Retool’s Post

I’m happy to share that I’ve obtained a new certification: AWS Educate Introduction to Generative AI from Amazon Web Services (AWS)! https://lnkd.in/dN5HBQ4N #aws #GenAi #AI

The Hugging Face Platform offers no-code and low-code solutions for deploying AI models on AWS. Key features are Inference Endpoints, Spaces, AutoTrain, and open source tools to democratize ML. #aws #awscloud #cloud #artificialintelligence #awsmarketplace #awspartnernetwork #customersolutions #generativeai #intermediate200 #awscompetencypartners #awspartnerguestpost #awspartnersolutionsarchitectssa #awspartnersuccessstories #huggingface

⭐ Excited to share that I’ve completed the "Introduction to AI and Machine Learning on Google Cloud"! ⭐ This course provided valuable insights into the fundamentals of AI and machine learning, equipping me with the skills to leverage Google Cloud's powerful tools. I’m looking forward to applying what I’ve learned in my projects and continuing to explore the vast potential of AI in data science. A big thank you to the instructors and the community for the support! 🚀 #AI #MachineLearning #GoogleCloud #DataScience #ContinuousLearning

Exciting news! Our latest blog post dives into the collaboration between OpenAI and Oracle Cloud to expand Azure AI. Learn about the impact on AI development and the future of cloud computing. Follow TechNews Pub for more updates! Visit us - https://lnkd.in/dPY6F-kG #OpenAI #OracleCloud #AzureAI #AIdevelopment #CloudComputing #TechInnovation

It was good to brush up on my ML and Statistics skills 🙂 For a marketer or any enterprise #CX leader , embedded (i.e. black box) AI models in their #CDP, #CustomerService or #MarketingAutomation solutions are not enough. They lack control over the tuning of the model (with issues like model drift and training/serving skew), or the ability to do Feature Engineering, and the algorithms are often simple linear regression or k-means clustering. This leads to losing valuable personalisation opportunities. I am glad to see the increased adoption of #BYOM from the likes of GCP #VertexAI and AWS #SageMaker in Twillio Segment and Salesforce Data Cloud. I also like how #VertexAI has an offering for different levels of AI maturity from basic via AutoML and BigQueryML, to sophisticated with TensorFlow and Keras so organisations can evolve their use of machine learning.

I thought when I first worked in business Intelligence that I will take it as my granded passion and stick to. Then when I started my research in AI, I thought wow it’s also magnificent, interesting and am so enthusiasted to learn more about. And then I jumped to cloud computing and big data, and I found that am aslo intersterd in. As a conclusion, I think my passion to learn will never stop, no matter how much I love what I have learned, there are still many to learn!

Google has historically been a leader in AI research, with innovations like TensorFlow and the transformer model, which have played a significant role in the development of generative AI. However, in the race to commercialize these technologies in the cloud, Google Cloud is contending with strong competition, particularly from Microsoft Azure, AWS, and emerging players like Databricks. Azure has surged ahead by integrating OpenAI's models and focusing on enterprise solutions, while AWS continues to dominate with its extensive cloud infrastructure and specialized AI services, such as its custom-built silicon (Trainium and Inferentia) designed for AI workloads. AWS’s robust ecosystem and focus on catering to developers and enterprises alike make it a formidable competitor in the generative AI space. Databricks, known for its powerful data processing capabilities, has further strengthened its position with the addition of Mosaic AI. This enhancement allows Databricks to manage and deploy large-scale AI models more effectively, making it a preferred choice for organizations focused on data-intensive AI workloads. Recently, I completed the "Create Generative AI Apps on Google Cloud" course on Coursera, which provided hands-on experience with Google's cutting-edge AI tools. This course highlighted Google's robust capabilities in developing generative AI applications, reinforcing my view that while Google may not currently lead in market share, its technological foundation and ongoing investments position it as a strong competitor in the generative AI space. In summary, Google Cloud is actively working to solidify its place in the generative AI market, leveraging its deep AI expertise and innovative tools. The competition is fierce, with Azure and AWS holding significant advantages, and Databricks excelling with its enhanced Mosaic AI platform for data-intensive AI workloads. Despite this, Google’s ongoing efforts and advancements make it a key player to watch. #GenerativeAI #Google #AWS #Azure #Microsoft #OpenAI #Databricks #VertexAI #AmazonBedrock #AzureAIStudio #Databricks #MosaicAI #LLM #LLMs #Tensorflow #Pytorch #Nvidia

Thinking of delving into the seemingly complex world of Generative AI? Read and let me know what you think especially if you're new to cloud computing! #GenerativeAI #CloudComputing https://lnkd.in/dXSd66-T

Introducing Dataflux Dataset for Cloud Storage to accelerate PyTorch AI training The PyTorch Dataflux Dataset abstraction accelerates data loading from Google Cloud Storage, for up to 3.5x faster training times with small files. https://accntu.re/4dMu3W2 #cloud #cloudarchitect #cloudadoption #cloudengineer #cloudengineering #cloudarchitecture

AWS Machine Learning - Specialty is not just about theory, it gives practical in-depth knowledge of the latest modeling techniques and cloud services that you can use to innovate with AI.