Achieved Google Cloud Champion in AI and ML. I knew you get a lot of knowledge and friends by attending the conferences but never knew you may get the title of Google Cloud Champion too. Thanks to Walter Lee who suggested and recommended me at Google Next, got interviewed by Google Vertex AI team and now an official Google Cloud Champion in AI and ML after attending the Google I/O. This year I/O blurred some lines between Google's other products and Google Cloud because of AI and Models. The AI advancements like longer context windows (2M Tokens) combined with a variety of inputs (MultiModal) resulting in automation of intelligence needed tasks. This gets amplified if multiple Agents of different but complementary intelligence get combined resulting in beyond human ability for certain types of tasks (work). For example, Project Astra's visual observations of surroundings can be more detailed than humans can observe. The demo use case was to ask how to fix broken things by showing it and getting context aware steps to correct. No more manuals needed. It's not limited to individuals and can be any industrial application including software architecture diagrams e.g. include cache for faster data retrieval. Other advancements are more realistics Image and Video generation as well as new types of music by combining the desired instruments. Gems are your own customized expert for different topics like finance or learning. For learning, the tutor will teach the steps to answer and not just the answer. Impact on AI Systems: Accordingly, the ML systems will also change from single Model Inference pipelines to Multiple models (or chain of Models) inferences and then post processing of inferences for the final output. Instead of only calling Models, you need to build Agents (deployed App) which are a combination of Models + Orchestrations (langchain) + Runtime env. + Tools like RAG or Function calling. Parallel function calling completes multiple sub-tasks at the same time e.g. Function 1 can check Product in stock while Function 2 can get store distance and open timings in the SAME time. RAGs also need to go MultiModal, instead of text embeddings need to do MultiModal embeddings to store vectors for image and videos too. As Cloud AI (Server side) becoming bigger and complex, advancements are also happening on Edge AI i.e. On-device (Client side) for privacy, performance and cost. No doubt I met so many people, enjoyed the Starline 3D meeting experience, and variety of food. Thanks to GDG and represented GDG Cloud Indy in NA Connect. #CloudChampions #GoogleCloud #GoogleDevelopers #GoogleDevelopersGroups #GDGs #Cloud #MachineLearning #ArtificialIntelligence #AIML #CloudCertified #VertexAI #MLOps #Gemini #RAG #MultiModal #GooglePartners #GoogleIO
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Congratulations 🥳 Vaidehi 🙌
Congratulations
Congratulations 🎉
Congratulations Vaidehi S. Tripathi, PMP ! Next Thosan Girisona Suganda 😁
Congratulations welcome to the hub Vaidehi S. Tripathi, PMP
Congratulations Vaidehi S. Tripathi, PMP welcome to the club 🎉👏🥳
Congratulations Vaidehi S. Tripathi, PMP
Congratulations!! Vaidehi S. Tripathi, PMP🎉
Congrats!
Cloud & AI Principal Architect = ["GenAI", "Gemini VertexAI", “Infrastructure (IAC)”, “DevOps", “Data”, "CloudCyberSecurity"]~>| 11/11XGCP |1xAzure |1xAws |1xHashicorp{Terraform} Certified ITech Speaker |Content Writer
1moCongratulations 🎉