Generative AI Project Lifecycle-GENAI On Cloud

Key Takeaways

hello all my name is Kish naak and welcome to my YouTube channel so guys we are going to continue the generative AI on cloud series and in this video I'm going to probably discuss about the Gen AI Project Life Cycle now since you already know that we are definitely going to develop a lot of applications specifically in cloud from the basic data inje till the deployment so it is very much necessary that you actually understand a generic workflow of the in AI Project Life Cycle so let me quickly go ahead and let me go ahead and write some amazing things for you in this particular notebook and I will be explaining you completely step by step how you can probably see or follow a Project Life Cycle and uh as we go ahead there will be a lot many things that will be coming like llm Ops platform uh and uh we will be working specifically with Azure uh AI Studio AWS Sage maker studio and all so definitely both the clouds will get covered so before I go ahead please make sure that you keep the like Target of all these kind of videos till thousand that will definitely motivate me and I've been exploring many more things so that you get the right kind of guidance and knowledge so let me go ahead and let me start the Gen AI Project Life Cycle uh with respect to this gen AI Project Life Cycle I would like to make this entire life cycle into four to five steps okay the first step that is nothing but here I'm just going to write over here it is basically defining the use case okay so what kind of use case are you solving now this use case is can be a rag application can be a text summarization application can be a chatbot So based on different different use cases that actually depends on your requirements your company requirements so this is the first step you really need to define the use case that you are specifically doing now with respect to this particular use case we usually take this entire module into the scope part okay so this is basically my scope right if I basically use a generic term now once you define a use case you let's say that I am going to probably develop a rag application in that I'm going to definitely use Vector databases I may have a lot of PDF files I also need to probably convert that into a vectors and store it in some kind of vector to DB so some kind of use case you really need to Define and all the requirements that is required in that particular use case coming to the next step coming to the next step which is super important because this steps will be involving two important things okay and that is nothing but choosing choosing the right model OKAY the right model when when I say choosing the right model here there are two different things that you can probably split this into one whether you are using or you are probably using some kind of foundation models so here I'm going to probably write whether you are using Foundation model and solving a use case so this is the one category that I would like to divide this particular module into the other category is that whether you want to build your own custom llm right custom llm now see there are two things over here right when I say Foundation model Foundation models are already those larger models like open AI llama 2 llama 3 right you have Google gin Pro so these all are very huge Foundation models and for for most of the generic use cases you can directly use those kind of foundation models and you can solve the use case itself right now with respect to this Foundation models we can also further go ahead and do fine tuning right so let's say I have a find uh I have a foundation model which I am specifically using to solve my business use cases on top of that if I really want to make this Foundation model behave well for my own custom data then what I can do on top of this Foundation model I can use Laura CLA techniques and I can probably fine-tune all these kind of models okay so this is one of the step the Second Step the second step that I have written over here is Custom llm Custom llm is nothing but it is building your llm from scratch building your llm from scratch from scratch right and obviously uh this one there's a lot of benefit also if a company is building an llm model completely from scratch for the specific use cases but lot of resources will definitely be required we have to really take care of model hallucination many things and all as we go ahead but yes uh I've also seen many many companies developing their own custom llm model okay so choosing the right model or what kind of models you're specifically us to solve this particular use cases that becomes the second important module with respect to this gen AI Project Life Cycle and obviously I've spoken about Foundation models both in AWS you have in Google you have in Microsoft aure you have currently Microsoft Azure AI Studio specifically have all the excess of opening Serv obviously it is investing a huge amount of money over there okay now once you probably select the right kind of model there are main three task that you probably do for going forward okay so main three task okay the first task is nothing but you can specifically use prompt engineering and solver a use case prompt engineering and solver use case the second task that you actually can do is nothing but fine-tuning right fine tuning fine tuning so with the help of fine tuning also you can probably develop your own custom llm model and on top of that you can basically do it let's say you're completely creating your llm model from scratch one more important mechanism that you have is nothing but uh aligning uh or you can probably say training with human feedback training with human feedback and this is one of the very important step that is actually used while you're training your llm models how a llm model is basically trained I've already created a video in my playlist uh with respect to the Lang chain and all generative AI playlist you can probably go ahead over there fine tuning how to specifically do fine tuning and all that also I've actually shown you the reason why I'm showing you this gentic AI Project Life Cycle because tomorrow when I'm probably creating videos um let's say in the upcoming videos when I'm creating videos related to AWS series over there you'll be seeing all these particular steps going ahead okay now once you probably do all the steps uh the further step is something called as evaluation okay evaluation is basically seeing that how your model is performing by performing all these particular steps there also different different performance metrics which we are probably going to follow okay this two steps I would like to combine and say something like this okay so I'll say probably adapt and align models okay so this will be the specific model model that uh we specifically use for this purpose now over here your model will be ready everything is perfect uh you are able to solve the use cases let's say your performance metrix is increasing over here so your metrix is specifically increasing and it is saying that now your model is ready now it comes to the deployment part right so with respect to the deployment part I would uh definitely say deployment uh further you also need to do lot of integration with different different application so I will probably say application integration okay application integration and here uh what we do we specifically perform two major step one we optimize models okay um I'll just write optimize and deploy models optimize and deploy models okay and this deployment is specifically done for inferencing okay and here is where most of your Cloud platforms here is where your llm Ops llm Ops is used you know different different inferencing techniques are there one technique I've already covered with respect to a platform which is called as Gro right it uses a inferencing technique which is called as lpu so it is always good idea that you should definitely know know multiple ways of inferencing see at the end of the day whatever models you specifically create unless and until the inferencing is not fast you definitely cannot use those things right so it is very much necessary that you know the idea of this module extensively because tomorrow building all these things is very easy fine tuning is very easy right you definitely have a template a framework a data set preparation and all and you can per perform this particular step so that is the reason in this series of videos be seeing that how much I will be focusing on this llm uh llm mops platform right and I will also show you multiple platforms like which can definitely make your inferencing very much good so this is the most important thing here uh definitely we'll be using AWS Azure right you can use all these things gcp and we'll see what all services they have specifically provided probably for the inferencing purpose again but initially our Focus will definitely be on AWS okay then the second step uh um after we do the deployment uh in the application integ integration what we do next is that we build U llm powered application llm powered application powered application because your integration is done your API is created now it's all how well you can actually build the solutions you can solve different different use cases and all so this overall gives a brief idea about the entire gen AI Project Life Cycle um since uh we have already started this generative a on cloud so this is necessary to know and uh you should probably follow all the steps and whenever I create any videos with respect to gen on AWS all the steps will be considered in mind and it'll be shown to you so I hope you like this particular video this was it from my side I'll see you all in the next video have a great day thank you one all please make sure that you hit like share with all your friends I'll see you in the next video thank you take care bye-bye