Jaydeep Karandikar’s Post

The AWS Certified Machine Learning - Specialty certification validates your expertise in ML and AWS technologies.

🚀 Generative AI + AWS: The Future of Innovation 🤖☁️ Generative AI is revolutionizing industries, but it requires immense computational power, scalable infrastructure and quick experimentation environment. While some companies still define their strategies, some implement test cases to experiment while more advanced companies already answer real-life use cases. Here's a sneak peek on AWS offering aroung Gen AI: 🔹 AWS Bedrock: A new service that allows developers to easily build and scale generative AI applications using pre-trained models from AWS without managing the underlying infrastructure. 🔹 AI-Optimized Infrastructure: With AWS Inferentia and Trainium, AWS offers AI-specialized infrastructure to speed up and reduce the cost of AI model training and inference. 🔹 SageMaker: Simplifies the entire machine learning workflow, from model building to deployment, making it easier to develop generative AI models at scale. 🔹 Data Lakes & Storage: Services like S3 and Redshift provide scalable and cost-effective storage for the massive datasets that generative AI relies on. 🔹 AI & ML Tools: AWS offers a rich set of APIs and tools to integrate AI capabilities into existing applications with minimal effort. #GenerativeAI #AWS #Bedrock #CloudComputing #MachineLearning #AIInnovation

Exciting news for machine learning practitioners! We are thrilled to announce the general availability of cross-account sharing for Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM). This powerful new feature simplifies the process of securely sharing and discovering machine learning models across different AWS accounts, thus eliminating the complexities typically associated with AWS Identity and Access Management (IAM) policies. In our latest blog post, we explore the significant benefits of centralized model governance. By allowing users to share registered models with specific AWS accounts or across the entire organization, this feature enhances visibility and governance in machine learning workflows. This fosters an environment of responsible AI and compliance in line with emerging regulations. Join us as we delve into the architecture, use cases, and practical steps to implement centralized model governance in your organization. Learn how to leverage this new capability to streamline ML model approval, deployment, auditing, and monitoring processes while ensuring compliance and ethical standards. Read more about it here: [Centralize model governance with SageMaker Model Registry Resource Access Manager sharing](https://ift.tt/xwVmLR9)

Cohere’s compression-aware model training techniques allows the model to output embeddings in binary and int8 precision format, which are significantly smaller in size than the often used FP32 precision format, with minimal accuracy degradation. This unlocks the ability to run your enterprise search applications faster, cheaper, and more efficiently. Amazon Bedrock now supports compressed embeddings from Cohere Embed #aws #genAI #embeddingmodels #cohere #bedrock

Sharing an interview I did at the AWS Summit in New York. If you've never heard about Amazon Q for QuickSight, this short video will give you the details. #AWS

🌟 𝗔𝗪𝗦 𝗿𝗲-𝗜𝗻𝘃𝗲𝗻𝘁 2023 𝗥𝗲𝗰𝗮𝗽 🌟 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 2023 showcased the transformative power of generative AI, revolutionizing industries by creating images, videos, stories, and code. Key advancements in data, scalable compute, and machine learning technologies, such as transformers and diffusion models, are driving this change. 𝗠𝗮𝗷𝗼𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁𝘀 Amazon Bedrock, a managed service offering high-performing foundational models, simplifies building and scaling AI applications. Amazon CodeWhisperer, an AI coding companion, enhances developer productivity with code recommendations. 𝗢𝗽𝗲𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗔𝗪𝗦 Accessibility: How will AWS make generative AI tools more accessible to non-experts? 🤔 Data Security: What measures are in place to enhance data governance and security? 🔐 Cost Optimization: How can AWS balance model performance with cost-effectiveness? 💸 Scalability: What improvements are planned for scaling AI applications? 📈 Customization: How is AWS addressing the need for more customizable AI models? 🛠️ Ethical AI: What frameworks ensure the ethical use of AI? 🌐 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗽𝗹𝗲𝗮𝘀𝗲 𝘄𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝘃𝗶𝗱𝗲𝗼 𝗜 𝗰𝗿𝗲𝗮𝘁𝗲𝗱. https://lnkd.in/gJ4vstp8

I have completed another AWS course on Machine Learning. This time, I dove deep into the Machine Learning (ML) lifecycle and learned how to leverage various AWS services at each step. Some key takeaways: ML Model Performance Evaluation: Techniques to assess and optimize model accuracy. Model Deployment: Understanding how to efficiently deploy models in real-world environments. MLOps: A crucial practice for operationalizing and streamlining the entire ML lifecycle—from model development to deployment, monitoring, and maintenance. I also explored a cool real-world use case: Amazon’s Call Center, showing how ML improves customer service. #MachineLearning #AWS #MLOps #AICertification #MLDeployment

Completed the Genrative AI Essentials on AWS.

Today, I focused on self- development and earned the AWS Machine Learning: Art of the Possible Certification. The course was fantastic and easy to understand, offering valuable insights into today's technologies and machine learning. #FultecSystemsLtd #SelfInvestment #AmazonWebService #Belize

Check out my new AWS community blog on how to create secure chatbots using Amazon Q Business with data stored on Amazon FSx for NetApp ONTAP.