ANALYST Project’s Post

🚀 The Convergence of Engineering and Data in the Age of AI 🚀 As we witness AI’s transformative impact across industries, it’s clear that the future of software development is inseparable from the world of data. No longer can we consider engineering and data as distinct entities; they are becoming one and the same. In this new era, every major software innovation is developed with AI in mind, and conversely, every significant AI advancement is driven by real enterprise data. This symbiosis is redefining what it means to be an engineer in the tech landscape. For engineers looking to pioneer the next wave of AI products, it’s crucial to develop a keen understanding of data. The ability to work seamlessly with data sets, extract insights, and build models that deliver continuous value is what will set apart the successful AI innovations from the rest. Let’s embrace this shift and prepare for a future where engineering and data teams are the twin engines propelling us forward. #AI #DataScience #SoftwareDevelopment #Innovation #Engineering

Is #AdTech meeting its potential in today's digital ecosystem? According to recent data by Statista, 87% of professionals surveyed on the impact of AI in advertising believe that the technology will transform the industry in the next five years. To harness the impact of these technologies, #AdTech and #MarTech vendors should commit to bringing skilled talent to their teams. In our latest blog post, we explore the skills and responsibilities sought after in skilled AI developers. Our experts share their insights on what an ideal skillset of an AI engineer looks like. We highlight the importance of a mix of technical and non-technical skills in AI development. Knowledge of programming languages, frameworks, and database architectures is vital for building accurate ML models and scalable tools. However, if tech and product teams are not aligned, the project will fail to capture business objectives and drive user acquisition. Learn more about the skills and responsibilities of AI developers in our blog post by Vova Kyrychenko, the CTO at #Xenoss! ⬇️Find the link in the comments! #aiengineering #ai #programmatic #generativeai #machinelearning 

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗖𝗼𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀??? We can all agree that AI is transforming software engineering! The question is....will the future revolve around managing AI coding agents? S͟o͟m͟e͟ ͟i͟t͟e͟m͟s͟ ͟t͟o͟ ͟c͟o͟n͟s͟i͟d͟e͟r͟: ☑️𝗔𝗜-𝗖𝗼𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀: Handling repetitive tasks, freeing us for complex challenges. ☑️𝗦𝗸𝗶𝗹𝗹 𝗦𝗵𝗶𝗳𝘁: Engineers will focus on AI management and integration. ☑️𝗕𝗼𝗼𝘀𝘁𝗲𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Faster projects and higher productivity. ☑️𝗡𝗲𝘄 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: Addressing AI ethics, reliability, and bias. At Sonatafy Technology, we're leading this revolution. Embrace the change, upskill, and innovate. Are you ready for the AI-driven future? Share your thoughts! #AI #SoftwareEngineering #Innovation #TechLeadership #FutureOfWork

🚀 Exciting Developments in Engineering: How AI and ML Can Help Engineer Solutions 🚀 In today's rapidly evolving world, engineering challenges are becoming increasingly complex, requiring innovative solutions to address them effectively. That's where Artificial Intelligence (AI) and Machine Learning (ML) come into play! These cutting-edge technologies are revolutionizing the way engineers approach problem-solving and paving the way for groundbreaking advancements across various domains. 🔍 Enhanced Problem-Solving: AI and ML algorithms can analyze vast amounts of data to identify patterns and insights that may not be apparent to human engineers. By leveraging these insights, engineers can develop more efficient and effective solutions to complex engineering problems. ⚙️ Optimized Design Processes: From optimizing product designs to streamlining manufacturing processes, AI and ML techniques offer invaluable support to engineers. These technologies enable engineers to explore a wide range of design possibilities, identify optimal solutions, and accelerate the development cycle. 🛠️ Predictive Maintenance: By analyzing sensor data and equipment performance metrics, AI and ML algorithms can predict potential failures before they occur. This proactive approach to maintenance not only minimizes downtime but also extends the lifespan of machinery, leading to significant cost savings for businesses. 🌐 Global Collaboration and Innovation: AI and ML technologies facilitate collaboration among engineers worldwide by enabling real-time data sharing and collaboration on engineering projects. This global collaboration fosters innovation, allowing engineers to tap into diverse perspectives and expertise to solve complex challenges. 💡 Continuous Learning and Improvement: One of the most exciting aspects of AI and ML is their ability to learn and adapt over time. As engineers gather more data and insights, AI algorithms can refine their models and algorithms, leading to continuous improvements in engineering solutions. In conclusion, the integration of AI and ML technologies into the engineering workflow is unlocking new possibilities and reshaping the future of engineering. By harnessing the power of AI and ML, engineers can tackle challenges with greater efficiency, innovation, and effectiveness, driving progress and shaping a better tomorrow for all. Let's continue to embrace these transformative technologies and push the boundaries of what's possible in engineering! 💡✨ #AI #ML #Engineering #Innovation #FutureTech #Energy #powersystem #datascience

What We Learned from a Year of Building with LLMs 1. Professionals should focus on operational details, not just strategy and tactics. Details matter! 🔍 2. Data quality is crucial for ML system performance. Strictly review inputs and outputs! 📊 3. Consider performance, cost, and compatibility when selecting and migrating models. Choose wisely! 🤖💡 4. Product design should be user-centric with designers involved from the start. User first! 🎨🙌 5. Design user-friendly feedback systems to improve models. User input enhances experience! 📲👍 6. AI engineers are key in rapid prototyping and iteration, but data and ML engineers are also vital! 👩💻🛠️ 7. Encourage team experiments and education to enhance understanding of new AI tech. Learning never stops! 📚🚀 8. Prioritize problem-solving processes over tools when facing tech challenges. Processes drive efficiency! 🔧🧩 9. Adjust risk tolerance based on product use cases and target audience. Manage risk wisely! ⚖️🌐 10. AI product development is a continuous learning and adaptation process, requiring constant iteration and optimization. Keep progressing! 🔄✨ #Operations #Details #DataQuality #MachineLearning #ModelSelection #AI #ProductDesign #UserExperience #HITL #UserFeedback #Teamwork #AIEngineering #Experimentation #AITech #ProcessFirst #TechChallenges #RiskManagement #AIProducts #ContinuousLearning #AIDevelopment

🔴 80% of AI projects fail – this is the estimate featured by Harvard Business Review. This is twice the failure rate for non-AI corporate IT projects ☝️ What is the root cause of this problem? To understand why some AI projects fail and others succeed, we contacted Vitalii Diravka, Delivery Manager at #Xenoss, who helped bring multiple #AdTech and #MarTech machine learning projects to the market. Vitalii emphasized the importance of the following factors: 🔸Building a team of professionals who can match the tech to the use case 🔸Meticulous approach to data gathering and processing 🔸Prioritizing user experience 🔸Continuous testing and monitoring To learn more about how building a high-performing AI team increases the odds of success for an AI project, check out our new blog post written by Valery Sverdlik, Delivery Director at Xenoss. We take a deep dive into the process and best practices for hiring AI engineers there. 🖇See the link in the comments! #artificialintelligence #machinelearning #aiengineering #hireengineers #naturallanguageprocessing #aiinnovation

𝗧𝗵𝗶𝘀 𝗶𝗺𝗮𝗴𝗲 𝗻𝗲𝗲𝗱𝘀 𝗻𝗼 𝗲𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻. 🛠️ 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 🔑 𝘂𝗻𝘀𝘂𝗻𝗴 𝗵𝗲𝗿𝗼𝗲𝘀 𝗶𝗻 𝘁𝗼𝗱𝗮𝘆'𝘀 AI era. They work tirelessly behind the scenes to build and maintain the 🏗️ infrastructure that powers our data ecosystems. While AI engineers, PM, data analysts and scientists often take center stage, it's the Data Engineers who lay the groundwork, ensuring data is accurate, accessible, and well-structured. Investing in your data infrastructure's foundation is 🔥 essential for AI readiness. Strong data pipelines, efficient storage, and proper governance are key to harnessing the power of AI and advanced analytics. Without a solid data foundation, even the most 💪 sophisticated AI models may underperform. Let's recognize and support the vital work of Data Engineers. Your future AI success depends on it. 🌟 #dataengineering #AI

Self-education: The Key to Continuous Improvement and Transformation. 📚✨ For #IndustrialEngineers, there are significant opportunities to play a key role in the adoption and implementation of AI/LLMs in industrial settings. By developing skills in data science, machine learning, and system design, we can bridge the gap between traditional practices and emerging technologies. Industrial Engineers who blend their systems thinking, domain expertise, and process optimization skills with an understanding of AI/ML capabilities will be well-prepared for the future. It’s clear that fostering closer integration between Industrial Engineering and AI/ML disciplines is crucial as we move forward. Let's commit to ongoing learning and collaboration to drive meaningful progress in our field. #IndustrialEngineering #AIIntegration #FutureReady Recent Industrial Engineering Content: ✅Rising Gaps between Process Improvement and Tech/AI: https://lnkd.in/gcT2HSu4 ✅Mastering Problem-Solving: https://lnkd.in/gfXKFb2E ✅Understanding CX: https://lnkd.in/gpd43W7D ✅Different Career Paths: https://lnkd.in/gtKFZxFk ✅Evolution of Business Process Improvement: https://lnkd.in/gvCjeYSt ✅Industrial Engineering From A to Z: https://lnkd.in/g4bsKydq ✅2024 Learning Schedule: https://lnkd.in/gRi-uP8N ✅10 Common Types of Errors: https://lnkd.in/g54yu5ks ✅Levels of Mistake Proofing: https://lnkd.in/gRvTeRMd Follow #konnectkonsult and Korina “Kon” Mercado “Kon” Mercado and stay informed about valuable Continuous Improvement tips! #ProcessOptimization #LeanThinking #LLM #PromptEngineering #QualityImprovement #AI #ML #OperationalExcellence #QualityManagement #KaizenPrinciples #LeanSixSigma #LifeLongLearning #IndustrialEngineering #AgileSigma

Devin AI: Your First AI Software Engineer - A Partner to Engineers, Not a Replacement Devin AI, equipped with advanced artificial intelligence, raises the question of its potential to replace engineers. However, while it excels at automating tasks and analyzing data, it is unlikely to entirely supplant the skills and expertise of engineers. Instead, it is set to collaborate with engineers, streamlining technical tasks and freeing up time for higher-level design and innovation. Engineers bring essential problem-solving skills, creativity, and ethical judgment that machines cannot replicate. Ultimately, Devin AI is poised to augment engineers' abilities and enhance the engineering profession rather than replace it. What are your insights on the topic? #ai #improvenotreplace #peformancebooster #smartcactus #automation #softwaredevelopment #cloudcomputing #digitalhealth