Raheel Iqbal’s Post

With 93% of organisations planning to integrate generative AI into their operations within the next five years, the demand for skilled professionals in this field has never been higher. However, despite this growing demand, a staggering 75% of organisations struggle to find the talent they need to harness the power of AI effectively.

🤖Unlock the Future of AI with OCI’s Free Certification!!! 🚀 Embark on a Journey of Innovation: Dive into the world of Generative AI with OCI’s 2024 Generative AI Professional Certification and Course. It’s your chance to stay ahead in the tech revolution! 🎓 Free Certification Promotion: Don’t miss out! Access the OCI Generative AI learning and certification opportunities at no cost until July 31, 20241. It’s an investment in your future that won’t cost you a dime. 🔍 Course Highlights: Technical primer on Large Language Models (LLMs)4 Hands-on experience with OCI Generative AI service Build a conversational chatbot with LangChain framework Professional-level certification exam to showcase your expertise 📅 Flexible Learning: Complete the 6-hour course at your pace and emerge with a solid understanding of LLMs and the LLM application stack. #OCIGenerativeAI #AICertification #FreeCourse #TechCareer #Innovation #AIRevolution #OracleUniversity #LLM #GenerativeAI #AIProfessionals #OCI #Customersuccess

I am happy to share that I have successfully completed course on Introduction to Artificial Intelligence (Al) from IBM offered by Coursera. #ai #artificialintelligence #ibm #coursera

INTELLECT-1 is the first decentralized 10-billion-parameter model. It leverages global contributions for open-source AGI development. OpenDiLoCo scaling has been achieved for training large models across distributed devices, with new advancements in bandwidth efficiency and fault tolerance. The new Prime framework further optimizes decentralized training, increasing compute utilization to 98% for the INTELLECT-1's 10-billion-parameter training run. #ai #technology https://lnkd.in/gJEg24it

To develop an LLM for your organization, you have to face the challenge of time effort, compliance, and cost of feeding AI with intense volumes of data. But what if you could train LLMs with synthetic data, saving both time and cost? IBM Research is developing a fast and secure method to do just that. Reach out to learn how you can start enabling this technology today! #AI #LLM #IBMResearch

I recently completed my #Generative AI course on Coursera, and it was an incredibly engaging experience, packed with hands-on lab work. One of the most interesting aspects was understanding prompt engineering. Essentially, you can use a base #LLM (Large Language Model) and retrain it with prompts. For instance, you can use single-shot prompts where you write a prompt with the expected response to train the base LLM, or you can use multiple prompts to enhance training. However, there are limitations, such as the size of the prompts, and tuning the base model weights can be expensive. Imagine trying to train a base model like FLAN-5 with its 250 million parameters! Another fascinating concept I learned is #PEFT (Parameter Efficient Fine-Tuning). This method involves training just 1-2% of the base model's parameters. It's widely used because it allows you to fine-tune a base model with your own training data, essentially creating a custom model. Hyperparameter tuning is crucial here. However, creating the training data is vital and can be time-consuming. Often, you need data from external sources or real-time data to train the model effectively. Reinforcement learning (RL) is ideal for this, where you train the model using a reward model and let it learn from the training data. Developing an effective reward model is critical, but RL offers a cost-effective way to train LLMs, including the reward model itself. Then there's Retrieval-Augmented Generation (RAG), which allows you to train LLMs with existing documents, like those from PLM or SAP systems. This method can turn these documents into a valuable knowledge source for the organization, easily retrievable through prompts. We also explored the Program Aided Language (PAL), which integrates LLMs with existing systems involving complex algorithms and mathematical calculations. This allows for real-time data and calculations to be integrated and trained with LLMs. Reflecting on all these learnings, it's clear that #Generative AI is set to disrupt the traditional software development life cycle. In the future, individuals and organizations could have their own LLMs functioning as central brains, acting as data contextualization engines and learning from behaviours and actions within the organization. This will significantly impact #IT-OT convergence and help organizations simulate behaviors with minimal effort. For my practical project, I trained the FLAN-5 model with wind turbine simulated data and error codes. It was thrilling to prompt the model with questions like "What is error code T1 related to?", "Why did this error happen again?", and "How to rectify it?". Despite having limited resources, the process took a couple of weeks, but the results were immensely rewarding. In summary, the course was a deep dive into the future of AI, showcasing how LLMs can transform organizational knowledge management and operational efficiencies.

I’m happy to share that I’ve obtained a new certification: Introduction to Artificial Intelligence (AI) (with Honors) from IBM IBM Coursera #introductiontoai #ai #ibm #coursera #certificate

Oracle Cloud Infrastructure (OCI) provides a robust suite of services tailored for developing and deploying artificial intelligence (AI) applications. Leveraging OCI's scalable infrastructure, organizations can efficiently build and deploy AI models. Essential AI services on OCI include: - Machine Learning: OCI Machine Learning offers a managed platform for constructing, training, and deploying machine learning models. It features automated hyperparameter tuning, model deployment, and management capabilities. - Data Science: OCI Data Science fosters a collaborative environment for scientists to explore, analyze, and visualize data. It seamlessly integrates with other OCI services to streamline the AI development workflow. - Autonomous Database: The OCI Autonomous Database is a self-driving database designed to store and manage data essential for AI applications. It boasts self-tuning, self-repairing, and self-securing functionalities. AI Services: OCI delivers specialized AI services, such as computer vision, natural language processing, and speech recognition. These services offer pre-trained models and APIs for easy integration into applications. Rapidflow Inc, led by Adil M., is a prominent player that harnesses these cutting-edge AI services on OCI for innovative solutions. #OracleCloud #AI #Innovation

Increase accuracy and reduce bias in AI models #AI #HPEAI #ArtificialIntelligence #SwarmLearning #MachineLearning #ML

🌐 @HPE Swarm Learning brings decentralized machine learning to the forefront, enhancing accuracy and reducing bias. 🎯 Dive into the details in this insightful blog, courtesy of rusd solutions! #hpe #machinelearning #ai #techsolutions #innovation #rusdsolutions