What do you do if you want to level up your AI expertise in cloud computing and distributed systems?

here’s a structured approach based on the advice you mentioned about having a strong foundational knowledge: -deepen your core AI knowledge: *study advanced algorithms *experiment with AI frameworks -master cloud computing essentials *learn cloud service model *get hands-on with major cloud providers *certificates and training -understand distributed systems in depth: *study system design *explore big data technologies apply AI in a distributed context *develop distributed AI applications *use cloud-based AI services *edge computing and IoT join a community and collaborate *participate in hackathons and competitions *contribute to open source projects stay updated and keep learning: *follow industry trends continuous education

In my experience to enhance AI expertise in cloud computing and distributed network do the following: 1. Identify the cloud hyperscaler provider you want to start with. 2. Identify the domain you want to get into like generative AI, Machine learning and so on. 3. Find the services related to the technology. 4. Experiment and do a lot of hands on. 5. Create a demo app using that service and then deploy it on the hyperscaler. 6. Apply proper security checks like firewalls, load balancers, VPN and so on. By doing the above you’ll gain expertise in AI as well as cloud computing. This deployment will also help you in gaining knowledge on distributed network.

Knowing basics of machine learning and AI is obviously very important. But the way AI is growing and the way low code no code approach is provided to us via these cloud services providers , all we users need to do is to search for use cases. And for that we need to read, read and read.

The most important aspect of learning any new skill is making sure you're clear on the basics, and AI is no exception. To start, you'd be amazed by how much you can learn through baseline conversations with ChatGPT. Ensure that you're providing the context of what you're looking to learn and consider setting the stage by telling it to act as a professor teaching these concepts to a new student. Never be afraid to ask follow-ups and clarifying questions either! After you're confident in your foundation, I'd recommend moving to YouTube to get more targeted instruction on any confusing concepts. Beyond that, resources like LinkedIn Learning and Udemy are amazing platforms to start increasing your abilities. You'll be an expert in no time!

Learn to “think cloud”. Most of AI practitioners will be familiar with software design-level abstractions. Meaning that they can think about implementation of AI applications in terms of classes and functions of popular frameworks like PyTorch and TensorFlow. Working with cloud is different. You need to think services and systems. Most of cloud providers will have a service for everything. Instead of thinking about the implementation code, you can focus more on the use case. Another great advantage of cloud is the elasticity and cost efficiency which also leaves more room for focusing on the business case and value creation rather than worrying about an initial fund and complex financial setup.

Cloud markets are oligopoly in nature. Getting familiarised with the common tools make us upto date with market trends. Azure and AWS are common ones. Thus making sure we have the best understanding on cloud based AI applications

There are many ways to gain practical experience and deepen your understanding of AI tools. Exploring TensorFlow is a great way to grasp the mechanics of these systems.

To level up your AI expertise in cloud computing and distributed systems, its beneficial to gain hands-on experience with a mix of leading commercial and open source tools. On the commercial front, platforms like AWS managed AI services (i.e. Bedrock, Transcribe, Kendra, Rekognition, etc.) and code-first services like Sagemaker. Google Cloud and Azure also offer comprehensive services that are integral for building and deploying scalable AI solutions. For open-source tools, becoming proficient in technologies like Kubernetes for orchestration, Apache Spark for big data processing, and Tensorflow/PyTorch/HuggingFace/LangChain for various AI use cases can provide a solid foundation.

The best way to upskill yourself in AI is to leverage the tools! You need to get familiar with the basic stack and popular cloud services such as AWS, Azure, GCP provide tools that allow you to test and get familiar with the product. A great way to start would be to pick a specific area of AI (e.g. computer vision, timeseries forecasting, GenAI, etc) and go into the tools used to provision your environment, train a model and make inferences from the model. Learn larger ML frameworks such as PyTorch or TensorFlow is a great start, but you can look into managed services (such as SageMaker Canvas, Amazon Bedrock, Rekognition) which can be accessible via UI/API and take away the tedious/complicated tasks to get you up and running much quicker.

In practical coding, focus on writing clean, concise, and well-commented code. This improves readability for you and future collaborators. Regularly test your code to catch errors and ensure functionality. Utilize version control systems to track changes and revert if needed. Finally, consistent practice is key to mastering any coding language. By following these principles, you'll write strong, maintainable, and efficient code.

To level up your coding skills, dive into open source projects on GitHub! ?? Many repositories offer beginner-friendly tasks ?? (look for the good-first-issue tag).

To level up your AI expertise in cloud computing and distributed systems, I'd recommend diving into cloud providers like AWS or GCP, learning about containerization with Docker, and studying distributed systems concepts like scalability and fault tolerance. You can also explore frameworks like Apache Spark and TensorFlow, and join online communities like Kaggle and GitHub to learn from others and stay up-to-date on the latest developments. Additionally, consider taking online courses or attending conferences to deepen your knowledge in these areas.

Practice coding has never been easier especially with AI to help. From cursor to Github copilot to chatgpt or anthropic. And they can set you up with lesson plans to hands-on practicums too.

Start simple and explore the possibilities. E.g choosing the right language. What are the benefits of Python, when to use C#? (Performance, Usability, etc.) Always keep your core problem in mind so you will not get lost.

Master the basics first ??, then dive deep into the cool stuff like neural networks, deep learning, and reinforcement learning ????. Scale your projects up with cloud tech ????. Get into complex data pipelines and resources to train and deploy top-notch AI models ???. Level up and take on bigger AI challenges! ????

Advancing a concept involves taking it to the next level. This can be achieved through two main approaches. One method is to refine and solidify the concept itself. This means iterating on it to make it more robust, addressing potential weaknesses, and ensuring its broad applicability. Alternatively, you can focus on its real-world implications. This involves exploring potential applications and identifying areas where the concept can be further developed and implemented. Ultimately, the goal is to move beyond the initial idea and create something with greater depth, usefulness, and impact.

Master the basics first because once you are fundamentally strong it will be easier when you get into complex problems/ situations. Because if you are not then at that point of time you may need to go through all the concepts and it will be difficult to make a decision.

You can try these: - Dive into model compression and optimization methods for efficient deployment. - Learn reinforcement learning concepts and their application in the cloud. - Study advanced topics in distributed data processing. - Explore federated learning approaches for decentralized training on edge devices. - You can take it further by understanding privacy-preserving techniques like differential privacy, secure multi-path computation, and homomorphic encryption. - Delve into fault tolerance, high availability, and resilience strategies for distributed AI systems. - Understand scalability, elasticity, and auto-scaling concepts, which are usually helpful.

Once basics are mastered, delving into advanced AI concepts like neural networks, deep learning, and reinforcement learning is crucial. I’ve explored how these can be scaled using cloud-based distributed systems. For example, I developed a deep learning model for image recognition, integrated into my AI SaaS application, Cloudelipute, to generate high-quality content. Studying complex data pipelines and storage solutions facilitated training and deploying sophisticated AI models. Advanced knowledge empowers tackling challenging AI projects, driving innovation and efficiency in both construction and automotive sectors.

- Leveraging advanced technologies like NVIDIA Jetson Edge devices and edge-cloud Computing, I developed a pioneering system for real-time traffic surveillance and public safety monitoring. This system harnesses the power of deep neural networks, CNNs, and generative AI for unparalleled accuracy and efficiency. - Our system achieves feats previously thought impossible. Real-time analysis, powered by intensive computation and cutting-edge machine learning algorithms and LLMs, enables immediate responses to public safety protocols. - Beyond traffic surveillance, the implications of this technology are vast. Its adaptability and scalability pave the way for broader applications and use cases in almost every kind of industry.

At the current rate of AI technology advanced, it is fundamental to understand how to connect the technology to solving real-world business problems as this ensures we don't develop in a vacuum. Reading up on AI technology case studies from popular cloud services such as AWS, Azure and GCP can be a great way to build that muscle. Some examples: 1) employing deep-learning to squeeze out the most accurate inventory timeseries forecasts for supply chain optimization, 2) leveraging computer vision models to identify item defects on a manufacturing line, 3) using generative AI to prompt call centre agents on the best way to resolve a customer issue.

Collaborate and Discuss Don't try to level up in a silo. Join online communities, forums and study groups related to cloud AI and distributed computing. Learn from others, discuss challenges and share insights. Collaboration enables you to expand your perspective beyond what any individual can achieve alone. An open exchange of ideas accelerates everyone's learning.

Identify real business challenges and propose AI-driven solutions. For instance, in marketing, optimize ad targeting using machine learning algorithms to analyze customer behavior and preferences. Implement distributed systems to handle large-scale data processing for personalized recommendations. Execute POCs to demonstrate feasibility and value. By tackling genuine problems, you'll gain hands-on experience and deepen your understanding of AI in real-world contexts.

You can try these: 1. Stay current with the latest developments, research papers, and industry trends. 2. Attend webinars and conferences on cloud AI and related topics to expand your skills. 3. Contribute to open-source projects and hackathons. 4. Experiment with new cloud services, join professional communities, and pursue relevant certifications. 5. Keep challenging yourself, never stop learning.

Nothing can compensate the trait of continuous learning. Build a habit to stay updated with he latest trend, tools and methodologies in AI, cloud computing, and distributed systems. This habit won't just keep you ahead but will also make you a valuable asset.

Enroll in study courses and trainings , keep updating by taking certification exams and update your learning on day to day basis.

Embrace change and keep yourself up to date. From my perspective, learning also includes failing, so don’t be afraid of failing. AI has the potential to change markets, so be ready for these challenges and learn from mistakes and your network iteratively.

Continuous learning is indeed important - however, I would follow or try to develop a T-shaped skill set, so start by broadening your general knowledge with standard courses on AI, cloud computing and similar areas. This should build your horizontal foundation(s). Then, once you know what you want and need, try to deepen your expertise with hands-on, rigorous courses from top-tier providers (only) that form the vertical depth of the 'T' and really work and practice 'on the edge'. In my view, this approach will ensure that you develop a well-rounded and specialised area of focus, ready for both collaboration and expert problem solving in industry or academia.

I will start with online courses to understand the basics of AI, cloud computing, and distributed systems. Then, I'll learn how to use cloud platforms like AWS, Azure, and Google Cloud to build and deploy AI solutions. For hands-on experience, I'll work on projects where I implement AI algorithms in distributed systems on these cloud platforms. For example, I might create a chatbot using machine learning algorithms and deploy it on AWS EC2 instances. And to stay connected with others in the field, I'll join online communities, forums, and meetups related to AI, cloud computing, and distributed systems.

I will try to take online courses or enroll in a certification program from platforms like Coursera, Udemy, or edX,it will give me a proper direction what I need to learn and I will Read books and research papers on the topic to deepen my understanding. And I will practice by working on real-world projects or participating in hackathons. And I Will join online communities or forums where I can discuss ideas and learn from others.

Will try to learn more; everyday ! Every drop of knowledge counts ! Learning never lets you down ! More accurate one answers are; better are the results !

Any learning is incomplete without hands-on I will create a free azure account with a new mail ID Install azure AI and cognitive services in azure. Create and provide a large language model Create search queries with the provided LLM Train the system with similar data in LLM Prepare working generative AI model with the trained system.

I believe there should be a good amount of focus on prompt engineering as it is gearing up to be in great demand for training AI Assistants and it is soon to be great importance.