MLOps Certification Program
(Bridge the Gap Between Data Science & Production AI)
What is MLOps & Why Does It Matter? Machine Learning Operations (MLOps) is the discipline of bringing ML models from notebooks to production — reliably, scalably, and continuously. Most data scientists can build a great model, but only a fraction know how to deploy it, monitor it in production, retrain it automatically, and keep it running reliably at scale. At Isha Training Solutions, our MLOps certification is built around real enterprise pipelines. You won’t just learn theory — you will build, break, debug, and ship real ML systems using the same tools used at Google, Amazon, and leading AI companies.
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 99 USD Or USD13000 INR 12900 INR 7900 Rupees
OR
Free Demo On:
25th June @ 9:00 PM – 10:00 PM (IST) (Indian Timings)/
25th June @ 11:30 AM –12:30 PM (EST) (U.S Timings)/
25th June @ 4:30 PM – 5:30 PM (BST) (U.K Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 9:00 PM – 10:00 PM (IST)
For Participants in the US: Monday to Friday @ 11:30 AM –12:30 PM (EST)
For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)
What student’s have to say about Trainer :
|
Kumar Janapareddy Varalakshmi Somu Archana C Venkat Pradeep Akshay Patil Chandra Mouli Rada Mintu T.S Siddhartha Bhargavi B |
Salient Features:
- 58 Hours of Live Training along with recorded videos
- Lifetime access to the recorded videos
- Course Completion Certificate
Who can enroll in this course?
- Freshers or recent graduates from technical backgrounds (B.Tech, M.Tech, MCA, BCA, MSc, BE)
- Software Engineers testers looking to transition into cloud technologies
- Manual & Automation Test Engineers and data analysts exploring Azure data services
- Working professionals aiming to move into Data Engineering or Cloud ETL roles
- Professionals Returning to Work After a Career Break
- IT Professionals Looking to Transition into AI & MLOps
- Anyone Interested in Building, Deploying, and Managing Machine Learning Models in Production
What will I Learn by end of this course?
- Understand the Complete MLOps Lifecycle from Development to Production
- Build and Train Machine Learning Models Using Python
- Master Data Preparation, Feature Engineering, and Model Evaluation
- Version Control ML Projects Using Git & GitHub
- Implement Security, Governance, and Best Practices in MLOps
- Learn How AI Models Are Managed in Enterprise Environments
- Prepare for MLOps, Machine Learning Engineer, AI Engineer, and Data Engineering Roles
- Use Industry-Standard MLOps Tools and Frameworks
- Manage End-to-End Machine Learning Pipelines
Course syllabus:
🧩MLOps Foundations & the ML Lifecycle
Class 1 – What is MLOps? Core principles and maturity levels
- What is ETL and Data Warehousing
- The full ML lifecycle: data → model → production
- DevOps vs MLOps vs DataOps — differences and overlaps
- Git and version control for ML projects
- Linux fundamentals for ML engineers
Class 2 – Data Versioning & Feature Engineering
- Data Version Control (DVC): tracking datasets and models
- Data pipelines with DVC remote storage (S3, GCS)
- Feature engineering best practices for production
- Introduction to Feature Stores: Feast and Tecton
- Handling data drift at the data layer
Class 3 –Experiment Tracking & Model Registry
- MLflow: tracking experiments, parameters, and metrics
- Model versioning and the MLflow Model Registry
- Weights & Biases (W&B;) for collaborative experiment tracking
- Comparing experiments and selecting best models
- Reproducibility in ML — seeds, environments, artifacts
Class 4 – Containerization & Infrastructure
- Docker fundamentals for ML engineers
- Writing Dockerfiles for ML training and serving
- Kubernetes basics: pods, deployments, services
- Helm charts for ML workloads
- GPU resource management in containerized environments
Class 5 –ML Pipeline Orchestration
- Apache Airflow: DAGs, operators, and scheduling
- Kubeflow Pipelines: cloud-native ML workflows
- Prefect and Metaflow as pipeline alternatives
- Triggering retraining pipelines automatically
- Error handling, alerting, and distributed training
Class 6 – Model Deployment & Serving
- Serving models with FastAPI: REST API design
- BentoML for packaging and deploying ML services
- Triton Inference Server for high-throughput serving
- Batch vs real-time vs streaming inference patterns
- Shadow deployments and canary releases
Class 7 –CI/CD for Machine Learning
- GitHub Actions: automating training, testing, and deployment
- Continuous training (CT) pipelines
- Unit testing ML code with pytest
- Data validation with Great Expectations
- Model evaluation gates: preventing bad models from shipping
Class 8 – Model Monitoring & Observability
- Types of drift: data drift, concept drift, prediction drift
- Evidently AI for data and model monitoring dashboards
- Prometheus and Grafana for model performance metrics
- Setting up alerting for drift and latency thresholds
- Logging strategies for ML services in production
Class 9 – Cloud MLOps: AWS, GCP & Azure
- AWS SageMaker Pipelines: end-to-end ML on AWS
- Google Cloud Vertex AI: managed ML on GCP
- Azure Machine Learning: pipelines and endpoints
- Cloud storage for ML artifacts (S3, GCS, Azure Blob)
- Cost optimization for ML workloads in the cloud
Class 10 – LLMOps & Generative AI in Production
- Introduction to LLMOps: what changes with large language models
- Fine-tuning pipelines for LLMs: LoRA, QLoRA
- LangChain and vector databases: Pinecone, Weaviate
- Serving LLMs with vLLM and Ollama
- RAG pipelines in production and monitoring hallucinations
How can I enroll for this course?
OR
For any other details, Call me or Whatsapp me on +91-9133190573
Live Sessions Price:
For LIVE sessions – Offer price after discount is 129 USD 109 89 USD Or USD15000 INR 12000 INR 9900 Rupees.
Sample Course Completion Certificate:
Your course completion certificate looks like this……
Important Note:
To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.
To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.
We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.
Course Features
- Lectures 49
- Quiz 0
- Duration 58 hours
- Skill level All levels
- Language English
- Students 176
- Assessments Yes
- 11 Sections
- 49 Lessons
- 58 Hours
- MLOps Foundations & the ML Lifecycle5
- Data Versioning & Feature Engineering5
- Experiment Tracking & Model Registry5
- Containerization & Infrastructure5
- ML Pipeline Orchestration5
- Model Deployment & Serving5
- CI/CD for Machine Learning5
- Model evaluation gates: preventing bad models from shipping5
- Cloud MLOps: AWS, GCP & Azure0
- AWS SageMaker Pipelines: end-to-end ML on AWS4
- LLMOps & Generative AI in Production5
