Mlforgeows
Machine Learning Infrastructure
PRODUCTION ML SYSTEMS

Engineer ML Systems That Scale

Build production-grade machine learning infrastructure with hands-on training in MLOps, distributed computing, and real-time systems. Transform theoretical knowledge into operational excellence.

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500+
Engineers Trained
98%
Project Success
24/7
System Uptime
COURSE PIPELINE

Production-Ready ML Training

Three specialized courses designed to build complete ML engineering capabilities from deployment to distributed systems.

MLOps Pipeline
SGD 1,140

MLOps and Model Deployment

Master operational aspects of machine learning including CI/CD pipelines, containerization with Docker and Kubernetes, and model serving infrastructure for scalable deployments.

  • Automated testing and model monitoring systems
  • Version control for data and model artifacts
  • Feature stores and serving layer implementation
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Distributed Computing
SGD 2,890

Distributed Machine Learning

Scale workloads across clusters using Apache Spark MLlib, Horovod, and distributed TensorFlow. Handle petabyte datasets and train models with billions of parameters efficiently.

  • Data-parallel and model-parallel training strategies
  • Distributed hyperparameter tuning at scale
  • Fault-tolerant training system architecture
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Real-time Systems
SGD 3,760

Real-time ML Systems

Build low-latency systems for real-time prediction and streaming analytics. Master Kafka, Flink, online learning algorithms, and edge computing for millisecond response times.

  • Stream processing and real-time feature computation
  • Online learning and concept drift detection
  • Hardware acceleration and model quantization
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ENGINEERING APPROACH

Systems Built for Singapore's Tech Landscape

Our curriculum reflects the engineering rigor demanded by Singapore's financial services, logistics, and technology sectors. Each course emphasizes production deployment, operational reliability, and performance optimization for high-stakes environments.

Training modules incorporate infrastructure patterns used by regional technology leaders. Students work with cloud platforms commonly deployed in APAC data centers, ensuring practical relevance for local industry requirements.

15 Weeks
Per Course Duration
40+ Hours
Hands-on Projects

Pipeline Architecture

Design end-to-end ML workflows from data ingestion through model deployment, implementing robust error handling and monitoring at each stage.

Infrastructure as Code

Provision and manage ML infrastructure using Terraform and Kubernetes, ensuring reproducible deployments across development and production environments.

Performance Optimization

Profile and optimize training pipelines for throughput and latency, applying distributed computing patterns and hardware acceleration techniques.

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Deploy Your ML Engineering Skills

Join engineers building production ML systems for Singapore's technology ecosystem. Courses begin quarterly with cohort-based learning and industry mentorship.

Request Course Info +65 6829 5741
30 Cecil Street
Prudential Tower
Singapore 049712
Mon-Fri: 9:00-18:00
Sat: 10:00-16:00
Sun: Closed
TECHNICAL DETAILS

Frequently Asked Questions

What prerequisites are needed for these courses?
Students should have working knowledge of Python and basic machine learning concepts. Experience with Linux command line and version control systems is beneficial. The MLOps course serves as an entry point, while Distributed ML and Real-time Systems build on those foundations. Programming assessments help determine appropriate course sequencing.
How are courses structured and delivered?
Each 15-week course combines evening lectures at our Cecil Street facility with weekend hands-on labs. Cohorts of 12-16 students work on progressively complex projects using cloud infrastructure and industry-standard tooling. Instructors are practicing ML engineers providing real-world context for technical concepts. Course materials remain accessible for 12 months post-completion.
What infrastructure access do students receive?
Students receive cloud computing credits for AWS and GCP environments, GPU instance access for training workloads, and shared cluster resources for distributed computing exercises. The learning environment mirrors production setups used by Singapore technology companies. Lab infrastructure supports concurrent model training, allowing students to experiment with scaling patterns and optimization techniques.
What payment options are available?
Course fees can be paid in full upon enrollment or through installment plans spread across the course duration. Singapore Citizens and Permanent Residents may qualify for SkillsFuture credit application. Corporate training accounts are accepted for company-sponsored students. All fees include infrastructure access, course materials, and project review sessions.
How is student progress evaluated?
Assessment focuses on functional implementations rather than examinations. Each course includes three major projects evaluated on system performance, code quality, and documentation completeness. Final projects involve deploying complete ML pipelines meeting specified latency and throughput requirements. Students present technical architecture decisions to instructor panels comprising practicing engineers.
What happens with student project data?
Project work uses synthetic datasets or public benchmarks avoiding sensitive information. Students retain full ownership of code and models developed during courses. Infrastructure logs are maintained for troubleshooting but contain no personally identifiable information. Data handling practices align with Singapore's Personal Data Protection Act requirements for educational institutions.
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