DevOn’s Post

Love the shout out from the team @ JFrog. Give a check to the article and short passage below. "Currently, data scientists and ML engineers are using a myriad of disparate tools, which are mostly disconnected from standard DevOps processes within the organization, to mature models to release." #machinelearning #mlops #datascience #data Qwak

🚀 Elevate your data processing game with Celery and Kubernetes! Dive into my latest Medium article where I unveil the secrets of building high-performance workflow orchestrators. From concept to production, discover the art of seamless task orchestration and optimization. This article is for Developers, Data Engineers, DevOps and Solutions Architects, weather its building scalable data pipelines, microservices or a platform for your product this article has it all. Do check it out and let me know what you guys think https://lnkd.in/dC-V7Mz8 #DataEngineering #Celery #Kubernetes #WorkflowOrchestration #TechInsights #Devops #Devlopers #microservices #ETL

𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐌𝐋𝐎𝐩𝐬 𝐭𝐚𝐬𝐤𝐬 𝐢𝐧𝐭𝐨 𝐃𝐞𝐯𝐎𝐩𝐬 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐰𝐢𝐭𝐡 𝐊𝐢𝐭𝐎𝐩𝐬 ♻ 📦 One of the bottleneck of using datasets in #MachineLearning is that they are disparate and separate #MLOps pipelines need to be built to manage them alongside existing #DevOps pipelines. This process is inefficient at certain times as this has following drawbacks ❗ 𝐎𝐯𝐞𝐫𝐥𝐚𝐩𝐩𝐢𝐧𝐠 𝐄𝐟𝐟𝐨𝐫𝐭𝐬:- Need to manage  a DevOps pipeline for application code and a separate MLOps pipeline for the trained model, its dependencies and configuration files. ❗𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐎𝐯𝐞𝐫𝐡𝐞𝐚𝐝:- Managing different pipelines introduces potential inconsistencies between the pipelines. ❗𝐒𝐢𝐥𝐨𝐞𝐝 𝐓𝐞𝐚𝐦𝐬: -  Data scientists might struggle to understand deployment complexities, while DevOps engineers might lack knowledge of the specific needs of working with ML models ❗𝐒𝐭𝐞𝐞𝐩𝐞𝐫 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐮𝐫𝐯𝐞𝐬:- Requires additional set of tools to be deployed in the infrastructure to adapt to new learning models and manage them on DevOps These challenges can be mitigated using #OpenSource tool 𝐊𝐢𝐭𝐎𝐩𝐬 that encapsulates components of an ML Project into an OCI compliant image using "ModelKits" that can be pushed to a private registry and consumed within existing CI/CD pipeline Key Features: 🎁 Unified packaging: A ModelKit package includes models, datasets, configurations, and code. 🏭 Versioning: Each ModelKit is tagged so everyone knows which dataset and model work together. 🤖 Automation: Pack or unpack a ModelKit locally or as part of your CI/CD workflow for testing, integration, or deployment. 🪛 LLM fine-tuning: Use KitOps to fine-tune a large language model using LoRA. 🎯 RAG pipelines: Create a RAG pipeline for tailoring an LLM with KitOps. 🔒 Tamper-proofing: Each ModelKit package includes a SHA digest for itself, and every artifact it holds. 📝 Signed packages: ModelKits and their assets can be signed so you can be confident of their provenance. 🐳 Deploy containers: Generate a #Docker container as part of your kit unpack (coming soon). 🚢 Kubernetes-ready: Generate a #Kubernetes / KServe deployment config as part of your kit unpack (coming soon). Project url - https://lnkd.in/gMzGU2ZV #machinelearning #genai #llm #ml #cloudnative #architect #cloud

Great conversation with Demetrios Brinkmann and Andy McMahon on #LLMops / #MLops / #AiInfrastructure “MLops is heavily inspired by DevOps. With every iteration it seems to get worse where everyone thinks and talks about tooling and less about the process and culture side of it. 💯 the situation in Ai infrastructure space where everyone cares about the tools not about what is it we are trying to do, the process, and culture to make a n+1 style of execution.” “People should push the bounds of opensource tools first. This will make you an informed buyer. You will also realize is it an Ai tool you need or is it actually an infrastructure tool you need. https://lnkd.in/eYUvuKhh

With software applications, developers often end up spending at least a few days before every release on the tedious task of creating the test data, whether for performance, for functionality testing, or for API testing. Kalyan Veeramachaneni says that the The Synthetic Data Vault enterprise product we've built allows companies to build generative models and then sample from it to get the data to test their applications. This allows them to spend more time on the more interesting work of building and shipping features. Learn more in this video, where Kalyan talks to Paul Nashawaty from Futurum Group about the future of synthetic data. https://lnkd.in/eCfwwny6 #devops #bigdata #syntheticdata #generativeai #data #datascience #enterprisedata #tabulardata  #softwaretesting #softwareapps #apptesting

🤖📊 Responsibilities Along ML Pipelines The traditional DevOps methodology, by itself, is inadequate for the complexities of AI systems. DevOps and SysOps have been instrumental in improving the efficiency, reliability, and speed of software delivery and operations. However, their focus has traditionally been on automating and optimizing the development and operational phases of traditional software systems. This gap has become more apparent with recent breakthroughs in AI, which necessitate a methodology tailored for the intricacies of ML and AI processes — MLOps. MLOps tackles the limitations of DevOps and SysOps by addressing the complexities and needs of ML lifecycle management. Essential Practices in MLOps 🛠️ Based on insights from article by Harshit Tyagi on Towards Data Science, MLOps can be broken down into 7 primary phases: 1️⃣ Framing ML Problems: Translating business objectives into ML tasks with clear KPIs. — Data Analysts/Data Scientists 2️⃣ Architecting ML and Data Solutions: Identifying data sources, ensuring data credibility and compliance, and designing data pipelines. — Data Engineers/Data Scientists 3️⃣ Data Preparation and Processing: Involving feature engineering, data cleaning, and selection, and choosing the right cloud services for efficient and cost-effective data management. — Data Engineers/Data Scientists 4️⃣ Model Training and Experimentation: Conducting iterative model training sessions, versioning models and data for reproducibility, and utilizing open-source tools for managing ML systems components. — Data Scientists 5️⃣ Building and Automating ML Pipelines: Identifying system requirements, selecting cloud architectures, and auditing pipeline runs. — ML Engineers 6️⃣ Deploying Models: Exploring different deployment strategies, such as static, dynamic, serverless, or model streaming, and ensuring model explainability and compliance with governance requirements. — ML Engineers/DevOps 7️⃣ Monitoring, Optimizing, and Maintaining Models: Establishing performance tracking, logging strategies, and continuous evaluation metrics to maintain system integrity and model accuracy in production. — ML Engineers/Data Scientists Curious to learn more about AI and data? Follow me for next week's trends, industry insights, and innovative use cases. Learn more about Nimble's AI-driven scraping solution: https://lnkd.in/dFdGjQj2 #bigdata #artificialintelligence #innovation #webscraping

🚀 From DataSecOps to DevSecOps to MLOps: Tackling Technical Debt in Machine Learning 🚀 Are you looking to enhance your software development and deployment practices? Do you want to integrate security and machine learning seamlessly into your operations? If so, join me on an enlightening journey as we explore the evolution from DataSecOps to DevSecOps to MLOps and how each stage helps tackle technical debt in machine learning! 🔍 What You’ll Discover: 1️⃣ Introduction to MLOps: Understand why MLOps is a game-changer in machine learning and data science, especially in mitigating technical debt. 2️⃣ The MLOps Lifecycle: Follow the series as we navigate the key stages from ML development to operations, highlighting where technical debt can arise and how to address it. 3️⃣ Real-World Use Case: See how MLOps can be applied to practical problems like predicting taxi trip durations and helping businesses improve customer satisfaction and operational efficiency. 4️⃣ MLOps Maturity Model: Explore the progression from Level 0 to Level 4 with detailed insights on how each level helps reduce technical debt through improved practices and automation. 5️⃣ Infra Setup at Level 0: Get hands-on with setting up an EC2 instance, configuring the AWS CLI, and connecting via SSH to VSCode, laying the foundation for effective technical debt management. 6️⃣ Running ML Models: Learn how to use port forwarding to run Jupyter Notebooks and execute ML code seamlessly. 🌟 Why It Matters: Efficiency: Streamline development and release cycles, reducing the accumulation of outdated practices and code. Quality: Improve the reliability and performance of ML models. Automation: Leverage CI/CD for continuous integration and deployment, ensuring consistency and reducing manual intervention errors. Collaboration: Break down silos between teams for better alignment and faster innovation. 🔜 What’s Next? Stay tuned for the next level in our journey where we dive into MLOps Maturity Level 1! We’ll explore deeper integration of DevOps practices, setting up CI/CD pipelines, and fostering collaboration between data scientists and engineers. #MLOps #DevSecOps #DataSecOps #DataTalksClub #MachineLearning #AI #DataScience #DevOps #CloudComputing #Innovation #TechnicalDebt #Automation #Collaboration

🚀Let Diving Deep into MLOps: Key Models and Frameworks 🚀 Any Software is a flow of work to get a result. So, MLOps is also the flow of models and frameworks. As I prepare for my role as an MLOps Engineer, I'm excited to explore the foundational models and frameworks that drive the MLOps ecosystem. These tools are crucial for building, deploying, and maintaining machine learning models at scale. Here are some of the main models and frameworks that are shaping the future of MLOps: TensorFlow Extended (TFX): TFX is an end-to-end platform for deploying production ML pipelines. It includes components for data ingestion, model training, validation, and serving, making it a comprehensive solution for MLOps. Kubeflow: Built on Kubernetes, Kubeflow aims to make deploying scalable and portable ML workflows easier. It leverages Kubernetes’ strengths to facilitate the orchestration of ML tasks, from experimentation to deployment and management. MLflow: MLflow is an open-source platform designed to manage the ML lifecycle, including experimentation, reproducibility, and deployment. It offers tools for tracking experiments, packaging code into reproducible runs, and managing and deploying models. Seldon: Seldon is an open-source platform for deploying, scaling, and managing thousands of machine learning models on Kubernetes. It focuses on inference and monitoring, providing robust support for scaling ML models in production. Airflow: Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. While not exclusively for ML, it is widely used for orchestrating complex ML pipelines, thanks to its flexibility and extensive integration options. DVC (Data Version Control): DVC is a version control system for machine learning projects. It tracks data files and ML models, ensuring reproducibility and enabling collaborative data science workflows. Metaflow: Developed by Netflix, Metaflow is a human-centric framework for data science. It simplifies the process of building and managing real-life data science projects, providing a smooth path from prototype to production. Understanding and leveraging these models and frameworks is essential for driving innovation and ensuring the success of ML projects. If you're passionate about MLOps, data science, or DevOps, let's connect and exchange insights! #MLOps #MachineLearning #AI #DevOps #DataEngineering #TechI

DataOps is a discipline that merges data engineering and data science teams to support an organization’s data needs, similar to how DevOps helps organizations scale software engineering.    In the same way that DevOps applies CI/CD to software development and operations, DataOps entails a CI/CD-like, automation-first approach to building and scaling data products. At the same time, DataOps makes it easier for data engineering teams to provide analysts and other downstream stakeholders with reliable data to drive decision making.    #DataOps #DataManagement #DataStreamlining #Efficiency #Automation #DataAnalytics #DataEngineering #StreamlinedProcesses #DataScience #DigitalTransformation

What is MLOps? MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment by monitoring, validation, and governance of machine learning models. We are embedding decision automation in a wide range of applications and this generates a lot of technical challenges that come from building and deploying ML-based systems. To understand MLOps, I share with you this Roadmap 1. We must first understand the ML systems lifecycle which involves several different teams of a data-driven organization. https://lnkd.in/djar2Mqs 2. Data Engineering — data acquisition and preparation. https://lnkd.in/gPrEVbDT 3. Data Science — architecting ML solutions and developing models. https://lnkd.in/djRezyFe 4. IT https://lnkd.in/dqspggR7 5. DevOps — complete deployment setup, and monitoring alongside scientists. https://lnkd.in/dTbz-Y47 Follow Rahul Kumar for more