Best MLOps & LLMOps in
Varanasi
Accelerate your engineering career with 100% placement assurance, high-tech offline laboratory training, 90+ Hours+ hours of intensive live projects, and 1-on-1 mentorship from top architects.
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4Achievers Varanasi Tech Campus
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MLOps & LLMOps Syllabus in Varanasi
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:
- โThe MLOps Maturity Model: From Manual Scripts to Automated CI/CD
- โData Version Control (DVC): Tracking Gigabytes of Datasets with Git Integration
- โRemote Storage Configuration: S3, GCS, Azure Blob with DVC
- โDVC Pipelines: Reproducible DAGs with dvc.yaml and Parameter Tracking
- โSetting 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:
- โMLflow Architecture: Tracking Server, Artifact Store & Backend DB (PostgreSQL)
- โLogging Hyperparameters, Loss Curves, Precision-Recall & Model Artifacts
- โMLflow Autologging with PyTorch, Scikit-Learn, and XGBoost
- โMLflow Model Registry: Staging, Production, and Archived State Transitions
- โModel 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:
- โDockerfiles for ML: Multi-Stage Builds, CUDA Base Images, Minimizing Image Sizes
- โFastAPI Model Serving Containers with Gunicorn & Uvicorn Workers
- โKubernetes Foundations: Pods, Deployments, Services, ConfigMaps, Secrets
- โNVIDIA GPU Operator for Kubernetes: Enabling GPU Acceleration in Clusters
- โHorizontal 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:
- โKubeflow Pipelines (KFP): Building Containerized Components & Pipeline DSL
- โPassing Artifacts, Datasets, and Metrics Between Pipeline Steps
- โApache Airflow for Data-to-ML Scheduled Orchestration
- โAutomated Model Evaluation Gates: Promoting Models only if Metrics Exceed Production Baseline
- โContinuous 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:
- โInference Challenges: Memory Bandwidth vs Compute Bound, KV-Cache Inflation
- โvLLM Architecture: PagedAttention, Tensor Parallelism & Continuous Batching
- โTriton Inference Server: Multi-Model Concurrency, Dynamic Batching & Ensembles
- โQuantized Model Serving: AWQ, FP8, INT4 Inference for Maximum Throughput
- โAPI 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:
- โLLMOps Observability: Langfuse, Arize Phoenix & OpenTelemetry Tracing
- โPrompt Versioning, Testing & Staging in Production Gateways
- โSemantic Prompt Caching with Redis to cut API Costs by 40-70%
- โDetecting Data Drift, Concept Drift & Model Performance Degradation
- โCI/CD for AI using GitHub Actions: Automated Linting, Unit Testing & Docker Registry Push
Where Our Varanasi Students Get Hired
Over 500+ global enterprises and innovative unicorns hire directly from 4Achievers programs.
Frequently Asked Questions (Varanasi)
Everything you need to know about fees, batch timings, placement support, and prerequisites.
Why choose 4Achievers for MLOps & LLMOps in Varanasi?
4Achievers provides 100% placement assurance, 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 100% placement support provided in Varanasi?
Yes! Our dedicated Corporate Placement Cell coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.