MLOps Training Online | MLOps Course in Hyderabad

1. Mastering the • MLOps Lifecycle: • Key Skills for 2025–26 • Bridging the Gap BetweenMachine 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 is Important • 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 • 1️⃣ Data Collection – Gathering raw data • 2️⃣ Data Processing – Cleaning & preparing data • 3️⃣ Model Development – Training ML models • 4️⃣ Model Deployment – Putting models into production • 5️⃣ Monitoring & Maintenance – Tracking performance & retraining • 6️⃣ Continuous Improvement – Updating with feedback loops • www.visualpath.in

5. Key Skills for • Students • 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 in 2025 • MLflow – Model tracking • Kubeflow – ML on Kubernetes • Seldon – Model deployment • Apache Airflow – Workflow automation • Weights & Biases – Experiment tracking • Azure Machine Learning – Cloud AI workflows

7. Top Corporate Recruiters Tech Giants: Google, Microsoft, Amazon, IBM, NVIDIA • AI Leaders: OpenAI, Hugging Face, DataRobot • 01 • 02 • Consulting Firms: Accenture, Deloitte, PwC • Startups: Scale AI, Cohere, Stability AI • 03 • 04 • Cloud Providers: AWS, Azure, GCP • 05

8. Career Roles in • MLOps • MLOps Engineer • Machine Learning Engineer • AI Infrastructure Engineer • Data Engineer (ML Focus) • AI Solutions Architect www.visualpath.in

9. Beginner Project Ideas • Deploy a 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 the backbone of production AI systems. In 2025–26, companies will need engineers who can build, deploy, and manage AI models at scale. 🚀 Start your journey today — future-proof your AI career! • www.visualpath.in

12. You • Thank • I hope this presentation was helpful and engaging. Don’t hesitate to ask questions or share your ideas. • +91 7032290546 • www.visualpath.in