- info@whitestoneinternationalcollege.org.uk
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Whitestone International College of Innovation delivers quality-assured, standards-aligned programmes that integrate academic rigour, industry relevance, and digital fluency to develop principled leaders who deliver measurable impact.
- London, United kingdom
- +44 20 3727 6493
-
Info@whitestoneinternational
college.org.uk
United States
United Arab Emirates
India
Courses
Professional Diploma in Machine Learning, Data Engineering, and MLOps
The Professional Diploma in Machine Learning, Data Engineering, and MLOps provides learners
with the essential technical, analytical, and operational expertise required to design, build, and
manage AI-driven systems at scale.
Course Overview
The Professional Diploma in Machine Learning, Data Engineering, and MLOps provides learners
with the essential technical, analytical, and operational expertise required to design, build, and
manage AI-driven systems at scale.
This programme integrates machine learning theory, data engineering infrastructure, and MLOps
(Machine Learning Operations) practices—enabling professionals to automate and operationalize
intelligent solutions across industries.
Participants gain hands-on experience in Python, TensorFlow, PyTorch, Docker, Kubernetes, and
cloud ML platforms (AWS, Azure, GCP) to develop end-to-end pipelines from raw data to
production-level AI models.
Why This Course is Important?
- Industry-Driven Skills: Equips professionals with end-to-end expertise in model development, deployment, and lifecycle management.
- High-Demand Domain: Meets the growing need for skilled MLOps and data engineering professionals in AI transformation.
- Career Mobility: Bridges data science, DevOps, and AI engineering for high-value global roles.
Learning Outcomes
By the end of this programme, participants will be able to:
- Build and manage scalable data pipelines using modern data engineering tools.
- Design, train, and evaluate advanced machine learning and deep learning models.
- Integrate automation through CI/CD pipelines and container orchestration systems.
- Deploy and monitor ML models using MLOps frameworks (MLflow, Kubeflow, etc.).
- Apply principles of data governance, reproducibility, and responsible AI.
- Utilise cloud-based ML infrastructure (AWS, Azure, or GCP) for real-world AI applications.
Target Audience
- Data Scientists and Machine Learning Engineers
- Data Engineers and DevOps Professionals
- Software Engineers and Cloud Architects
- AI Practitioners and Technical Project Managers
- Graduates aspiring to advance into AI system operations
Entry Requirements
- Bachelor’s degree in Computer Science, IT, Engineering, or related discipline.
- Basic programming experience (preferably Python).
- Understanding of data analytics or machine learning concepts recommended.
- English language proficiency (IELTS 5.5 or equivalent).
Programme Structure & Modules
- Overview of AI ecosystem and ML lifecycle
- Supervised, unsupervised, and reinforcement learning fundamentals
- Data ingestion, transformation, and storage design
- ETL frameworks, orchestration tools (Airflow, Prefect)
- Feature engineering, model selection, and tuning
- Deep learning with TensorFlow and PyTorch
- CI/CD pipelines, model registry, versioning, and drift detection
- Monitoring, logging, and model retraining automation
- Deploying models with Docker and Kubernetes
- ML pipelines on AWS, Azure ML, and Google Vertex AI
- Data privacy, fairness, and transparency in AI
- Frameworks for ethical and explainable AI compliance
- Real-world case simulation from data engineering to MLOps
- Deployment of a complete ML pipeline with monitoring and reporting
Awarding Body
Whitestone International College of Innovation
United Kingdom
Qualification Type
Professional Diploma – Postgraduate Level Qualification
(Industry-aligned, regulated qualification issued by Whitestone International College of Innovation, UK)
Delivery Mode
Classroom – London (UK) / Dubai (UAE) campuses
Live Online – Instructor-led virtual sessions
Blended Learning –Online theory + hands-on labs + final capstone project
Duration
10–12 weeks (full-time) or 4–6 months (part-time blended)
Total Learning Hours: 180–200 hours
- Assessment: Practical assignments, lab exercises, and final capstone deployment project
- Certification: Upon successful completion, participants receive the Professional Diploma in Machine Learning, Data Engineering, and MLOps
- UK-issued Professional Diploma recognised internationally
- Comprehensive training in data engineering, machine learning, and MLOps
- Practical project demonstrating real-world deployment skills
- Enhances employability in AI, data science, and automation careers
Aligned with international technical and professional standards:
- Microsoft Azure AI & Data Engineering Certifications
- Google Cloud Professional ML Engineer Framework
- AWS Machine Learning Specialty Standards
- ISO/IEC 23894: Artificial Intelligence – Risk Management
- CPD-UK Continuing Professional Development Standards
Programme Fees
Clear Fee Structure With No Hidden Costs
£
1250
Per Course
-
Industry-focused programmes with global standards.
-
Practical skills for real-world success.
-
Academic excellence with career-ready outcomes.
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