Best MLOps & LLMOps in
Hyderabad
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MLOps & LLMOps Syllabus in Hyderabad
Curriculum co-designed with engineering leads from tier-1 MNCs. Click each module below to view detailed topics, live coding projects, and industry tools.
01
ML Lifecycle, Reproducibility & Data Version Control (DVC)
14 Hours
ML Lifecycle, Reproducibility & Data Version Control (DVC)
Establish rigorous engineering hygiene for Machine Learning code, data, and model artifacts.
Core Technical Topics & Competencies Covered:
- ✔The MLOps Maturity Model: From Manual Scripts to Automated CI/CD
- ✔Data Version Control (DVC): Tracking Gigabytes of Datasets with Git Integration
- ✔Remote Storage Configuration: S3, GCS, Azure Blob with DVC
- ✔DVC Pipelines: Reproducible DAGs with dvc.yaml and Parameter Tracking
- ✔Setting up Production Python Environments with Poetry, Conda & Pre-Commit Hooks
02
Experiment Tracking & Model Registry with MLflow
16 Hours
Experiment Tracking & Model Registry with MLflow
Track thousands of model training runs, parameters, metrics, and manage model lifecycle states.
Core Technical Topics & Competencies Covered:
- ✔MLflow Architecture: Tracking Server, Artifact Store & Backend DB (PostgreSQL)
- ✔Logging Hyperparameters, Loss Curves, Precision-Recall & Model Artifacts
- ✔MLflow Autologging with PyTorch, Scikit-Learn, and XGBoost
- ✔MLflow Model Registry: Staging, Production, and Archived State Transitions
- ✔Model Packaging: MLflow Models, PyFunc Flavors & Conda/Docker Environments
03
Containerization & Kubernetes for Machine Learning
16 Hours
Containerization & Kubernetes for Machine Learning
Package models into microservices and deploy scalable clusters with Docker and Kubernetes.
Core Technical Topics & Competencies Covered:
- ✔Dockerfiles for ML: Multi-Stage Builds, CUDA Base Images, Minimizing Image Sizes
- ✔FastAPI Model Serving Containers with Gunicorn & Uvicorn Workers
- ✔Kubernetes Foundations: Pods, Deployments, Services, ConfigMaps, Secrets
- ✔NVIDIA GPU Operator for Kubernetes: Enabling GPU Acceleration in Clusters
- ✔Horizontal Pod Autoscaling (HPA) based on Request Volume and GPU Utilization
04
Pipeline Orchestration with Kubeflow & Apache Airflow
16 Hours
Pipeline Orchestration with Kubeflow & Apache Airflow
Automate end-to-end continuous training, model validation, and automated deployment pipelines.
Core Technical Topics & Competencies Covered:
- ✔Kubeflow Pipelines (KFP): Building Containerized Components & Pipeline DSL
- ✔Passing Artifacts, Datasets, and Metrics Between Pipeline Steps
- ✔Apache Airflow for Data-to-ML Scheduled Orchestration
- ✔Automated Model Evaluation Gates: Promoting Models only if Metrics Exceed Production Baseline
- ✔Continuous Training (CT) Triggers: Scheduling vs Data Drift Triggers
05
LLMOps: High-Throughput Serving with vLLM & Triton
14 Hours
LLMOps: High-Throughput Serving with vLLM & Triton
Scale generative AI foundation models with state-of-the-art inference engines.
Core Technical Topics & Competencies Covered:
- ✔Inference Challenges: Memory Bandwidth vs Compute Bound, KV-Cache Inflation
- ✔vLLM Architecture: PagedAttention, Tensor Parallelism & Continuous Batching
- ✔Triton Inference Server: Multi-Model Concurrency, Dynamic Batching & Ensembles
- ✔Quantized Model Serving: AWQ, FP8, INT4 Inference for Maximum Throughput
- ✔API Gateway Layer: Rate Limiting, Streaming SSE Responses & Load Balancing
06
LLM Observability, Prompt Management & Cost Governance
14 Hours
LLM Observability, Prompt Management & Cost Governance
Monitor LLM token usage, latency, prompt drift, and trace multi-step reasoning chains in production.
Core Technical Topics & Competencies Covered:
- ✔LLMOps Observability: Langfuse, Arize Phoenix & OpenTelemetry Tracing
- ✔Prompt Versioning, Testing & Staging in Production Gateways
- ✔Semantic Prompt Caching with Redis to cut API Costs by 40-70%
- ✔Detecting Data Drift, Concept Drift & Model Performance Degradation
- ✔CI/CD for AI using GitHub Actions: Automated Linting, Unit Testing & Docker Registry Push
Where Our Hyderabad Students Get Hired
Over 500+ global enterprises and innovative unicorns hire directly from 4Achievers programs.
Frequently Asked Questions (Hyderabad)
Everything you need to know about fees, batch timings, Career Guidance & Interview Prep, and prerequisites.
Why choose 4Achievers in Hyderabad for MLOps & LLMOps in Hyderabad?
4Achievers provides 1-on-1 Mock Interview SessionsDedicated Support, dedicated offline labs with high-speed development machines, real-world live projects, and personal 1-on-1 mentor guidance from working senior engineers.
What is the course duration and batch schedule?
The program spans 12 Weeks with both weekday regular batches (Mon-Fri) and weekend tracks (Sat-Sun) specifically designed for working professionals and university students.
Is there 1-on-1 Mock Interview SessionsDedicated Support provided in Hyderabad?
Yes! Our dedicated Corporate Career Mentorship Desk coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.