Program Highlights
As AI moves into production, engineering robust, scalable, and responsible systems is essential. InfosecTrain’s AI Engineering Pro Training will equip practitioners to design, deploy, and maintain production-grade AI pipelines. Learners work with containerization, orchestration, experiment tracking, advanced model optimization, MLOps automation, cloud deployment, and explainability frameworks, moving from research to repeatable, monitored systems. Through hands-on labs, industry case studies, and a full capstone, participants gain the skills to deliver end-to-end AI solutions that meet performance, scalability, and governance needs.
20 Hours LIVE Instructor-led Training 
Hands-on Labs 
Real-world Applications & Case Studies 
Practical Frameworks 
Capstone Project 
Certified Experts 
Career Guidance & Mentorship 
Dedicated Telegram Support Group 
Access to Recorded Sessions
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The AI Engineering Pro Training is designed for learners ready to move beyond intermediate concepts and master real-world, production-grade AI engineering. This course will focus on scalable system design, advanced modeling, cloud deployment, and full MLOps lifecycle management. Through hands-on labs with Docker, Kubernetes, MLflow, and cloud platforms, participants learn to build, optimize, deploy, and monitor enterprise-level AI solutions. The program will culminate in a full end-to-end capstone project that mirrors real industry workflows and prepares learners for professional AI engineering roles.
- Module 1: AI Engineering at Scale
- Understand the full AI engineering workflow in production: data ingestion → model training → deployment → monitoring → retraining.
- Explore system-level design patterns for scalable AI systems.
- Hands-on introduction to containerization with Docker: package an ML model and run it locally.
- Learn experiment tracking with MLflow: log metrics, compare runs, and manage models.
- Orchestrate a simple AI workflow with Kubernetes (Minikube) to simulate real-world scaling.
- Mini-lab: Containerize a model, track experiments with MLflow, and deploy on Kubernetes.
- Module 2: Advanced Modeling & Optimization
- Review classical vs modern ML models in production use-cases.
- Master hyperparameter tuning with GridSearch, RandomSearch, and advanced tools like Optuna.
- Hands-on: Optimize a fraud detection model with Optuna and compare against default settings.
- Explore ensemble methods (stacking, blending, boosting) and why they often outperform single models.
- Learn feature engineering at scale: domain-driven features, dimensionality reduction (PCA), and feature
selection. - Introduction to transfer learning for LLMs and vision models: fine-tune BERT or ResNet using PEFT (LoRA,
adapters). - Lab: Fine-tune a pre-trained transformer (e.g., BERT for sentiment analysis) with LoRA for efficiency.
- Module 3: Deployment & MLOps
- Understand why MLOps is critical for real-world AI adoption.
- Learn CI/CD for AI: GitHub Actions / Jenkins pipelines to automate training and deployment.
- Hands-on: Build a pipeline that trains a model and automatically redeploys when code changes.
- Deploy AI models as REST APIs with Flask/FastAPI, containerized via Docker.
- Push models to AWS Sagemaker / GCP Vertex AI / Azure ML for cloud deployment.
- Monitoring production AI:
- Detect data drift, concept drift, model performance decay.
- Automate alerts and retraining workflows.
- Lab: Deploy a churn prediction model as a REST API with FastAPI, containerize it, and deploy to AWS.
- Module 4: Responsible & Ethical AI
- Explore the risks of biased and opaque models in real-world decisions (finance, healthcare, hiring).
- Hands-on: Use SHAP and LIME to explain black-box models.
- Conduct a fairness audit: detect dataset bias, compare performance across subgroups, and mitigate bias.
- Introduction to adversarial robustness: demonstrate how adversarial inputs can fool a model, and test
defenses. - Understand global AI governance and compliance frameworks: EU AI Act, NIST AI RMF, ISO standards.
- Lab: Audit a credit scoring dataset for bias, mitigate issues, and generate an explainability report.
- Module 5: Capstone Project – End-to-End AI Engineering
- Requirements:
- Build and optimize the model (use ensembles or transfer learning).
- Containerize with Docker and track experiments with MLflow.
- Deploy to cloud (AWS/GCP/Azure).
- Set up monitoring for drift and performance decay.
- Conduct a bias & explainability audit.
- Deliverables:
- Working deployed model + API endpoint.
- Deployment pipeline (CI/CD + cloud).
- Technical documentation & explainability report.
- Presentation & peer review simulating enterprise workflow.
- Requirements:
This training is ideal for:
- Learners with ML/AI basics who want to become job-ready AI Engineers
- Completion of AI Engineering Intermediate or equivalent ML/AI knowledge
- Familiarity with Python, machine learning, and neural networks
- Basic understanding of cloud platforms and APIs
Upon successful completion of the training, participants will be able to:
- Build end-to-end AI systems and pipelines
- Optimize models using advanced techniques and transfer learning
- Deploy AI solutions on cloud platforms
- Implement MLOps practices for scalable and maintainable AI
- Understand ethical AI principles and mitigate bias
- Complete a capstone project demonstrating full AI engineering workflow
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It was a very good experience with the team. The class was clear and understandable, and it benefited me in learning all the concepts and gaining valuable knowledge.
I loved the overall training! Trainer is very knowledgeable, had clear understanding of all the topics covered. Loved the way he pays attention to details.
I had a great experience with the team. The training advisor was very supportive, and the trainer explained the concepts clearly and effectively. The program was well-structured and has definitely enhanced my skills in AI. Thank you for a wonderful learning experience.
The class was really good. The instructor gave us confidence and delivered the content in an impactful and easy-to-understand manner.
The program helped me understand several areas I was unfamiliar with. The instructor was exceptionally skilled and confident in delivering content.
The program was well-structured and easy to follow. The instructor’s use of real-life AI examples made it easier to connect with and understand the concepts.
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Frequently Asked Questions
What is the AI Engineering Pro course by InfosecTrain?
The AI Engineering Pro course is an advanced, hands-on program that teaches enterprise-level AI system design, scalable deployment, and MLOps workflows. This Advanced AI engineering course prepares learners to build, automate, and manage production-grade AI systems end-to-end.
Who should join the AI Engineering Pro training?
This Professional AI engineering program is ideal for learners with ML/AI basics who want to become expert-level AI Engineers, MLOps Engineers, or professionals transitioning into enterprise AI architecture roles.
Does this course cover advanced AI deployment and MLOps?
Yes. This course includes CI/CD pipelines, automated training workflows, containerization, orchestration with Kubernetes, and full lifecycle model monitoring.
What skills can I gain from the AI Engineering Pro program?
You will develop high-level skills in AI automation and scaling, cloud deployment, model optimization, explainability, drift detection, pipeline orchestration, and enterprise AI governance.
Are hands-on labs included in the AI Engineering Pro training?
Absolutely. The training includes practical labs covering Docker, Kubernetes, MLflow, FastAPI, cloud platforms, and real-world monitoring and retraining scenarios.
What are the prerequisites for the AI Engineering Pro course?
Learners should have foundational ML/AI skills, Python knowledge, understanding of neural networks, and basic familiarity with cloud environments. Completing the Intermediate level or equivalent experience is recommended.
How long does the AI Engineering Pro online training take?
The training is delivered through structured live sessions, labs, and a capstone project. The full duration is shared in the course schedule for each batch.
Does InfosecTrain provide a certificate for the AI Engineering Pro course?
Yes. After finishing the program, you will receive a certificate of completion issued by InfosecTrain.
What tools and technologies are taught in the AI Engineering Pro training?
The training covers Docker, Kubernetes, MLflow, FastAPI, GitHub Actions/Jenkins, cloud AI services (AWS, GCP, Azure), Optuna, SHAP, LIME, and production-grade monitoring frameworks.
How can I enroll in the AI Engineering Pro course at InfosecTrain?
Enrollment can be completed through the InfosecTrain website or by contacting the support team for assistance with batch details.
Does the course include hands-on labs for deployment, orchestration, and monitoring?
Yes. The training emphasizes AI system deployment training, Kubernetes orchestration, MLflow tracking, cloud integration, and real-time monitoring with drift alerts.
Is this course suitable for senior AI engineers or professionals transitioning to AI architect roles?
Definitely. This Enterprise AI engineering program strengthens system design, automation, optimization, and governance skills required for high-level AI roles.
How does the AI Engineering Pro training improve enterprise AI engineering skills?
It builds mastery in scalable architecture, cloud-native deployment, automated pipelines, compliance, explainability, and operational AI—core competencies for enterprise AI environments.
Does the course cover security, governance, and compliance in AI systems?
Yes. The program includes AI governance, risk management, bias mitigation, adversarial robustness, and global compliance frameworks like the EU AI Act and NIST AI RMF.
What makes InfosecTrain’s AI Engineering Pro course different from other advanced AI programs?
The InfosecTrain AI Pro course stands out for its hands-on approach, cloud-integrated workflows, real-world labs, expert trainers, and a full end-to-end enterprise AI capstone project.
Does the training include downloadable learning materials and project resources?
Yes. Learners receive slides, datasets, templates, lab resources, and project reference files.
How does the Pro-level course prepare learners for high-level AI engineering roles?
By building expertise in automation, deployment, monitoring, optimization, and responsible AI, everything required for AI automation and scaling at enterprise level.
How can I enroll in the AI Engineering Pro training with InfosecTrain?
Learners can enroll through the official website or request assistance from the InfosecTrain support team.
