Scalability & Systems

This section explores the importance of scalability in machine learning systems, focusing on their structural and operational aspects to handle increasing workloads effectively.

Understanding Scalability In Machine Learning

Scalability in machine learning refers to a system's capability to manage increased workloads through the addition of resources, emphasizing efficient system design for large datasets.

Large-Scale Data Processing Frameworks

This section covers the importance and methodologies of large-scale data processing frameworks, focusing on MapReduce and Apache Spark.

Mapreduce

MapReduce is a programming model designed to process large datasets through distributed algorithms, optimizing data handling for efficiency.

Apache Spark

Apache Spark is a powerful in-memory data processing engine that excels in speed and provides rich APIs for various types of data processing tasks.

Distributed Machine Learning

Distributed machine learning involves parallel computing techniques to handle large models and datasets by distributing computing tasks across multiple nodes.

Data Parallelism

Data parallelism involves splitting data across multiple nodes where each node processes a mini-batch to update model parameters.

Model Parallelism

Model parallelism enables the distribution of a machine learning model across multiple nodes, making it feasible to train larger models that exceed the memory capacity of a single machine.

Parameter Server Architecture

The section on Parameter Server Architecture explains the design of a centralized or sharded system that manages model parameters during distributed machine learning.

Systems For Scalable Training

This section discusses various systems and techniques that facilitate scalable training of machine learning models, focusing on the use of GPUs, TPUs, and federated learning.

Gpu And Tpu Acceleration

This section discusses GPU and TPU acceleration, their respective roles in deep learning, and the challenges faced in scalability.

Federated Learning

Federated learning enables model training on edge devices while safeguarding user data privacy by sharing only gradients instead of raw data.

Online And Streaming Learning

This section discusses online and streaming learning in machine learning, focusing on incremental model updates and the frameworks that support real-time data processing.

Online Learning

Online learning refers to the incremental updating of machine learning models as new data is introduced.

Streaming Frameworks

This section discusses streaming frameworks like Apache Kafka and Apache Flink/Spark Streaming for processing real-time data efficiently in machine learning applications.

Scalable Model Deployment And Inference

This section covers techniques and architectures for deploying machine learning models effectively and efficiently at scale.

Model Serving Architectures

This section discusses various architectures for serving machine learning models, focusing on batch and real-time inference methods.

Load Balancing And Autoscaling

Load balancing and autoscaling are techniques used to optimize resource usage in machine learning model deployment by distributing requests and dynamically adjusting resource capacity.

A/b Testing And Canary Deployments

This section outlines A/B testing and canary deployments as strategies for assessing and implementing new machine learning models in production environments.

Scalable Data Storage And Management

This section explores scalable data storage solutions, focusing on data lakes, data warehouses, and feature stores.

Data Lakes And Warehouses

Data Lakes store raw unstructured data, while Data Warehouses are optimized for query and analytics.

Feature Stores

Feature stores serve as a central repository for storing, reusing, and serving machine learning features to enhance model development and deployment efficiency.

Monitoring, Logging, And Reliability

This section discusses the importance of monitoring and logging in machine learning models to ensure reliability and fault tolerance.

Case Studies In Scalable Ml Systems

This section explores real-world applications of scalable ML systems through two prominent case studies: Google’s TFX and Uber’s Michelangelo.

Google’s Tfx (Tensorflow Extended)

Google's TFX is an end-to-end machine learning pipeline framework designed to facilitate the entire ML workflow.

Uber’s Michelangelo

Uber’s Michelangelo is an internal ML platform that emphasizes automated processes for training, deploying, and feature engineering at scale.