MLOps/LLMOps Course
Evaluation: Model Benchmarks and LLM Application Assessment
LLMOps Part 10: Understanding model benchmarks, LLM application evaluation, and tooling.
Akshay Pachaar
69 posts published
MLOps/LLMOps Course
Evaluation: Fundamentals
LLMOps Part 9: A foundational guide to the evaluation of LLM applications, covering challenges and a practical taxonomy of evaluation methods.
MLOps/LLMOps Course
Context Engineering: Memory and Temporal Context
LLMOps Part 8: A concise overview of memory, dynamic and temporal context in LLM systems, covering short and long-term memory, dynamic context injection, and some of the common context failure modes in agentic applications.
MLOps/LLMOps Course
Context Engineering: An Introduction to the Information Environment for LLMs
LLMOps Part 7: A conceptual overview of context engineering, covering context types, context construction principles, and retrieval-centric techniques for building high-signal inputs.
MLOps/LLMOps Course
Context Engineering: Prompt Management, Defense, and Control
LLMOps Part 6: Exploring prompt versioning, defensive prompting, and techniques such as verbalized sampling, role prompting and more.
MLOps/LLMOps Course
Context Engineering: Foundations, Categories, and Techniques of Prompt Engineering
LLMOps Part 5: An introduction to prompt engineering (a subset of context engineering), covering prompt types, the prompt development workflow, and key techniques in the field.
MLOps/LLMOps Course
Building Blocks of LLMs: Decoding, Generation Parameters, and the LLM Application Lifecycle
LLMOps Part 4: An exploration of key decoding strategies, sampling parameters, and the general lifecycle of LLM-based applications.
MLOps/LLMOps Course
Building Blocks of LLMs: Attention, Architectural Designs and Training
LLMOps Part 3: A focused look at the core ideas behind attention mechanism, transformer and mixture-of-experts architectures, and model pretraining and fine-tuning.
MLOps/LLMOps Course
Building Blocks of LLMs: Tokenization and Embeddings
LLMOps Part 2: A detailed walkthrough of tokenization, embeddings, and positional representations, building the foundational translation layer that enables LLMs to process and reason over text.
AI Agents Course
A Practical Deep Dive Into Memory Optimization for Agentic Systems (Part C)
AI Agents Crash Course—Part 17 (with implementation).
AI Agents Course
A Practical Deep Dive Into Memory Optimization for Agentic Systems (Part B)
AI Agents Crash Course—Part 16 (with implementation).
MLOps/LLMOps Course
Foundations of AI Engineering and LLMs
LLMOps Part 1: An overview of AI engineering and LLMOps, and the core dimensions that define modern AI systems.
LLM and Fine-tuning
A Practical Guide to Integrate Evaluation and Observability into LLM Apps
A comprehensive guide to Opik, an open-source LLM evaluation and observability framework.
MLOps/LLMOps Course
CI/CD Workflows
MLOps Part 18: A hands-on guide to CI/CD in MLOps with DVC, Docker, GitHub Actions, and GitOps-based Kubernetes delivery on Amazon EKS.
MLOps/LLMOps Course
Monitoring and Observability: Practical Tooling with Evidently, Prometheus, and Grafana
MLOps Part 17: ML monitoring in practice with Evidently, Prometheus and Grafana, stitched into a FastAPI inference service with drift reports, metrics scraping, and dashboards.
AI Agents Course
A Practical Deep Dive Into Memory Optimization for Agentic Systems (Part A)
AI Agents Crash Course—Part 15 (with implementation).
MLOps/LLMOps Course
Monitoring and Observability: Core Fundamentals
MLOps Part 16: A comprehensive overview of drift detection using statistical techniques, and how logging and observability keep ML systems healthy.
MLOps/LLMOps Course
Model Deployment: EKS Lifecycle and Model Serving
MLOps Part 15: Understanding the EKS lifecycle, getting hands-on with AWS setup, and deploying a simple ML inference service on Amazon EKS.
MLOps/LLMOps Course
Model Deployment: Introduction to AWS
MLOps Part 14: Understanding AWS cloud platform, and zooming into EKS.
MLOps/LLMOps Course
Model Deployment: Cloud Fundamentals
MLOps Part 13: An overview of cloud concepts that matter, from virtualization and storage choices to VPC, load balancing, identity, and observability.
MLOps/LLMOps Course
Model Deployment: Kubernetes
MLOps Part 12: An introduction to Kubernetes, plus a practical walkthrough of deploying a simple FastAPI inference service using Kubernetes.
MLOps/LLMOps Course
Model Deployment: Serialization, Containerization and API for Inference
MLOps Part 11: A practical guide to taking models beyond notebooks, exploring serialization formats, containerization, and serving predictions using REST and gRPC.
MLOps/LLMOps Course
Model Development and Optimization: Compression and Portability
MLOps Part 10: A comprehensive guide to model compression covering knowledge distillation, low-rank factorization, and quantization, followed by ONNX and ONNX Runtime as the bridge from training frameworks to fast, portable production inference.
MLOps/LLMOps Course
Model Development and Optimization: Fine-Tuning, Pruning, and Efficiency
MLOps Part 9: A deep dive into model fine-tuning and compression, specifically pruning and related improvements.
MLOps/LLMOps Course
Model Development and Optimization: Fundamentals of Development and Hyperparameter Tuning
MLOps Part 8: A systems-first guide to model development and optimizing performance with disciplined hyperparameter tuning.
MLOps/LLMOps Course
Data and Pipeline Engineering: Distributed Processing and Workflow Orchestration
MLOps Part 7: An applied look at distributed data processing with Spark and workflow orchestration and scheduling with Prefect.
MLOps/LLMOps Course
Data and Pipeline Engineering: Sampling, Data Leakage, and Feature Stores
MLOps Part 6: A deep dive into sampling, class imbalance, and data leakage; plus a hands-on Feast feature store demo.
MLOps/LLMOps Course
Data and Pipeline Engineering: Data Sources, Formats, and ETL Foundations
MLOps Part 5: A detailed walkthrough of data engineering for MLOps, covering data sources, format performance trade-offs, and ETL/ELT pipelines.
MLOps/LLMOps Course
Reproducibility and Versioning in ML Systems: Weights and Biases for Reproducible ML
MLOps Part 4: A practical walkthrough of W&B-powered reproducibility.
MLOps/LLMOps Course
Reproducibility and Versioning in ML Systems: Fundamentals of Repeatable and Traceable Setups
MLOps Part 3: A practical exploration of reproducibility and versioning, covering deterministic training, data and model versioning, and experiment tracking.
