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Deciding on MLOps: Is Your Team at the Right Stage? Are you at the crossroads of tech innovation, wondering if it’s the right time to integrate an MLOps platform into your operations? Whether you're part of a dynamic startup or spearheading data science efforts in an established company, navigating the complexities of MLOps can be transformative yet daunting. I’ve distilled my firsthand experiences and key learnings into a comprehensive blog post that explores the optimal timing and strategic considerations for adopting an MLOps platform. what are your insights or experiences with integrating MLOps into your operational workflow? #MLOps #MachineLearning #AI #DataScience #TechnologyLeadership

Today is AI appreciation day, a good occasion to celebrate advancements in the industry and its contributions across different industries. When I look at how AI/ML shapes the way that people interact or organisations work, I am often in awe of it. Yet, I often hear how challenging it is to get started, so here are some thoughts to help you out. ???? Start with a use case; it will give you a sense of achievement and a clear goal. Spend time scoping the project and defining its goals and challenges. Interacting with and aligning stakeholders can lead to difficult conversations, but it will define (or break) the project's success. ?? Experiment first, but keep in mind the big picture. I often see projects stuck in the initial phase without any plan to move further. Experimentation can give the initial answers, but to get ROI, you should always aim to triage projects that bring no value and run them in production when ready. Bear in mind the need to build pipelines, security and compliance concerns, and the need to always check for data drift or bias. ? Get your initial environment ready as soon as possible, and build stable architecture to run in production. There are many jokes about how much data scientists spend getting their initial ML setup. Using open source tools will lower the barrier entry, and then using tools such as the Data Science Stack will enable you to get faster. Yet, when you are ready to move your initiatives to production, ensure you consider an MLOps platform where experiments are tracked, artefacts are stored and it is portable, so you are not constrained on the underneath environment. As part of the journey, and likely my way of showing appreciation, I share some of my insight. I saw the industry changing at a pace that is often overwhelming, but it remains, by far, the most exciting area that I could have been working on. Follow our AI team from?Canonical?and me?in the upcoming months for more insight about #opensource #mlops and #genai. Happy AI Appreciation Day everyone!??

?? Harnessing ML Ops in Operations for a Competitive Edge ?? In today’s fast-paced business landscape, the integration of Machine Learning Operations (ML Ops) into operational workflows is not just a trend—it's a necessity. As organizations strive to enhance efficiency, minimize risks, and drive informed decision-making, ML Ops offers transformative tools and techniques that can redefine our operational capabilities. ?? What are the Key Components of ML Ops? Collaboration and Communication: Foster a culture where data scientists, operations teams, and IT work hand-in-hand to streamline model deployment. Continuous Integration/Continuous Deployment (CI/CD): Automate the deployment of models to ensure they are always performing at their best and can be iterated upon quickly. Monitoring and Maintenance: Implement tools like MLflow and Kubeflow to keep an eye on model performance, identify drift, and make necessary adjustments without significant downtime. Version Control: Utilize DVC (Data Version Control) to manage datasets and model versions effectively, ensuring transparency and reproducibility. ?? Latest Tools Making Waves in ML Ops: Weights & Biases: Simplifies the process of tracking experiments, visualizing results, and sharing findings with teams. TensorFlow Extended (TFX): An end-to-end platform for deploying production ML pipelines. Seldon: A powerful framework for managing, deploying, and scaling machine learning models in Kubernetes. DataRobot: This platform facilitates automated machine learning, making it easier for operational teams to generate actionable insights quickly. ?? Realizing the Potential:Incorporating these ML Ops practices and tools into our operations not only enhances our efficiency but also creates a culture of innovation. As we leverage data to its fullest potential, the possibilities are endless! Let’s embrace the future of operations together! ?? What tools or techniques have you found most beneficial in your ML Ops journey? Share your thoughts below! ?? #MLOps #MachineLearning #Operations #AI #DataScience #Innovation #TechTrends #ContinuousIntegration

BigPanda Unveils Innovative Generative AI Solution Tailored for ITOps #AI #artificialintelligence #Biggy #BigPanda #Efficiency #generative #IT #llm #machinelearning #operations #Software

AI-driven observability tools haven’t lived up to the hype. Here’s why: Lack of Data for Rare Failures: Supervised learning struggles without enough examples of rare system failures. Complex Data, Complex Problems: Distributed systems generate diverse data, and feature engineering often fails to capture the full picture. Trust Issues: Black-box models don’t provide the transparency DevOps teams need to trust AI decisions. Constantly Changing Systems: Continuous deployment causes concept drift, making AI models outdated unless they’re retrained frequently. These challenges have kept AI observability tools from reaching their full potential. What’s the future of observability? #Observability #AI #AITriage #TechInnovation #MachineLearning #TechInnovation #StartupLife

?? Event Recap: Generative AI & ML Tech Stack World Series ?? I had the incredible opportunity to attend the "Generative AI & ML Tech Stack World Series" event organized by our company VTS Enterprises India Private Limited on June 8th, 2024. It was a truly enlightening experience, filled with valuable insights and forward-thinking perspectives from some of the top IT personnel in the industry. Here are some highlights from the event: ?? Advancements in Artificial Intelligence: Our speakers delved deep into the latest advancements in AI, discussing the transformative impact of generative models and their applications across various industries. The emphasis on ethical AI and ensuring responsible use of technology was particularly thought-provoking. ?? Innovations in Machine Learning: The session on ML highlighted the importance of continuous learning and adaptation. We explored cutting-edge algorithms and tools that are shaping the future of predictive analytics and automation. The discussions on the integration of ML in day-to-day operations were incredibly insightful. ?? Trends in Data Engineering: Data is the backbone of all AI and ML endeavors. The speakers shared best practices for managing and optimizing large datasets, ensuring data quality, and leveraging cloud technologies for scalable data solutions. The importance of a robust data infrastructure was a key focus. ?? Evolution of DevOps Practices: The integration of DevOps practices in AI/ML projects is crucial for ensuring seamless deployment and operational efficiency. The sessions covered the latest trends in CI/CD pipelines, containerization, and orchestration tools, underscoring the need for a collaborative and agile approach. I am immensely grateful to our CEO Sir,C Y for envisioning and spearheading this event. Your leadership and commitment to fostering innovation and knowledge sharing within the company are truly inspiring. Thank you, Sir, for providing us with such a remarkable platform to learn and grow. Looking forward to applying these insights in our projects and continuing to push the boundaries of technology. ?? #AI #MachineLearning #DataEngineering #DevOps #TechInnovation #vts#GenerativeAI #TechStackWorldSeries #CompanyEvent #ThankYou

?? Fascinating?insights from Oz Katz on building reproducible AI pipelines with Kubeflow, lakeFS, and LangChain!?#KubeCon ?? Key?Innovations:? Data version control? RAG pipeline reproducibility? Automated quality checks? Branching strategies? Request logging system ?? Notable?Features:? LangChain integration? Vector?database storage? Data?quality validation? Pipeline debugging?tools? Source?tracing capability??? Perfect?for:? MLOps Engineers? Data Scientists? Platform Engineers? DevOps Teams? AI Developers ?? Impact:? Enhanced reproducibility? Better?data quality? Improved debugging? Transparent?outputs? Reliable?AI systems ?? Preview of the talk: 1.?Session?focused?on?using?Kubeflow,?lakeFS,?and?Langchain?to?enhance?data-driven?applications 2.?LangChain?becoming?essential?for?developing?RAG-based?applications 3.?Data?imperfections?can?significantly?undermine?AI?application?quality 4.?Demonstrated?RAG?chatbot?building?using?Project?Gutenberg?texts 5.?Process?involves?chunking?text?and?creating?embeddings?for?vector?storage ... ?? Watch?the full presentation: https://lnkd.in/gVX2nTeT #AI?#Reproducibility #CloudNative?#CNCF

Red Hat #OpenShift AI is a hybrid #MLOps platform for streamlining collaboration, accelerating project timelines, integrated with the #DevOps lifecycle, bringing together IT, data science, and development teams to simplify and accelerate model collaboration, creation, and sharing. Register for this FREE webinar: https://lnkd.in/gmT_mZSm to know how to acquire the skills with the training offerings. While registering, in the field, "Through which Red Hat Training Partner did you come to know about this event?" please choose 'IPSR Solutions Limited' from the dropdown menu. #RedHatOpenShift #RedHatWebinar #DevOps #AI #DataScience #ITDevelopment

?? Understanding Docker, YAML, and Configuration Files in Data Science & AI ?? In the fast-paced world of Data Science & AI, Docker and YAML play a crucial role in ensuring consistent and reproducible environments. ?? Docker containers encapsulate your code, dependencies, and environment, making it easy to share and deploy anywhere. ?? YAML files provide a human-readable way to define configurations for these containers, making setups seamless. ?? Config Files ensure that your models, data pipelines, and applications run with the correct settings every time. Mastering these tools is key to efficient, scalable, and collaborative AI development! ?? #DataScience #AI #MLOps

Great article from Sequoia Capital - "Thanks to Agentic Reasoning, the AI transition is?Service-as-a-Software. Software companies turn labor into software. That means the addressable market is not the software market, but the services market measured in the?trillions?of dollars" #serviceasasoftware #agenticreasoning #agenticflow #vc #venturecapital #ai #llm #largelanguagemodels #generativeai #aipoweredleadership ?? NeuTalent