The generative AI decision tree

Key Takeaways

[Music] generative AI is proliferating highly specialized models are emerging to address industry challenges but with so much potential how do you know where to focus your efforts for Maximum Impact choosing the right approach to integrating generative AI into your platform can be challenging with a multi ude of models and deployment options how do you navigate the complexities and find the right fit for your Project's unique requirements should you use a pre-trained model from Wenders leverage an open source model or build your own custom solution and once you have choosen how do you decide where to run it within Google Cloud's diverse ecosystem don't worry we have got you covered this video will guide you through the Maze of AI model options focusing on the key differences to help you find the perfect combination for your project by the end you'll be equipped with a framework to support making a decision for your AI needs so let's dive in start your critical first step in your AI journey by identifying your use case you may have identified a number of different opportunities for AI within your organization different models will work better for the specific use case so the the first step in your journey is clarifying what you're trying to do once you have firmed up your use case next you will evaluate models the first characteristic to help you evaluate your model is deciding on the source of your model models can be open source like Gemma commercially supported like gemini or customized open- Source models like Gemma and those available through tensorflow Hub or hugging face offer freedom and flex exibility they're freely available often backed by large communities and highly customizable this makes them attractive for organizations seeking cost Effectiveness and the ability to tailor models to their specific needs with all of the freedom and flexibility of the open source models they do require a larger level of technical expertise to deploy integrate and maintain next we have commercial models they come with different sets of advantage Fe es like userfriendly interfaces high performance and often require some type of dedicated vendor support this makes them suitable for organizations prioritizing ease of use and reliability the thing to note is that commercial models typically come with subscription costs and possibly a longer standing contract with a vendor when determining if a commercial model is right for your business think about the importance of platform flexibility versus convenience finally custom models offer maximum control and can be precisely tailored to your specific needs this can provide a significant competitive Advantage however building custom models requires a substantial investment in resources and Technical expertise it's a commitment to building from the ground up based on your use case and the characteristics you need from the source of your model you can identify models that will work for you now you need to evaluate where you want to run that model and you have choices in the platform let's walk through a few scenarios illustrating this path as AI evolves we see distinct patterns emerge based on unique goals of each user and business whether you are a developer building Innovative applications or a researcher pushing the limits of AI Google cloud has the solutions to help you succeed let's EXP explore each one if you are a jni app developer speed and simplicity are your allies you are drawn to off-the-shelf models easy to use tools and low code Solutions you need a platform with high level of abstraction allowing you to quickly build and deploy AI models for your app that's where Vex AI comes in vertex AIS model Garden offers a curated selection of foundation models readily available for deployment and with generative AI Studio you have a noode environment for customization and integration making it even faster to bring your A powered applications to life what if you need to be a bit more involved you're not using AI you are refining it you want to take those Foundation models and make them your own by tuning them with your unique data this means you need a platform with easy to set up data and AI integration and also a custom choice of AI accelerators what xcii gives you that too it allows you to fine tune models with your own data experiment with a different AI accelerators and seamlessly integrate with your existing data infrastructure it's the perfect balance of pre-built efficiency and bspoke performance finally the model builder you are pushing the boundaries of AI training and serving custom models from scratch for you it's all about performance scalability and having that granular control over your AI infrastructure that's where Google's AI hyper computer shines it's a full stack of AI optimized Hardware software and consumption options working together to improve AI workload performance scalability and cost efficiency for example many users leverage platforms like Google could engine as part of the stack to remove the heavy lifting needed to set up AI deployments it helps automate orchestration manage large training and inference clusters all the while giving you portability and helping you optimize costs so there you have it as you navigate the world of generative AI remember that a tailored approach with the right model format and platform will pave the way for maximizing your investment and achieving your goals when evaluating your model formats use this framework to find a solution that strikes the right balance between cost Effectiveness and your team's needs look for a model that fits comfortably within your budget while being easy for your team to use and adapt to your evolving requirements and of course make sure it provides the level of control you need over your AI solutions for platforms we explore two powerful options on one hand there is Vex which is ideal for Rapid developer M and deployment this is your go-to for efficiency and speed on the other hand we have ai hyper computer which is designed for organizations needing custom AI models choose this for maximum flexibility and control ultimately the right approach depends on the speed cost and control your organization needs Google is driving Innovation at every layer of the AI stack enabling you to achieve higher performance productivity and cost efficiency bring your AI workloads into production and transform how your business operates and serves its customers check the description to explore our Solutions today and start building the future of your business [Music]