From Software Developer to AI Engineer: Antje Barth

Key Takeaways

[Music] hi everyone I'm so excited to be part of this conference and share with you five practical steps from software developer to AI engineer and if anyone is wondering here this avatar on the slide this this is what happens if you ask AI to make you look a little bit more agentic all right let's get started so I'm pretty sure everyone is familiar with this image here and the post from swix that defines the new role of the AI engineer and as you've experienced probably daily in your jobs you don't need to be a full ml researcher anymore or data scientist thinks that took months or years before to get AI project s into production is now able to be just a couple of API calls super exciting but still if you're working with AI it still makes sense to understand the basics of the technology and this involves a couple of things right so you have to understand at a basic level how Foundation models work why they're sometimes producing output that you don't expect in in your application code right you have to understand how you can customize the models how you can you know for example sometimes F tune models to adapt them to your specific use cases and data sets how to include functions in your application code to give them access to additional systems the good news is if you're just starting on this journey to become an ni engineer there's plenty of resources now these days available to you to learn and I wanted to call out one specific course here which is called generative AI with large language models a few colleagues in mine we actually collaborated with Andrew in and the team at deeplearning.ai to put this course together and help you understand the fundamentals of generative AI to help you build real word applications if you're curious it's available on deeplearning.ai and on casera now the second step in this journey is to start get handson with the AI developer tools to help you increase your productivity and I think we're all seen this quote here and we experienced that in our daily jobs that how we do work how we develop applications has changed a lot these days we can literally use natural language inputs to interact with applications and really English has become one of the most um popular and hottest program languages I think um we can see this happening for example you can go these days from English to code by asking AI to for example rewrite a readme file we can also do code to English for example asking AI to document functions in our code but this is not all if we look at the software development life cycle I think many of us can agree that the majority of time we usually spend not writing valuable code but all the other things around it so sometimes up to 70% of unvaluable tasks which is writing boilerplate code writing documentation trying to maintain old codebase right and sometimes we only have like a fraction of the time maybe 30% that we're spending on actually what you know creates joy and kind of the creative tasks in software development and this is what led us at AWS this inspired us to create Amazon Q Amazon Q is a j fi powered assistant specifically developed for software development and this is much more than just aod coding assistant Q developer actually uses agents to perform much more complex tasks and help you automate those for example feature development and also code transformation think about working with old Java based codebases that you need to migrate maybe to a newer Java version and to show you how this works I asked my colleague Mike Chambers to put together a quick demo let's have a look with Amazon Q installed inside of my IDE I can go to new tab and I can start a conversation with Amazon Q developer and I can do the kinds of things that maybe you'd expect such as how can I create a serverless application how do I get started and the chat session brings back a list of instructions of what I should do starting off by installing AWS Sam CLI how to do that where to get that from and how to step through the creation of a project now if I've done that then serverless Sam for example might actually come back with some generated code and here is that code maybe I don't quite know what this code does so I can rightclick on the code and send it to Amazon Q asking Amazon Q to explain and the code then will go into a prompt along with explain and generate an answer and this is great for code that's been generated for us but also Imagine code for Legacy systems something that was worked on Years Ago by somebody else where you can get Amazon Q to help explain it we can also get Amazon Q to generate code now this is again probably the kind of thing you'd expect I can put in a comment line inside of my code in this case I want to create an input checking function I'm going to give it some more definition here that I actually want it to trim any string that's being sent in into this function and yes Amazon Q can generate this small function well that's great but what about if I've got more code that I need to have generated well I can go to the chat and put in /dev and I can put in a much more comprehensive description of something that I would like in this particular case I'm going to ask for it to write a function to search by category in Dynamo DB with a bunch of details about the way that want the output to be formatted so this is much more than just a single or a few lines of code and then this particular case what's going to happen is it will come back again with a stepbystep list of what's required so I need to add in template. yaml it's recommending that I create search by category. MJS and many more things but this isn't just a big shopping list of things that I need to do this is is actually a plan and it's a plan that Amazon Q can actually follow for us so it generates some code as a change set something that we can look at the difference between our current code and what it suggests and if we like that we can actually click on the insert code button and it will add all of that code into our project way more than just a couple of lines so Amazon Q developer is much more than just code completion all right if you're curious to learn more about Amazon Q Amazon Q developer we have a couple of more sessions throughout this day so make sure you're checking um those Expo sessions and we also do have a session at our AWS Booth here you can also visit our Amazon Q developer Center for much more examples what you can do with it all right let's come to step three and this is where the funds starts start prototyping and building with AI and the fun includes a couple of steps right everyone developing with AI knows this it starts all with defining your use case and then really you're on this road trying to choose from different models you're trying to you know customize them to your use case decide whether it's prompt engineering whether you do rag whether you need to do a little bit of fine-tuning there with your data and of course across the the whole development workflow you have to incorporate responsible AI policies making sure data is private and secure and also implementing guard rails into your application and then when you're integrated another fun part obviously working with the agents what we're hearing a lot here throughout this conference and the fun topic of you know how to keep them up to date geni Ops I think there's a lot of terms for that MFM Ops llm Ops so really kind of um a lot of things to consider here I want to dive in briefly into the topic of models to choose and this is really an important topic when you're evaluating models you have to really evaluate them thoroughly because most likely there's not just going to be one size fits all for you in fact if you look at all your use cases you want to implement there's likely no one model to rule them all and this is why we developed Amazon Bedrock Bedrock is a fully managed service that gives you access to a wide range of leading Foundation models that you can start experimenting with implementing into your applications it also integrates the tooling you need to customize your model whether it's fine-tuning also to include Rec workflows to build agents and of course everything in a secure environment where you are in full control of your data and speaking of choice just to give you a quick overview as of today this is the selection of models you can choose from we're working with leading companies such as eii 21 Labs entropic CER meta M tril stability Ai and we also offer our own Amazon Titan models for you to choose from and I'm super excited just to call this out last week together with entropic launch we integrated cloth free5 Sonet on Amazon Bedrock as well so you can also since last week use this model super exciting now with Choice also comes responsibility right and we continuously innovate and trying to make it easier for you to build applications across the different model types and just a few weeks ago we introduced a new unified Converse API in Amazon Bedrock what does this do the unified Converse API helps you with a new unified mthod structured invocation meaning you can use the same parameters and bodies regardless of which model you choose and we are on the platform side we're handling this translation if parameters are called different for the different models handling the system user assistant prompts for you and also giving you a consistent output format and as well having native function calling support in here but let me show you how this looks in code so here's the python example that shows how you can use the new API this is python so we're starting by just integrating the python SDK client here and then you can Define this list of messages and here's for example where you put in your user message prompts you can put in system prompts as well and then this message list you can just pass in this single API call using the converse API here in the model ID you can choose which model you want to test here I'm using an entropic model and then pass the messages and also the inference parameters and again in this API all those parameters are standardized and we're going to make the work behind um the covers to convert this to the spefic specific format that the model is expecting so you have an easy way to work across different models similarly here for function calling we do have a support built in that with the models that support it so how we implement this is by defining a tool list so tool here equivalent to to the functions you want to give access to and then when you're doing the converse API call you can pass this list of tools all right if you want to find out more about Converse API here's the link to our generative AI space on community. AWS which has a lot more Co tutorials code examples not just for python but across different languages as well so check it out the author here Dennis Trope is also somewhere in the audience here this week so if you want to connect with him talk about different code examples and how to use the API um feel free to reach out all right now let's integrate AI into our applications and this can be a whole session in its own but I want to focus on one of the hottest topics right now that we're discussing during the conference which is of course agents and I have one more demo here and I asked my colleague Mike last time to put together an exciting demo to show you what you can do with agents Mike and we need sound to be able to create agentic workflows right inside of the AWS console and inside of the service it works fully seress and I've used it to create an agent that plays Minecraft let me show you how I did it if we jump into the AWS console go down the menu on the left hand side to agents um you can see the agents screen here and I can open up my agent my Minecraft agent now if I just go into agent Builder U and just expand the screen out a little bit you get to see some of the parameters that iuse used to create this agent so you can see the large language model I used in this case Claude 3 Haiku and you can also see this the instructions for the agent now this is not some notes for myself this is actually prompt engineering that we're doing to explain how we want the agent in this case the Minecraft Bot to play the game and then we also have to add some tools in right some Minecraft tools so we do that through actions and inside of action groups so I've got a couple of different action groups we've got Minecraft actions and Minecraft experimental let's have a look at actions and inside of here we can see some really simple things some actions that the bot will be able to do and these are all linked up to code so we've got the action to jump we've got the action to dig and you can see the description here for Action to dig it's got some instructions again this is prompt engineering and then we've got some parameters that we can select collect in fact we require these parameters so the bot needs to get these for us um if I scroll down a little further there's a couple of really simple actions in here action to get a player location and action to move to O location I want to show you those in action because the bot can actually problem solve and reason its way to be able to use these tools to solve simple problems let's jump into the game um and so it is nighttime so let's set it to be the daytime um so that we can see what's going on so set time to day okay and there in the middle of the screen you can see Rocky Rocky is the Bedrock agent running inside of the game and we can talk to it we can have a chat session but what about if we want it to come to us now there is no tool to come to us so if I just I'm just going to back up a little bit further make it a little bit more of a challenge and I'm going to say come to me in chat and what's going to happen now is that the agent's going to reason through a whole set of actions it's going to look to see who requested that it's then going to take that name and that's my name and it's going to find the location of that player and then it's going to map a path from where it currently was to me all of those things happened all in that blink of an eye and there's a gentic workflows making all of that happen this is super exciting I'm discovering new things that this bot can do every day um but with that it's back to you all right thank you Mike if you're curious to know how we did this check out our booth session we we're running the demo there as well and we have another session in the agents track later today so make sure you're popping in there if you want to know more and of course you can find the project code for this on GitHub so if you want to play play it on your own and how you can integrate agents into a fun thing check out this project all right we're almost there so the last step I really want to call out is stay up to date there's so much happening in this space as you all know and a really good way to do that is to engage with a community speaking of community I have one last announcement to make and I'm super excited to announce that we're Transforming Our AWS Loft here in San Francisco into the AI engineering hub for the community so we're super excited to host workshops events and meetups there if you want to suggest a couple of topics you're most interested in to make those events most valuable to you fill out this quick survey here also if you're interested in speaking or hosting a Meetup yourself you can let us know and also we do have another event tonight which I think we're reaching capacity or just half reach capacity but we do have a happy hour with entropic tonight at The Loft in case you didn't make it anymore and we're at capacity um don't worry we're working on putting together much more events like this in the upcoming weeks and month so keep an eye out for those and with that I'm coming to the end of my presentation this wraps it up the five practical steps to become an AI engineer and let's innovate together and I'm looking forward and I'm excited to see what you build with AI thanks so much make sure you're checking out the rest of the sessions here and also pop by our booth outside thanks so much [Music]