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MLOps Training Online | MLOps Course in Hyderabad
- 1. Mastering the MLOps Lifecycle: KeySkills for 2025–26 Bridging the Gap Between Machine Learning and Operations www.visualpath.in
- 2. Introduction What is MLOps? •MLOps = Machine Learning + DevOps • Helps in building, deploying, and monitoring ML models efficiently • Ensures ML models stay accurate, scalable, and production-ready • Example: Deploying a fraud detection model to a banking app and keeping it updated www.visualpath.in
- 3. Why MLOps isImportant in 2025–26 • 📈 Rising demand for AI-powered applications • ⏳ Reduces time from model training to deployment • 🔄 Keeps models up-to-date with new data • 💰 Saves cost with automation and monitoring • ️ 🛡️Improves security & compliance for AI systems www.visualpath.in
- 4. The MLOps Lifecycle 1️ 1️⃣Data Collection – Gathering raw data 2️⃣Data Processing – Cleaning & preparing data 3 ️ 3️⃣Model Development – Training ML models 4️⃣Model Deployment – Putting models into production 5 ️ 5️⃣Monitoring & Maintenance – Tracking performance & retraining 6️⃣Continuous Improvement – Updating with feedback loops www.visualpath.in
- 5. Key Skills forStudents • Programming: Python, SQL • ML Frameworks: TensorFlow, PyTorch, Scikit-learn • DevOps Tools: Docker, Kubernetes, Jenkins • Cloud Platforms: AWS Sagemaker, Azure ML, GCP Vertex AI • Version Control: Git, DVC (Data Version Control) • Monitoring Tools: MLflow, Prometheus, Grafana
- 6. Best Tools in2025 • Kubeflow – ML on Kubernetes • MLflow – Model tracking • Seldon – Model deployment • Apache Airflow – Workflow automation • Azure Machine Learning – Cloud AI workflows • Weights & Biases – Experiment tracking
- 7. Top Corporate Recruiters 0102 03 04 Tech Giants: Google, Microsoft, Amazon, IBM, NVIDIA AI Leaders: OpenAI, Hugging Face, DataRobot Consulting Firms: Accenture, Deloitte, PwC Startups: Scale AI, Cohere, Stability AI 05 Cloud Providers: AWS, Azure, GCP
- 8. Career Roles in MLOps •AI Infrastructure Engineer • Machine Learning Engineer • MLOps Engineer • Data Engineer (ML Focus) • AI Solutions Architect www.visualpath.i n
- 9. Beginner Project Ideas • Deploya sentiment analysis model with Flask & Docker • Automate ML retraining with Airflow & Kubernetes • Monitor an ML model with MLflow & Grafana • Build a real-time stock price prediction system
- 10. Tips for Students ✅Learn both ML & DevOps fundamentals ✅ Practice with cloud-free tiers ✅ Start small, then scale up projects ✅ Contribute to open-source MLOps tools www.visualpath.in
- 11. Conclusion: MLOps is thebackbone of production AI systems. In 2025–26, companies will need engineers who can build, deploy, and manage AI models at scale. www.visualpath.in 🚀 Start your journey today — future-proof your AI career!
- 12. Thank You I hopethis presentation was helpful and engaging. Don’t hesitate to ask questions or share your ideas. +91 7032290546 www.visualpath.in