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Switch from Backend to AI/ML Engineering
Leverage your systems expertise to land a production AI/ML engineering role in 90 days.
CREATED BY
S
Sneha T. [PLACEHOLDER] ★ 4.9
Business Analyst Lead at ConsultPro | 8+ years of experience
About this Path
Built for senior backend engineers (Java, Python, Go) who already ship distributed systems and want to pivot into AI/ML roles without starting from scratch. The roadmap skips basics and focuses on the delta: ML fundamentals, model deployment, MLOps, and the system-design interview patterns that differentiate ML engineers from data scientists. You leave with a deployed end-to-end ML project and vocabulary to pass staff-level ML system design rounds.
Path Overview
Advanced LevelCertificate of CompletionAbout 72 hours to completeEnglish language22+ curated videosLearn online at your own pace6 modules with resourcesGamified & interactive
Path Curriculum
Supervised vs. Unsupervised: What Actually Matters
Loss functions, bias-variance tradeoff, and when to use which algorithm class.
scikit-learn & XGBoost Crash Course
Train, evaluate, and tune gradient-boosted trees on a real tabular dataset end-to-end.
PyTorch Essentials: Tensors, Autograd, and Training Loops
Build a feed-forward network from scratch; understand backprop at the code level.
Evaluation Metrics That Hiring Managers Ask About
Precision/recall, AUC-ROC, NDCG, and calibration — when each metric is the right one.
Feature Engineering Patterns
Encoding, scaling, embeddings, and time-series lag features with pandas and Polars.
Feature Stores with Feast
Define, materialize, and serve features offline and online; avoid training-serving skew.
Building Reliable Training Pipelines with Airflow
DAG design, idempotency, and data-quality checks before model training runs.
Handling Data Drift at the Source
Schema validation with Great Expectations; alert before drift corrupts your model.
Distributed Training with Ray Train
Shard datasets and run multi-GPU training jobs without a PhD in CUDA.
Experiment Tracking with MLflow
Log parameters, metrics, and artifacts; compare runs; promote models to staging.
Hyperparameter Tuning with Optuna
Bayesian optimization strategies that beat grid search in a fraction of the compute.
LLM Fine-Tuning with LoRA on a Budget
Apply parameter-efficient fine-tuning to a 7B model using Hugging Face PEFT.
FastAPI Inference Services: Latency and Throughput Patterns
Async handlers, request batching, and model warm-up to hit p99 < 50ms SLOs.
Containerizing Models with Docker and Triton Inference Server
Package models as OCI images; use Triton for multi-model, multi-framework serving.
Deploying on Kubernetes with KServe
InferenceService CRDs, autoscaling on GPU metrics, and canary rollout strategies.
Vector Databases and Embedding Search
Integrate Pinecone or Weaviate for semantic search and RAG retrieval pipelines.
Monitoring Models in Production with Evidently AI
Track data drift, concept drift, and prediction distribution shift with dashboards.
Automated Retraining Pipelines
Trigger retraining on drift signals using Airflow + MLflow model registry promotions.
Shadow Mode and Champion-Challenger Deployments
Route traffic safely to new models; measure business KPI lift before full rollout.
The ML System Design Framework
Problem scoping, data requirements, model choice, serving architecture, monitoring — in 45 min.
Designing a Real-Time Recommendation Engine
Two-tower retrieval, ANN index, feature freshness tradeoffs, and fallback strategies.
Designing a Fraud Detection Pipeline
Near-real-time inference, class imbalance, feedback loops, and regulatory constraints.
Translating Backend Wins into ML Portfolio Stories
Map distributed-systems experience to ML infra credibility in behavioral interviews.
What you'll learn
- ✓Build and train supervised and unsupervised models using scikit-learn, XGBoost, and PyTorch without re-learning Python basics.
- ✓Design and implement feature stores, training pipelines, and batch/real-time inference services using tools like Feast, Ray, and FastAPI.
- ✓Deploy ML models to production using Docker, Kubernetes, and SageMaker or Vertex AI with A/B traffic splitting.
- ✓Instrument models with data-drift detection, latency SLOs, and automated retraining triggers using Evidently AI and Airflow.
- ✓Articulate ML system design tradeoffs (embedding stores, serving latency vs. freshness, shadow mode vs. champion-challenger) in staff-level interviews.
- ✓Reframe your backend portfolio into an ML engineering narrative that resonates with hiring managers at AI-first companies.