine tuning or any of the fine tuning methods to have a model which generates output exactly like you want to. Thank you for this. The next question is, how can Vertex AI be integrated with other Google Cloud services like BigQuery, DataFlow and Cloud functions? So Vertex AI is already integrated in all these uh services like i showed in bigquery right uh vertex uh like it's interchangeable like uh gemini is there in bigquery uh bigquery is available as a source uh for creating data stores in vertex so if you take a look at or take a detail look at all the products within vertex ai and within bigquery uh you'll see that it's very tightly integrated like ai is coming to well ai from vertex is coming to bigquery and vice versa uh the one that i showed where gemini is able to generate uh sql queries through natural language prompting that is one such example and a vice versa example would be uh uh in last session i think we looked at how uh agents are created right in vertex um so agents uh can have uh sources data sources on which they are grounded and these data sources can come from big query as well so that's how both of these are intertwined and similarly it's for data flow and cloud functions as well. Thank you. Another question we have is, what are the best practices for deploying AI models to production environments, especially in terms of scalability and reliability? Yeah, so in terms, I'll take this in two parts. First of all, reliability, it's an LLM model so obviously uh you you need to put checks in place you'll need to uh have some sort of methods in place to make sure that it does not hallucinate as much in production uh production environments and that that can be achieved through a number of methods grounding is one method you can either ground it on your own data you can ground it with google search which is again a feature in vertex ai and you can also uh have some sort of like there are a number of controlled generation features and a number of features that come with vertex a that can that in fact customers are using to uh take ai workloads to production right and similarly for scalability, I talked about provision throughput earlier, right? To reserve capacity, to make sure that your resources do not get exhausted and that only you have, that you can be assured that, okay, if this capacity is reserved, then I can definitely scale this without my end users running into quota issues or resource exertion issues. Right. And again, a high level summary. There are a number of best practices that can be put in place. But this is a very high level summary of what customers and clients are doing to take AI workloads to production. Great. I can see most of the questions have been answered by Nikita in the comments. But one last question that we can take is, in the case of users with Chronicle, which metrics can we expect? Which metrics as in, I mean, I do not get the question exactly. Which metrics to expect as in from Gemini or what is it? I think Mario, you could just write in the comments. I need a little more context of what do you mean when you say that which metrics to expect from what? One more question I can see here is what factors should be considered when choosing between Gemini and Vertex-CRA models for a specific project? So Disha, this is completely use case dependent. You can try and test to experiment with different models. We have a number of tools in Vertex to do that. You can choose different models. You can compare the outputs from different models. We have a number of tools in Vertex to do that. You can choose different models. You can compare the outputs from these models. You can, you know, there are tools which allow you to compare two models and see which one suits your use case best. So choosing, so yeah, that would be the answer that choosing a model between Gemini and other models is completely use case dependent if you have a uh multi-modal uh let's say uh multi-modal even within gemini there are two different models right one is a pro model another is a flash model uh so even choosing between those two can be a decision uh if for you uh for your use case latency is a concern uh you do not uh bother a lot about the quality of the output but rather you want the output to be generated like blazing fast speed right uh then you would go for a gemini flash model over a pro model uh if you have a longer context window 2 million context window requirement for a multi-modal use case you want to upload a two hours of video and do some sort of processing on top of that uh then you would go for a gemini model because no other model out there has a two million context window to support that sort of a input size right um so how you choose between gemini models it's a similar choice between other open source models as well yeah so i can see we still have questions coming in. One more question that we have is, how can we deploy CNN ML models with the help of Vertex AI API and Google Cloud Platform? I can send the documentation on deploying models. I mean, it's a step-by-step procedure. I wouldn't be able to summarize here in one line answer. There's documentation for that i'll just share that real quick just give me a minute Yeah, I'll just paste it here in the chat window. Yeah. Okay, I'll just share this link in the comments. Yeah. So all of this is available in public for best practices, questions, and all these questions which are asking the steps for deploying a model and things like that. There is public documentation, very good documentation available. You just need to go to Google and if you just search for it, you'll find it in our official Google Cloud documentation pages. All best practices and the steps to do all of these things. We can take one more question. How can you use BigQuery enhanced with Gemini's advanced analytics and AI to study customer behavior and sentiment through new marketing? Okay. So to study customer behavior, I'm just going to take an example here because this is a very broad question. And I mean, it's not possible to answer in one line again here. But let's say you have some customer data. And there are a number of tables, there's some data and you want to build let's say visualization charts or have some natural language insights out of it right so we have our looker dashboard on top of bigquery which has which helps you create these visualizations and what you can do is gemini is also integrated into looker i haven't demoed that but gemini is also integrated into looker visualization dashboard which means that if you ask it questions that okay you need to find me customers in this what are different customer segments possible uh or let's say what if my customers in this segment in this demographic i would like to understand their behavior help me generate some insights on that if you just ask it in natural language it will be able to help you give summarized answers in natural language again and help you uh create this charts also if you just give natural language inputs right that is one and otherwise in bigquery also you can use gemini to write these auto ml uh queries AutoML, you know, SQL queries and AutoML queries to using Gemini. You can write these queries to build these models for sentiment analysis or customer behavior analysis. Okay. Thank you so much, Shrechi. I think these are the questions that we have. The rest have been answered by Nikita in the comments. Thank you so much, Shrechi. I think these are the questions that we have. The rest have been answered by Nikita in the comments. Yeah. So to everyone watching, this is a QR code that will take you to a feedback form. And I request you to please fill the form so that we can better understand your perspective about the session today. So I'm just going to leave it here for a bit. You can just scan this and it'll take you to the feedback form now thank you everyone for joining us today and a big big thank you to you richie for such an engaging session um it was pretty interactive and we have so many questions as you can see in the comments and to others who are watching remember that if you have any further questions or if you need any type of support please feel free to reach out to us at ai4impactsupport at hacktoskill.com we are also dropping the comment dropping the email in a comment so for your reference best of luck everyone with your hackathon journey and we can't wait for the amazing solutions that you come up with. And thank you again Richie. Thank you for being here, for taking up the time. No worries. Thank you.
Workshop-2 (English) | AI for Impact APAC Hackathon 2024 by Google Cloud
Welcome to the 2nd workshop of AI for Impact APAC Hackathon 2024.

The AI for Impact Hackathon, presented by Google Cloud and powered by Hack2skill, is a unique opportunity to leverage the transformative power of AI to address pressing social challenges across the APAC region. By utilizing Google’s GenAI tools, including Gemma, Gemini, Vertex AI, and Gemini Code Assist, participants will have the chance to create innovative solutions that can drive meaningful change and improve lives. By harnessing the potential of Google’s cutting-edge technologies, develop solutions that address a wide range of societal issues, from healthcare and education to sustainability and accessibility.

To know more and participate, visit: https://vision.hack2skill.com/event/aiforimpact_reg?utm_source=H2Ssocial&utm_medium=H2Ssocial

Register and submit your AI idea before 17th November, 2024.