SB
Spandana Bala
AI/Data Scientist
Hyderabad · India
Now at · Microsoft
AI & Data Science · ML · Deep learning · LLMs · Agentic analytics · Enrolling now
AI & Data Science
A career-focused, hands-on program: build strong Python, SQL, statistics and machine learning foundations, then specialise in deep learning, LLMs and agentic analytics — shipping models and AI features with MLOps, evaluation and monitoring, using the AI-assisted data tools employers now expect.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Edify Nuva centres
Where our Data Science alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the AI & Data Science program?
A data scientist turns data into decisions and models into products — framing the question, engineering the data, training and validating models, and now building the LLM and agent features that sit on top of them. Most data science courses end at a notebook. This program ends only when you have shipped a model to production with MLOps and monitoring, built an LLM-powered analytics feature with evals, and presented the business result to stakeholders.
The complete delivery chain
Twelve links, one owner — end to end.
01FRAME & PREPARE
- •Business question, hypothesis & success metric
- •SQL, pandas & Polars data engineering
- •EDA, statistics & experiment design
- •Feature engineering & data quality
02MODEL
- •Supervised & unsupervised learning
- •Deep learning, NLP & computer vision
- •LLMs, RAG & agentic analytics
- •Validation, explainability & fairness
03SHIP & COMMUNICATE
- •MLflow, serving & feature stores
- •Evals, drift & monitoring
- •Dashboards, storytelling & A/B tests
- •AI-assisted workflows with Claude Code & Cursor
Data science in 2026
Notebooks got agents. Data science became AI product work.
AI-assisted data scienceWorkflow shift
Claude Code, Cursor, Jupyter AI and Copilot write the boilerplate — cleaning, plotting, first-pass models. Data scientists now direct and review, and spend the saved time on problem framing, validation and communication.
LLMs as a modelling toolModel layer
Frontier and open-weight models handle text, documents and images that used to need bespoke NLP; fine-tuning, embeddings and structured outputs sit alongside XGBoost in the toolbox.
Agentic analytics & data agentsNew '26
Text-to-SQL and analysis agents, semantic layers (dbt, Tableau Knowledge, Snowflake Cortex, Databricks Genie) and MCP connections let stakeholders ask questions directly — data scientists build and govern those agents.
Lakehouse & modern data stackData layer
Snowflake, Databricks, BigQuery, dbt, Polars and DuckDB — with zero-copy sharing into platforms like Salesforce Data 360. SQL and data engineering fluency remain the foundation.
MLOps & evaluationReliability layer
MLflow, feature stores, model serving, drift monitoring and LLM evals with LangSmith, Braintrust or Evidently — the discipline that gets models past the proof of concept.
Responsible AI & governanceTrust layer
Fairness, explainability, privacy and the EU AI Act, NIST AI RMF and ISO 42001 — what enterprises now audit before a model or agent goes live.
What this means for your career: data science roles now ask for LLM and agent skills, MLOps and business communication alongside statistics and ML — the differentiator is a deployed model and an evaluated AI feature with a measured business result, not a folder of notebooks.
Who should join
Built for people moving into data science and applied AI.
Graduates in engineering, maths, stats or commerce
Data & business analysts
Software developers moving to ML
Finance, marketing & operations professionals
Researchers & academics
Career switchers into AI
Prior experience: none required — Python, SQL and statistics are taught from scratch. The program builds data foundations before machine learning, deep learning, LLMs and MLOps.
What you will be able to do
Build the model — and ship the decision.
Engineer data with Python & SQLpandas, Polars, PostgreSQL, dbt basics and clean, reproducible pipelines.
Reason with statisticsProbability, inference, hypothesis tests, A/B experiments and causal thinking.
Build and validate ML modelsscikit-learn, XGBoost, feature engineering, cross-validation, explainability and fairness.
Apply deep learning & LLMsPyTorch, transformers, fine-tuning, embeddings, RAG and structured outputs.
Build agentic analyticsText-to-SQL and analysis agents with semantic layers, MCP and evals.
Ship and communicateMLflow, serving, drift monitoring, Power BI dashboards and stakeholder storytelling.
Course curriculum
Twelve sections. 53 modules. Python → SQL → Statistics → ML → Deep learning → LLMs → Ship.
01
Fundamentals of Data & AI
How data, analytics, machine learning and AI fit together — and where data scientists work in the 2026 stack.
Analytics, data science, ML engineering and AI engineering roles
Descriptive, predictive, prescriptive and generative
The modern data stack — lakehouse, dbt, BI, ML platforms
Career pathways and certifications
From rules to statistics to neural networks to LLMs
Training, validation and generalisation
Bias, variance and overfitting
What LLMs can and cannot do for data work
Python, uv, Jupyter and VS Code setup
Git and GitHub for data projects
Claude Code, Cursor and Jupyter AI as pair programmers
Reviewing AI-generated analysis
Privacy, consent and PII
Fairness and bias in data
EU AI Act, NIST AI RMF and ISO 42001 overview
Documentation and reproducibility
02
Python for Data Science
Write Python the way data teams write it — clean, tested and reproducible.
Types, control flow, functions and comprehensions
Modules, packages and environments
Files, JSON and APIs
Error handling
Classes, dataclasses and typing
Decorators, generators and iterators
Functional patterns for data pipelines
Code organisation
Arrays, broadcasting and vectorisation
Linear algebra operations
Random numbers and simulation
Performance basics
DataFrames, indexing and joins
Group-by, reshaping and time series
Polars and DuckDB for larger data
Cleaning and validation patterns
pytest for data code
Data validation with Pandera and Great Expectations
Cookiecutter project structure
Documentation
03
SQL & Data Engineering Essentials
Get the data — correctly, at scale and repeatably.
SELECT, JOIN, GROUP BY and window functions
CTEs and subqueries
Indexes and query performance
Data modelling basics
Window functions and cohort analysis
Date and time logic
Analytical patterns — funnels, retention, sessionisation
Text-to-SQL and reviewing generated queries
Snowflake, BigQuery and Databricks
dbt for transformations and semantic models
Zero-copy sharing and Data 360 style CDPs
Cost awareness
Batch pipelines with Python and SQL
Airflow and Dagster basics
Data quality checks in pipelines
Scheduling and monitoring
04
Statistics & Experimentation
The reasoning that separates data scientists from dashboard builders.
Probability rules and Bayes
Common distributions
Expectation, variance and the CLT
Simulation with NumPy
Summaries, outliers and missing data
Visualisation with Matplotlib, Seaborn and Plotly
EDA workflows with AI assistance
Communicating findings
Confidence intervals and p-values
t-tests, chi-square and non-parametric tests
Multiple comparisons
Bayesian thinking
Randomisation and power analysis
Metrics, guardrails and peeking
Sequential and Bayesian testing
Reporting experiment results
Correlation vs causation
Difference-in-differences and regression discontinuity
Uplift modelling
When observational data misleads
05
Machine Learning
Supervised and unsupervised learning done properly — validated, explained and fair.
Problem framing and metrics
Train / validation / test and cross-validation
Pipelines and preprocessing
Baselines first
Linear and logistic regression
Regularisation
Decision trees and k-NN
Evaluation metrics and thresholds
Bagging vs boosting
XGBoost, LightGBM and CatBoost
Hyperparameter tuning with Optuna
Handling imbalance
Encoding, scaling and binning
Time-based and aggregate features
Feature selection
Leakage detection
k-means, hierarchical and DBSCAN
PCA, UMAP and t-SNE
Anomaly detection
Customer segmentation
Decomposition and stationarity
ARIMA, Prophet and gradient boosting for series
Foundation models for forecasting
Backtesting
SHAP and permutation importance
Fairness metrics and mitigation
Model cards and documentation
Explaining models to stakeholders
06
Deep Learning & Computer Vision
Neural networks from first principles to PyTorch in production.
Perceptrons, activations and backpropagation
Loss functions and optimisers
Building a network from scratch
Regularisation and normalisation
Tensors, autograd and modules
Datasets, DataLoaders and training loops
GPU training and mixed precision
Experiment tracking
CNN architectures
Transfer learning and fine-tuning
Object detection and segmentation (overview)
Vision-language models
RNNs, LSTMs and attention
Transformer architecture
Hugging Face Transformers
Embeddings
07
NLP & Large Language Models
From classical NLP to frontier models — as a data scientist uses them.
Tokenisation, TF-IDF and topic models
Text classification and NER
spaCy and scikit-learn text pipelines
Evaluation for text tasks
Anthropic, OpenAI, Google and open-weight models
Prompting, structured outputs and tool calling
Batch processing and cost control
Zero-shot and few-shot classification
Embedding models and vector databases
Semantic search and clustering
RAG pipelines on documents
Evaluating retrieval
When to fine-tune vs prompt vs RAG
LoRA and PEFT with Hugging Face
Distillation to small models
Evaluating fine-tuned models
Golden datasets and rubrics
LLM-as-judge
Regression suites
LangSmith, Braintrust and promptfoo
08
Agentic Analytics & Data Agents
Build the agents that let stakeholders ask the data directly — and govern them.
Databricks Genie, Snowflake Cortex, Tableau Knowledge and Power BI Copilot
Semantic layers as the foundation
Where data scientists build vs configure
Governance and trust
Schema-aware prompting and semantic layers
Validation, guardrails and read-only execution
Evaluating text-to-SQL accuracy
Deploying to stakeholders
Agents that run EDA, statistics and charts
Claude Agent SDK and LangGraph for analysis workflows
Human-in-the-loop review
Automated insight reports
Model Context Protocol basics
Exposing warehouses, dbt and BI as MCP tools
Connecting Claude Code and Cursor to data
Security and permissions
09
MLOps & Model Deployment
Get models out of notebooks and keep them healthy.
MLflow tracking and registry
Reproducible training runs
Model versioning
Weights & Biases (overview)
FastAPI model services
BentoML and cloud endpoints — SageMaker, Vertex, Azure ML
Batch vs real-time inference
Containers and deployment
Feature store concepts — Feast
Training-serving skew
Scheduled retraining
Data contracts
Data and concept drift with Evidently
Performance monitoring and alerts
Retraining triggers
Incident handling
AWS, Azure and GCP ML services
Databricks and Snowflake ML
Cost management
Choosing a platform
10
Analytics, BI & Data Storytelling
Turn results into decisions people act on.
Data modelling and Power Query
DAX and time intelligence
Row-level security and publishing
Power BI Copilot
Chart selection and design principles
Plotly, Streamlit and Tableau Next basics
Interactive dashboards
Accessibility
Structuring an insight narrative
Executive summaries and decision memos
Presenting uncertainty
Handling pushback
KPI trees and north-star metrics
Unit economics and forecasting for business
Decision frameworks
Measuring impact of models
11
Responsible AI, Governance & Privacy
What enterprises audit before a model or agent goes live.
Bias audits across the lifecycle
Mitigation techniques
Explainability for regulators
Documentation
GDPR, DPDP and CCPA in data science
Anonymisation and differential privacy basics
Access control and data classification
Secure handling of PII in LLM workflows
EU AI Act obligations
NIST AI RMF and ISO 42001
Model risk management in finance and health
Evidence packs
12
Capstone, Portfolio & Career
Two production deliverables, a verifiable portfolio and interview readiness.
Business framing, data pipeline and EDA
Model with validation, explainability and fairness report
MLflow, serving and drift monitoring
Dashboard and stakeholder presentation with a public verification URL
Text-to-SQL or analysis agent on a semantic layer
RAG or fine-tuned component with evals
Guardrails and governance notes
Demo and write-up
GitHub portfolio with READMEs and dashboards
Resume rewrite around shipped models and business impact
Data science interview practice — SQL, statistics, ML case studies
Warm introductions to hiring partners
Tools you'll master
32+ ML, DL & AI tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project
Production ML system: train → serve → monitor
Build an end-to-end production ML pipeline — reproducible training, model serving, drift monitoring, and an LLM augmentation layer that explains predictions and answers analyst questions.
01Reproducible training pipeline — DVC-versioned data & features, MLflow experiments, Optuna hyperparameter search, Weights & Biases tracking.
02Model-serving stack — Triton or BentoML server packaging your model + ONNX runtime, with FastAPI gateway, autoscaling on Kubernetes.
03Drift + quality monitoring — Evidently dashboards for data & concept drift, alerting hooks into Slack, retraining triggers.
04LLM augmentation layer — a small Hugging Face pipeline + a LangChain RAG layer that explains predictions and answers analyst questions.
Outcome: AUC +12 pts vs baseline
p95 inference: <120ms
Reviewer: ML Engineering panel
PyTorchMLflowTritonEvidentlyLangChain
Enterprise
NLP & LLM fine-tuning
Train a domain-tuned transformer end-to-end — Hugging Face PEFT/LoRA fine-tuning, evaluation on golden datasets, ONNX export, served via Triton with token-level latency dashboards.
Hugging FacePEFTONNXTriton
Real-time
Real-time recommendation system
Build a candidate-generation + re-ranking recommender on Spark + Feast feature store, served on SageMaker / BentoML, with online evaluation and Evidently drift monitoring.
SparkFeastBentoMLSageMaker
Project
Your ML system in a controlled project environment.
Pick a real partner ML problem. Deploy a production system end-to-end — feature store, training pipeline, model serving, drift monitoring, LLM explanation layer — into a partner team that's running it for real users.
Download the real world project
Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Edify Nuva · AI & Data Science Architect
Python · PyTorch · TensorFlow · scikit-learn · Hugging Face · LangChain
"A 2026 data scientist doesn't stop at notebooks. They ship the training pipeline,
stand up the model behind FastAPI, monitor drift in production, and wire an LLM into the loop so the
business actually understands what the model is saying. That's the bar I teach to, every class."
15 yrs
AI & DATA SCIENCE
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Edify Nuva and brings 15 years of applied AI & data science from AT&T, Salesforce, Cox Communications, and Broadcom — where he led recommendation, fraud, forecasting, NLP and computer-vision systems for Fortune-500 banks, telcos, and insurers. Most recently he architected production ML pipelines that pair classical and deep models with an LLM augmentation layer that explains predictions to business stakeholders.
His classes get you two things other programs don't give you: a founding architect who still ships production ML, and a curriculum rewritten every quarter to match what hiring managers actually ask about — credentials like AWS Machine Learning Specialty, Azure AI Engineer, Databricks ML Associate, TensorFlow Developer, and Pragmatic AI Engineer included. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Edify Nuva · ML Engineering & MLOps Lead
PyTorch · MLflow · Triton · ONNX · Spark · MLOps · LLM Fine-tuning
"MLOps is where data science stops being a notebook and starts being a system —
reproducible training pipelines you can re-run a year later, model serving you can stake an SLA on, drift
monitoring that's quiet on purpose, and an LLM layer that explains decisions to the people who own the
business outcome. That's what I teach."
10 yrs
ML ENGINEERING
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Edify Nuva, where he leads the ML engineering and MLOps practice. After 8 years building and running production ML pipelines, he stepped into the Chief Technologist seat to wire MLflow, Triton, Evidently, and Hugging Face into the way ML teams actually work — feature stores that stay accurate through retrains, drift monitoring that filters noise before it hits humans, and serving stacks that on-call engineers don't fight with.
His MLOps modules are built from real production post-mortems, not slide decks. Expect to leave with working training pipelines, model serving on Triton/BentoML, drift dashboards in Evidently, and an LLM fine-tuning workflow you can stake an SLA on. Ten years at Edify Nuva, eight of them shipping production ML — Hyderabad-based, hands-on, and known for the unglamorous parts of data science that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What ML & data science employers say about Edify Nuva grads.
Real feedback from data and ML leaders at AI-first companies and the firms hiring our AI & Data Science graduates.
Edify Nuva grads ramp 40% faster on production ML deploys than typical data science hires. Best AI & Data Science pipeline in India.
Aakash Mehta, Engineering Director, Microsoft
We've onboarded 80+ Edify Nuva alumni in 18 months. Lowest ramp time we've seen for production ML systems and drift monitoring practices.
Anita Sharma, Senior Manager, Deloitte
The AI & Data Science programme is comprehensive — PyTorch, MLflow, Triton, MLOps. Grads come pre-trained for production ML & LLM engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their MLOps + drift monitoring track produces PMs who ship production-grade ML systems on day one. Rare combination of modeling rigor and engineering craft.
Deepak Pillai, Senior Architect, TCS
What sets Edify Nuva apart is the production ML layer baked into the data science track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their AWS ML Specialty + Pragmatic AI Engineer prep is rigorous, and the shipped project — training pipeline, model serving, drift monitoring — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Edify Nuva's Data scientists ship production ML systems twice as fast in the first 90 days. Our internal modeling metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best AI & Data Science pipeline we've sourced from in India. Their projects are real production deploys, not notebooks.
Karthik Subramanian, Engineering Director, Cognizant
Strong PyTorch and MLOps foundation. Their AI/Data grads need almost zero ramp time on enterprise ML engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Edify Nuva alumni across our ML and watsonx engineering teams. Strong fundamentals, sharp on eval and drift monitoring.
Sanjay Verma, Talent Director, IBM
ML systems + drift monitoring is exactly the talent gap we've been struggling to close. Edify Nuva is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their AI/Data Science track delivers engineers who navigate PyTorch, MLflow, and Triton on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Edify Nuva graduates for our ML engineering practice. Strong on PyTorch, sharp on MLflow, fluent in MLOps.
Geetha Pillai, Talent Acquisition Lead, Cyient
Edify Nuva grads who blend ML systems with Azure ML evals land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Edify Nuva · Institute Certificate
Agent‑Ready AI & Data Scientist
Presented to
Spandana Bala
For the successful training, deployment, and monitoring of a production ML system —
reproducible training pipeline, model-serving stack, drift monitoring, and LLM augmentation — evaluated against the AWS ML Specialty, Azure AI Engineer, Databricks ML Associate, and Pragmatic AI Engineer credential rubrics.
Manikanta Kona
CEO · Edify Nuva
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the ML engineering community and mapped to AWS ML Specialty
and Pragmatic AI Engineer credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — training pipeline, model serving, drift monitoring, LLM augmentation — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: training pipelines, model serving, drift monitoring, LLM augmentation,
MLOps automation. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
Job roles
Roles this program prepares you for.
Data Scientist
Frame problems, build and validate models, communicate results.
Machine Learning Engineer
Train, deploy and monitor models in production.
Applied AI / LLM Data Scientist
Build LLM, RAG and fine-tuned features on top of data.
Analytics Engineer
Own dbt models, semantic layers and trusted metrics.
Data Analyst / BI Developer
Dashboards, experiments and decision support.
Decision Scientist / Experimentation Analyst
Design and analyse A/B tests and causal studies.
Agentic Analytics Developer
Build and govern text-to-SQL and analysis agents.
MLOps Engineer
Pipelines, registries, serving and monitoring.
AI Product Analyst
Measure and improve AI features with evals and metrics.
Lead Data Scientist (career path)
Grow toward leading data science teams and strategy.
What employers should see in your portfolio: that you can take a business question to a shipped answer — engineer the data in SQL and Python, model it with validated ML or deep learning, add an evaluated LLM or agent feature, deploy with MLOps and monitoring, and present the measured business result.
04Job placement support
Your first AI/Data Science offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into AI labs and ML-heavy product orgs. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your training pipeline, model server, drift dashboard, LLM augmentation layer, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the ML systems you shipped (training pipelines, model serving, drift monitoring), the partner-org project, and the business outcome. Reviewed by ML engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into AI labs and ML-heavy product orgs — Microsoft, Anthropic, OpenAI partners, Hugging Face, Databricks, Snowflake, Scale AI, NVIDIA, Stripe, Razorpay, plus services that staff data science teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
AI & Data Science alumni
Hundreds of ML & data science careers launched — here are eight.
NV
Naveen Vedala
Senior ML Engineer
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff Data Scientist (LLM)
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal ML Engineer
Seattle · United States
Now at · Scale AI
MM
Mujahed Mohammed
NLP Engineer
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
MLOps Engineer
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
Computer Vision Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Recommendation Systems Engineer
Hyderabad · India
Now at · NVIDIA
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal ML Engineer classes running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening AI/Data Science classes running on IST and PST. Every online class ships
the same shipped project — training pipeline, model serving, drift monitoring, LLM augmentation — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Admissions
ENROLLING NOW
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the ML/DL stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS or stats background?
No on both counts. Roughly 40% of every class comes from non-CS streams — engineering, math, physics, BCom, BBA, and self-taught modelers. The opening modules cover the NumPy/Pandas fundamentals, scikit-learn patterns, and the ML training loop from scratch. What you do need is consistency and regular practice.
Will I actually ship production ML, or only do notebooks?
You actually ship to production. Every learner builds a reproducible training pipeline with DVC + MLflow + Optuna, packages the model with ONNX + Triton/BentoML, deploys it on Kubernetes with autoscaling, and sets up Evidently drift monitoring. The project runs in a partner org — not a notebook.
Which frameworks and tools will I use?
Core ML: Python, NumPy, Pandas, Polars, scikit-learn. Deep learning: PyTorch, TensorFlow, JAX, Hugging Face. MLOps: MLflow, Weights & Biases, DVC, Airflow, Prefect, Feast. Serving: Triton, BentoML, ONNX, SageMaker, Modal. Monitoring: Evidently, Arize.
Will I prep for AIPMM AI/Data Scientist and Pragmatic Principal ML Engineer certs?
Yes. The curriculum is mapped to the AIPMM AI/Data Scientist track and the Pragmatic Principal ML Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire data scientists / ML engineers?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Edify Nuva does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same shipped project — training pipeline, model serving, drift monitoring, LLM augmentation — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for learners needing additional support, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
