Biju Krishnan’s Post

💡 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬 🚀🤖 🌟 𝐓𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐀𝐈 𝐢𝐬 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐫𝐚𝐩𝐢𝐝𝐥𝐲, 𝐚𝐧𝐝 𝐬𝐨 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐣𝐨𝐛 𝐫𝐨𝐥𝐞𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐢𝐬 𝐝𝐲𝐧𝐚𝐦𝐢𝐜 𝐟𝐢𝐞𝐥𝐝. 𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐠𝐥𝐢𝐦𝐩𝐬𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈, 𝐡𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐤𝐞𝐲 𝐚𝐫𝐞𝐚𝐬 𝐚𝐧𝐝 𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐣𝐨𝐛 𝐭𝐢𝐭𝐥𝐞𝐬. 🤖 𝐀𝐈 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 💡 Economic and Strategic Implications 💡Governance and Ethical AI 💡Infrastructure and Technological Shifts 𝐊𝐞𝐲 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬: 🔹 𝐀𝐈 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 💡AI Architect 💡AI Infrastructure Engineer 💡Model Hub Administrator 💡AI Data Engineer 🔹𝐀𝐈 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 💡Prompt Engineer 💡AI Software Engineer 💡MLOps Engineer 💡Vector Database Developer/Manager 💡AI Model Trainer 🔹 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐄𝐭𝐡𝐢𝐜𝐬 💡AI Ethics Officer 💡AI Governance Specialist 💡AI Bias and Fairness Analyst 🔹 𝐀𝐈 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 💡AI Business Analyst 💡AI Product Manager 💡AI Solutions Consultant 💡AI Change Management Specialist 🔹 𝐀𝐈 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐚𝐧𝐝 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 💡AI User Experience Designer 💡AI Training Developer 💡AI Support Specialist 💡AI Documentation Writer Generative AI is not just transforming technology; it's revolutionizing the job market! Get ready to embrace these exciting opportunities and shape the future of AI. 🌐💡 #AI #GenerativeAI #FutureOfWork #TechJobs #Innovation #AIcareers #DigitalTransformation

🔍 The Evolution of the Data Scientist Role 🔍 The role of the Data Scientist has evolved significantly, becoming more specialized to reflect the diverse nature of data work today. What used to be a catch-all role has now branched into several focused positions: 🌐 Data Scientists: Versatile professionals who bridge multiple data functions, offering a broad range of skills across data analysis, modelling, and strategy. The term "Data Scientist" is now an umbrella term in industry. The first break-away role was the Data Engineer. 🤖 Machine Learning Engineers: Experts in designing and deploying machine learning models, getting hands-on with the more complex engineering elements in data 🔧 MLOps Professionals: Specialists in automating and streamlining the deployment and monitoring of machine learning models, ensuring seamless operation in production 📊 Model-Ops Experts: Managers of the entire lifecycle of analytical models, from development to maintenance and governance. With increased demand and performance expectations, this is a new role we are likely to see develop in demand 💡 AI Engineers: Innovators who build and integrate AI systems, pushing the boundaries of what machines can achieve 🚀 Data Product Managers: Visionaries who align data initiatives with business goals, driving value and ensuring data products meet user needs It's exciting to see this evolution in the field! The specialisations allow for more targeted hires, and enables people to spend more time doing what they do best. More opportunity for value! Cognify Search

Are you Building your AI team? Check out the roles your enterprise needs (as suggested by the co-founders (S. Koushik Debroy & Ashish Singh) at TheCodeWork See, to be honest, Artificial intelligence (AI) isn’t just about frameworks — it’s also about people. As a business, you can have a successful AI journey only when you have executive leadership support and the right talent in key AI roles. Allow us to walk you through the key roles here: > Executive sponsor Enterprises that have successfully implemented AI have strong executive leadership support for the new technology. A C-suite sponsor can take an active role in ensuring that AI projects are aligned with the strategy of your company. > Systems architect Sometimes referred to as a cognitive solutions architect, the person in this role is responsible for operationalizing the entire suite of machine learning and deep learning models within the IT framework and systems at hand. > Data engineer Ensuring data quality means the data engineer role is vital to your AI success. They are responsible for defining and implementing the integration of data into the overall AI architecture. Skills include experience with data platforms including SQL and NoSQL databases. > Data scientist At its heart, data science is focused on exploring data to extract actionable information for making business decisions. The job calls for math aptitude and coding skills as well as critical thinking and problem-solving abilities. > DevOps engineer DevOps engineers work with architects, developers, data engineers, and data scientists to ensure solutions are rolled out and managed. They are professionals with experience in both application development and deployment. > Business analyst Business analysts use results from the data science models and act as “translators” between the business users and the machine learning team. 💡 Overview Bringing in experienced experts to work with employees on delivering the first use cases of AI is recommended. Reach out to us for a free consultation call on this - [https://lnkd.in/gutNhqyz] #ai #artificialintelligence #softwaredevelopment #devops

This is a great example of the real-world difference between #AI hype and AI reality. What gets automated are TASKS, not JOBS. The potential for AI's impact on productivity is huge - but we need to remember to not confuse tasks with jobs.

If you want to switch your career to AI, there is a massive opportunity right now. Here are some paths: 1. Product Manager -> AI Product Manager -> GenAI Engineer If you are a PM, the fist step is to familiarize yourself with AI. Ask yourself how AI can be used to solve business problems, how to enable an AI product strategy, etc. Then you can start implementing AI solutions via low-code/no-code tools. 2. Tech Sales -> Prompt Engineer -> GenAI Engineer If you are in sales, you already have a good understanding of the customer's needs. Get familiar with the different prompting techniques to extract the most value from current GenAI solutions. Then, you can implement AI solutions via low-code/no-code tools. 3. Software Engineer -> Machine Learning Engineer -> Data Scientist If you are a programmer, the shortest path to AI is as ML Engineer. Learn how to deploy a model, build APIs, reduce latency, create MLOps solutions, etc. Then you can start understanding machine learning and creating your own models. Is this useful? ____ 🔔 Press the notification button to get content like this. 📢 Repost if this content is useful. ✏ Comment with your views.

AI solutions in just 30 days? It's not a myth! 🚀 But how can you achieve this seemingly impossible feat? The answer lies in today's advanced AI models, tools, services, and libraries, which make creating a Proof of Concept (PoC) in a month entirely feasible. So, what exactly is an AI Proof of Concept? It's a released product that focuses on the main business goal with minimal features, ready for early adopters to test. Here are some examples of what a PoC could look like: 1️⃣ An AI chatbot or agent that works with one knowledge base, a few custom prompts, and tools, and can operate on various channels. 2️⃣ An LLM integration for a specific business function, like an API that takes human commands, performs actions, and displays results, such as a bot that responds to voice commands and retrieves data from a database or creates new entities like tasks or notes. 3️⃣ A document analysis and structure tool that takes inputs like CVs and generates a JSON with essential data from the CV, comparing it with the job description. To create a PoC within a month, you'll need a team of: ➜ AI Team Lead ➜ Business Analyst ➜ Functional Analyst ➜ AI Engineer ➜ Software Developer (depending on the task) ➜ DevOps (depending on the task) ➜ QA The secret sauce to achieving this tight timeline lies in: ✅ Using available LLMs with instructions for the PoC ✅ Having all the main training data readily available ✅ Clearly understanding the process from a business perspective ✅ Maximizing the use of low-code solutions like VoiceFlow or Flowise, or ready-to-go solutions based on LangChain for the PoC By leveraging these strategies, you can harness the power of AI and create a custom solution in just one month. Have you built an AI PoC in a short timeframe? Share your experience and insights in the comments below!

Are you considering a career in Data or Machine Learning (ML)? Are you confused with various data roles and what they do? Here’s a detailed breakdown of critical roles and their associated responsibilities: 🔘 Data Engineer: Tailored for Data Enthusiasts 1. Data Ingestion: Acquire proficiency in data handling techniques. 2. Data Validation: Master the art of data quality assurance. 3. Data Cleansing: Learn advanced data cleaning methodologies. 4. Data Standardisation: Grasp the principles of data formatting. 5. Data Curation: Efficiently organise and manage datasets. 🔘 Data Scientist: Suited for Analytical Minds 6. Feature Extraction: Hone your skills in identifying data patterns. 7. Feature Selection: Master techniques for efficient feature selection. 8. Model Exploration: Dive into the realm of model selection methodologies. 🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts 9. Coding Proficiency: Develop robust programming skills. 10. Model Training: Understand the intricacies of model training. 11. Model Validation: Explore various model validation techniques. 12. Model Evaluation: Master the art of evaluating model performance. 13. Model Refinement: Refine and improve candidate models. 14. Model Selection: Learn to choose the most suitable model for a given task. 🔘 ML Engineer: Tailored for Deployment Enthusiasts 15. Model Packaging: Acquire knowledge of essential packaging techniques. 16. Model Registration: Master the process of model tracking and registration. 17. Model Containerisation: Understand the principles of containerisation. 18. Model Deployment: Explore strategies for effective model deployment. These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly. If you found the post helpful, could you share it with your network? -------------------     👋🏻 Hi! I’m Deepak Bhardwaj. I share my knowledge and experience in Data, AI, and Architecture.    #DataOps #MLOps #CareerInData #MachineLearning #DataScience #AI#ML #BestPractices

Good insight about carriers in ML and Data Science domain

"Passionate about driving innovation in technology to empower businesses and enhance user experiences. With a keen eye for emerging trends, I thrive on crafting solutions that bridge the gap between ideas and execution. Committed to continuous learning, I embrace challenges as opportunities for growth, constantly seeking to expand my skill set and push boundaries. As a dedicated team player, I foster collaboration and communication to deliver impactful results. Let's connect and explore how we can revolutionize the digital landscape together! #Innovation #Technology #BusinessEmpowerment #UserExperience #ContinuousLearning #ProblemSolving #TeamPlayer #Collaboration #DigitalTransformation #ImpactfulResults"

Are you considering a career in Data or Machine Learning (ML)? Do you need help understanding various engineering roles and what they do? Here’s a detailed breakdown of critical roles and their associated responsibilities: 🔘 Data Engineer: Tailored for Data Enthusiasts 1. Data Ingestion: Acquire proficiency in data handling techniques. 2. Data Validation: Master the art of data quality assurance. 3. Data Cleansing: Learn advanced data cleaning methodologies. 4. Data Standardisation: Grasp the principles of data formatting. 5. Data Curation: Efficiently organise and manage datasets. 🔘 Data Scientist: Suited for Analytical Minds 6. Feature Extraction: Hone your skills in identifying data patterns. 7. Feature Selection: Master techniques for efficient feature selection. 8. Model Exploration: Dive into the realm of model selection methodologies. 🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts 9. Coding Proficiency: Develop robust programming skills. 10. Model Training: Understand the intricacies of model training. 11. Model Validation: Explore various model validation techniques. 12. Model Evaluation: Master the art of evaluating model performance. 13. Model Refinement: Refine and improve candidate models. 14. Model Selection: Learn to choose the most suitable model for a given task. 🔘 ML Engineer: Tailored for Deployment Enthusiasts 15. Model Packaging: Acquire knowledge of essential packaging techniques. 16. Model Registration: Master the process of model tracking and registration. 17. Model Containerisation: Understand the principles of containerisation. 18. Model Deployment: Explore strategies for effective model deployment. These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly. If you found the post helpful, could you share it with your network? -------------------     👋🏻 Hi! I’m Deepak Bhardwaj. I share my knowledge and experience in Data, AI, and Architecture.    #DataOps #MLOps #CareerInData #MachineLearning #DataScience #AI#ML #BestPractices