ML Platforms and Frameworks

ML Platforms and Frameworks #

Training at scale requires more than dividing data between GPUs. A platform must decide where model state lives, who computes gradients, how updates are combined, and how information moves between devices.

This page focuses on the Parameter Server model and the practical constraints that determine whether adding resources makes training faster.

Topics covered:

• Parameter servers, workers, and independent scaling of storage and computation
• Training-state memory and parameter sharding
• Distributed loss, gradient aggregation, and the pull–compute–push cycle
• Communication costs, RDMA, MPI, PCIe, and NVLink
• Global batch size, learning-rate scaling, and warm-up
• Monitoring, fault recovery, and sparse versus dense workloads

Learning Objectives #

By the end of this page, you should be able to:

Value-Based DRL: Function Approximation

Value-Based DRL: Function Approximation #

Tabular methods store a separate value for every state or state-action pair. This becomes impractical when the state space is very large, continuous or represented by raw observations. Function approximation replaces this table with a parameterised function that can learn from observed states, generalise to related states and predict values for states not seen during training.

Course Content covered in this module:

• Function approximation
• Feature Construction for Linear Methods:
  • Tile Coding
  • Asymmetric Tile Coding
• Linear function approximation
• Semi-Gradient TD methods
• Off-policy function approximation and TD divergence
• TD-Gammon

Learning Objectives #

By the end of this module, you should be able to:

Distributed Deep Learning: Synchronisation and Communication

Distributed Deep Learning: Synchronisation and Communication #

Distributed training divides computation between workers, but those workers rarely finish together. A training system must decide when to update the model, how much outdated information to accept, and how to reduce communication.

Relevant themes from the content outline:

• Asynchronous parallelism and parameter staleness
• Effects on convergence and variance
• System and program optimisation for neural networks
• Communication–computation trade-offs

The Parameter Server model provides the architectural foundation. Here, the focus is on the policies that determine its performance.

Ensemble Learning

Ensemble Learning #

Ensemble Learning is a machine learning approach where we combine multiple models to produce a stronger final prediction.

Instead of depending on one model, an ensemble uses a group of models and combines their outputs.

The main idea is simple:

Many weak or moderately good models can work together to produce a better and more stable model.

Key takeaway:
Ensemble Learning improves prediction by combining several models.

ML System Optimisation

ML System Optimisation #

ML System Optimisation studies how to make machine learning workloads faster, more scalable, more memory-efficient, and suitable for different hardware platforms.

The subject connects machine learning algorithms with the systems that train and deploy them: multi-core CPUs, GPUs, distributed clusters, cloud platforms, edge devices, and embedded systems.

ML system optimisation = model quality + computational efficiency + hardware awareness + scalability

The learning path begins with performance measurement and parallel computing, progresses through distributed machine learning and scale-out platforms, and concludes with model compression and resource-constrained deployment.

Unsupervised Learning

Unsupervised Learning #

Unsupervised Learning is used when we have input data but no target labels.

The model is not told the correct answer. Instead, it tries to discover hidden structure in the data.

• K-means Clustering and variants
• Review of EM algorithm
• GMM based Soft Clustering
• Applications

Supervised vs Unsupervised Learning #

• Works on unlabelled raw data.
• The algorithm discovers hidden patterns without prior knowledge of outcomes.
• Requires no human intervention during training.
• Does not make direct predictions — it groups or organises data instead.
• Carries a higher risk because there’s no ground truth to verify results.
• Common techniques include Clustering, Association, and Dimensionality Reduction.

The most common example is clustering, where similar records are grouped together.

Evaluation/Comparison

Machine Learning Model Evaluation/Comparison #

Comparing Machine Learning Models #

Emerging requirements e.g., bias, fairness, interpretability of ML models #

Home | Machine Learning