Digital Product School by UnternehmerTUM’s Post

#AI meets #softwareengineering: What does that mean for us and our customers? Answers have been explored by our AI4U team. CTO Alexander Hofmann: "#GenerativeAI will drastically change the way we develop software. Today, we are already seeing huge efficiency gains through AI in digital design, development and testing. AI brings more speed. That is why we have launched the company-wide AI4U initiative to align our software engineering methods with AI across the company and to explore where AI can bring benefits to our customers. AI4U is Dr. Marco Pötke, Ida Buchwald, Benjamin Hofmann, Fabian Hertwig and Bastian de Groot. They combine expertise from the fields of data science, software development and digital design. They are already supporting our clients today in various projects: from AI assisted prototyping to AI pair programming and AI driven knowledge management to AI assisted code migration.   Why is code migration so challenging so far?   Benny: Migrating code from one framework to another is a complex challenge. Each framework has its unique architecture, conventions, and dependencies, making the transition more than just a simple code translation. Manual migration is not only time-consuming but also error-prone, requiring deep expertise in both the source and target frameworks.   What can #GenerativeAI help to improve?   Bastian: One of the perfect use cases for Large Language Models (LLMs) is in tasks that are compley yet highly repetitive. Code migration fits this cheme. It cannot simply be automated programmatically, but the same migration principles apply repeatedly.   What have you learned from the migration of Java code from Payara to the Quarkus framework?   Bastian: Migrating from Payara to Quarkus is an ideal starting point. Why? Because most of it can be migrated in place. This means during the migration, there’s no architectural change. We opted to do the migration file by file. AI-assistance shines when given clear instructions. Think of it as guiding a sharp-witted junior developer. The clearer and more precise you are, the better the outcome.   How does AI assisted code migration change your work as software engineers?   Benny: Our AI-assisted approach fundamentally changes the human role in code migrations. Instead of spending most of the time on repetitive, error-prone and manual tasks, humans will now focus on proper planning and quality assessment. This reorientation doesn't just reduce errors; it elevates the entire process, facilitating critical thinking and strategic decision-making.   In essence, while the role of human expertise is evolving in the face of AI integration, it remains essential. The synergy of both, Human Expertise and AI, is key.   What is your experience with GenAI in software engineering? We look forward to hearing from you!    

🌟Revolutionizing ML Experiment Management with ModelDB 🔍 ModelDB by VertaAI offers an open-source solution for machine learning model versioning, metadata tracking, and experiment management. It streamlines the ML lifecycle, ensuring reproducibility, efficient management, and comprehensive performance monitoring. 🔍 Key Insights: 🔹  Comprehensive Model Management: ModelDB allows versioning of ML models, tracking their code, data, configuration, and environment throughout their lifecycle, from development to deployment and monitoring. 🔹 Integration and Flexibility: It supports integration with Docker, Kubernetes, TensorFlow, and PyTorch, providing flexibility and scalability for various ML frameworks and environments. 🔹 Enhanced Experiment Tracking: Features include logging metrics, artifacts, tags, and user information, with beautiful dashboards for performance reporting and git-like operations on models. 🌐 Real-World Applications: 🔹 Healthcare: Track and manage predictive models for patient diagnostics and treatment recommendations. 🔹 Finance: Ensure reproducibility and compliance in algorithmic trading models. 🔹 Retail: Optimize recommendation engines and inventory management systems with efficient model versioning. 👉 https://lnkd.in/dPtdM4dr Let's discuss: 🔹  How can ModelDB enhance your ML workflows? 🔹 What challenges have you faced in managing ML experiments and model versioning? #AI #MachineLearning #TechInnovation #DigitalTransformation #ModelDB #ExperimentManagement #DataScience #TechTrends

MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01_Z0Hh0

‏{وَكَان فَضْلُ اللهِ عَلَيْكَ عَظِيمًا} اللهم لكَ الحمد حتى ترضى ولكَ الحمدُ إذا رضيت ولكَ الحمدُ بعد الرضى، اللهم لكَ الحمدُ حمدًا كثيرًا طيبًا مُباركًا فيه. On Thursday, May 30, 2024, I discussed my graduation project called "نبض" 🎓. The "نبض" comes to life based on my fulfillment of the requirements for the award of a Bachelor's degree in Software Engineering and the learning outcomes from the minor program in Artificial Intelligence at UPM. 💡Addressed Problem: Following international/regional standards is a crucial aspect when they have an impact on people's lives. However, the current procedures to ensure compliance often involve sending surveys or dispatching individuals to evaluate these places, which unfortunately consumes a lot of time, effort, and cost. This has motivated me and my team to introduce smart evaluation systems to automate the evaluation process using the power of artificial intelligence (AI) under a system called "نبض".  🎯Project Scope: In this version of "نبض", it serves the coffee and restaurant sectors, primarily focusing on two aspects: ensuring employees comply with standards by following a uniform dress code and maintaining customer satisfaction by measuring customer emotions during their visits. 🏆Golden Rule: Our proposed solution is flexible and transferable; it only requires a  device to operate the system, along with the cameras that will be integrated, which are already available in any commercial store. Therefore, there is no need to create or develop anything new; we can simply utilize existing resources to create a better customer environment and a place of higher quality. So, following standards and policies is just a click away with "نبض". ⚒ Tools and Technologies: The project toolkit contains web technologies with React library as the front-end technology, while RESTful and WebSocket are the two main architectural styles for the project's APIs. Regarding the AI part, Roboflow was used as the inference model. In terms of databases, the project utilizes different technologies such as Microsoft Azure SQL DB, and SQLite.  👥 Team Members: I was lucky to work on this project with Samah Channa, under the leadership of two great leaders, Samah Shamma and Sana Shamma, with the special guidance and support of Dr. Fazilah Haron. #UPM #Madinah #GraduationProject #SWE #AI #ComputerVision

It's amazing to see how a simple idea conversation with Jorge Torres, CEO of MindsDB, turned into a Docker Extension. This journey from a casual conversation to a full-fledged Docker extension is a testament to the power of collaboration and seeing ideas through. Huge thanks to Jorge and the entire MindsDB team for their support in making this happen! Building an AI-powered application requires significant resources, including qualified professionals, cost, and time. Prominent obstacles include: - Bringing (real-time) data to AI models through data pipelines is complex and requires constant maintenance. - Testing different AI/ML frameworks requires dedicated setups. Customizing AI with dynamic data and making the AI system improve itself automatically sounds like a major undertaking. These difficulties make AI systems scarcely attainable for small and large enterprises alike. The MindsDB platform, however, helps solve these challenges, and it’s now available in the Extensions Marketplace of Docker Desktop. Thanks to MindsDB team for their collaborative effort in authoring this blog post. https://lnkd.in/dU9SdzbC

𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐂𝐨𝐝𝐞 𝐰𝐢𝐭𝐡 𝐆𝐞𝐧𝐀𝐈? 🚀 Discover How 𝐀𝐖𝐒 𝐂𝐨𝐝𝐞𝐖𝐡𝐢𝐬𝐩𝐞𝐫𝐞𝐫 Can Transform Your Workflow! Generative AI is the new kid on the block everyone’s hyping up, with professionals eagerly pursuing learning paths and certifications. ✴ Yet, for developers, DevOps, and Cloud engineers, the real challenge lies in pinpointing the starting line for Generative AI and seamlessly integrating it into our daily workflows. Within the Software Development Life Cycle (SDLC), it falls upon developers and DevOps engineers to guarantee the quality and efficiency of application code. ✴ A notable hurdle in this process is the crafting of Unit Test cases during development — a task that looms large for developers due to its time-intensive nature and the pressure to meet deployment deadlines. Upon contemplating the practical application of Generative AI, I’ve identified a compelling use case for its initiation. 🎯 𝑬𝒏𝒕𝒆𝒓 𝑨𝑾𝑺 𝑪𝒍𝒐𝒖𝒅’𝒔 𝑪𝒐𝒅𝒆𝑾𝒉𝒊𝒔𝒑𝒆𝒓𝒆𝒓: A solution designed to expedite application delivery by automating the creation of 𝐔𝐧𝐢𝐭 𝐓𝐞𝐬𝐭 𝐜𝐚𝐬𝐞𝐬. 🛠 Developers can effortlessly integrate this plugin into their preferred Integrated Development Environments (IDEs), including AWS Lambda Console, AWS Cloud9, VS Code, IntelliJ IDEA, JupyterLab, PyCharm, and Amazon SageMaker Studio. 🤝 Moreover, AWS CodeWhisperer extends its support to over 15 programming languages, encompassing Python, Java, JavaScript, TypeScript, C#, and others. Utilizing this Generative AI tool, we can achieve, ✅ a staggering 90% enhancement in security, ✅ accelerate productivity by 57% , and ✅ increase the likelihood of project success by over 27%. The case study below serves as a guide for developers eager to incorporate Generative AI into the SDLC. https://lnkd.in/grxhQr8Z ------------------------------------------------------------------ 📩 DM | Follow Thiruvanai T.S. for more content. I believe the fastest way to learn is to share! How about you? Thank you. ------------------------------------------------------------------ #generativeai #innovation #developercommunity #devops #AWS #cloudcomputing

🚀 Calling all #JavaDevelopers! Are you looking to level up your coding game with the help of #AI tools? Look no further! Here are some of the top AI tools that can assist you in automating coding and test cases: 🔹 CodeGuru: This AI-powered tool from #AWS analyzes your code and provides intelligent recommendations for improvements, saving you time and effort. 🔹 Kite: With advanced code completion and error detection, this AI assistant makes coding faster and more efficient. 🔹 Testim: Say goodbye to manual testing with this AI-based automated testing tool, which reduces testing time and increases accuracy. 🔹 Codota: Get instant code suggestions and autocomplete with this AI tool that uses machine learning algorithms to analyze millions of code snippets. 🔹 JRebel: Improve your coding experience with this AI-powered tool that eliminates the need for manual code re-deployment, saving you time and increasing productivity. 🔹 DeepCode: This AI-based code review tool helps you catch bugs and security issues early on, ensuring high-quality code and faster development. With these AI tools, #JavaDevelopers can streamline their coding process, increase efficiency, and deliver high-quality code. Give them a try and take your coding skills to the next level! 💻 #AIforJava #Automation #TestCases #CodeOptimization

MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01YFPlz0

Home kellton Services Platforms & Products Industries Insights Careers About Search HomeAll InsightsBlogs AI in action: 5 ways it’s improving Software Development Data Engineering & AI AI/ML May 03 , 2023 Posted By: Silin Na linkedin Banner - AI in action: 5 ways it’s improving software development Other recent blogs Top Machine Learning Algorithms The ultimate compilation: 7 top Machine Learning algorithms May 31 , 2024 ‎What is a SOC Implementing Security Operations Center (SOC): Strategies for success May 23 , 2024 Software development Thumbnail Infrastructure as Code (IaC): The complete beginner’s guide explained here May 22 , 2024 AI is impacting every industry out there. And software development is no exception - From how project requirements are gathered to how an application is designed, built, deployed, and managed, every stage in the software development lifecycle is rapidly undergoing changes. There is even a report indicating that generative AI could lead to a 10-fold increase in coding productivity. To build digital products at the speed of business and craft memorable customer experiences, it is vital for organizations to keep tabs on the increasing usage of intelligent technologies in the field of Software Development. Here are some of the best ways you can leverage AI/ML tools and technology to drive innovation and gain a competitive edge. The rise of AI in Software Development: 5 ways it's transforming the industry Rise of AI in software development Precise estimates: When a project is not delivered on time and within budget, it leads to increased customer dissatisfaction and loss of repeat business. And it’s not just about the customer and business revenue; failing to meet the expectations of the customer also hurts the morale of the development team, and the sentiment is likely to impact their performance in future projects. However, thanks to modern artificial intelligence software, project managers and software development teams are now able to make more informed decisions about project timelines and budgets. This capability leads to more accurate estimates, better project planning, and a dramatic improvement in customer satisfaction. To develop the capabilities in terms of predicting estimates that are closer to reality, teams can train AI/ML models on historical data related to projects that were delivered in the past. By analyzing data related to team sizes, project timelines, and task complexity, the models can generate highly accurate estimates for new projects. Efficient prototyping with artificial intelligence: Delay in demonstrating proposed solutions with prototyping solutions like an MVP (minimum viable product) makes clients lose their trust and interest in signing up with a service vendor, which can lead to loss of business or significant delays in project kick-offs and contract signings. It is because building a prototype for a client can be time-consuming and complex.