Training

Build capability, not just capacity. Invest in skills that stick.

Why Training?

Technology implementations succeed or fail based on how well your people can use, manage, and extend the platforms you invest in. Too often, organizations spend heavily on software licenses and consulting, only to see adoption stall because teams lack the skills and confidence to leverage new tools effectively. Quantamatix’s Training practice bridges this gap with structured, role-specific learning programs that accelerate adoption, reduce dependency on external consultants, and build sustainable internal capability.

 Our Training Philosophy

 We don’t believe in one-size-fits-all training. Our programs are designed around three principles: relevance (training is tailored to your environment, processes, and actual system configuration, not generic courseware), practice (every module includes hands-on labs, exercises, and real-world scenarios that reinforce learning), and continuity (training doesn’t end with a session; we provide post-training support, reference materials, and follow-up assessments to ensure knowledge retention).

 Training Programs We Offer

 ServiceNow Training 

We deliver training for administrators, developers, and end users across ServiceNow modules. Programs range from foundational platform navigation and service catalog usage for business users, to advanced scripting (Business Rules, Client Scripts, Flow Designer), application development on App Engine, and platform administration for IT teams. Training can be aligned with ServiceNow’s official certification paths (CSA, CIS, CAD) to help your team earn recognized credentials.

 SailPoint Training

 Our SailPoint training programs cover identity governance concepts for business stakeholders, platform administration for IT security teams, and technical deep-dives into connector configuration, workflow development (BeanShell/Java), rule writing, and certification campaign management for implementation teams. We also offer specialized sessions on compliance reporting and audit readiness for GRC teams.

 SAP Ariba Training

 We train procurement teams, buyers, approvers, and suppliers on SAP Ariba modules including sourcing events, contract management, guided buying, invoice management, and supplier collaboration. For IT and integration teams, we offer technical training on Ariba integration with SAP S/4HANA using CPI and CIG, catalog management, and API-based extensions.

 Test Automation Training

 Our QA training programs equip manual testers with the skills to transition to automation, and help experienced automation engineers level up. We cover test automation fundamentals, framework design patterns (Page Object Model, BDD with Cucumber), tool-specific training (Selenium, Cypress, Playwright, Appium, JMeter), API testing best practices, CI/CD pipeline integration, and test strategy for agile and DevOps environments. 

Custom & Cross-Platform Training

 Beyond our four core technology practices, we design custom training programs for enterprise teams on topics including agile methodologies, DevOps practices, ITIL foundations, cloud fundamentals (AWS, Azure), and soft skills for IT professionals such as stakeholder communication and technical documentation. These programs can be combined with platform-specific training to create comprehensive learning journeys for your workforce. 

AI & Data Science Training 

Artificial intelligence is no longer a future ambition — it is a present-day business imperative. Organizations across industries are racing to embed AI into their products, operations, and decision-making. Yet the biggest barrier is rarely the technology itself; it is the skills gap. Most teams lack the foundational understanding needed to evaluate AI use cases, work effectively with data, or build and deploy models responsibly. Quantamatix’s AI & Data Science training program addresses this head-on with a structured, progressive curriculum that takes learners from foundational concepts through to hands-on model building. 

Our AI curriculum is organized into the following modules, designed to be taken as a complete learning path or as individual courses based on your team’s existing skill level:

 AI Fundamentals: A comprehensive introduction to artificial intelligence covering core concepts, history, and the current AI landscape. Learners explore the differences between AI, machine learning, and deep learning; understand supervised, unsupervised, and reinforcement learning paradigms; and examine real-world applications across industries.

 This module is designed for all roles — technical and non-technical — and provides the shared vocabulary teams need to evaluate AI opportunities and make informed decisions about where AI can drive the most value.

 Generative AI: A focused module on the generative AI revolution — covering large language models (LLMs), transformer architectures, diffusion models, and multimodal AI systems. Learners gain practical skills in prompt engineering, retrieval-augmented generation (RAG), fine-tuning strategies, and responsible AI practices. The module also addresses enterprise adoption patterns, including integrating generative AI into existing workflows, evaluating build-vs-buy decisions, understanding cost and latency trade-offs, and managing risks such as hallucination, bias, and data privacy.

 Data Foundations: AI is only as good as the data it learns from. This module builds essential data literacy covering data types, structures, and storage; data collection, cleaning, and preprocessing techniques; exploratory data analysis (EDA) and statistical foundations; feature engineering and selection; and data pipeline fundamentals. Learners work with real-world datasets to practice the full data preparation lifecycle, gaining the skills to transform raw data into model-ready features.

 Machine Learning Techniques: A hands-on deep-dive into core machine learning algorithms and methodologies. Topics include linear and logistic regression, decision trees and random forests, support vector machines, k-nearest neighbors, clustering algorithms (k-means, DBSCAN, hierarchical), dimensionality reduction (PCA, t-SNE), ensemble methods (bagging, boosting, XGBoost), model evaluation metrics, cross-validation, and hyperparameter tuning. Every algorithm is taught with theory, intuition, implementation, and practical exercises using real datasets. 

Deep Learning & Neural Networks: This module covers the architecture and training of neural networks from the ground up. Topics include perceptrons and multi-layer networks, activation functions, backpropagation and gradient descent, convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) and LSTMs for sequential data, attention mechanisms and transformer architectures, transfer learning and pre-trained models, and practical frameworks including TensorFlow and PyTorch. Learners build and train models on real-world tasks including image classification, time-series forecasting, and text generation. 

Natural Language Processing (NLP): A specialized module on teaching machines to understand, interpret, and generate human language. Covers text preprocessing (tokenization, stemming, lemmatization), bag-of-words and TF-IDF representations, word embeddings (Word2Vec, GloVe, FastText), sequence-to-sequence models, named entity recognition, sentiment analysis, text classification, question answering, and modern transformer-based approaches (BERT, GPT families). Learners build practical NLP pipelines and understand how to apply language models to business problems such as document classification, chatbot development, and knowledge extraction. Python for AI (NumPy, Pandas, Scikit-learn): The essential programming toolkit for anyone working in AI and data science. This module covers Python fundamentals for data work, numerical computing with NumPy (arrays, broadcasting, vectorized operations), data manipulation and analysis with Pandas (DataFrames, grouping, merging, time series), data visualization with Matplotlib and Seaborn, and machine learning implementation with Scikit-learn (pipelines, preprocessing, model selection, evaluation). Learners also gain exposure to Jupyter Notebooks as a development environment and best practices for reproducible, well-documented analytical work. This module can be taken as a prerequisite or alongside the Machine Learning and Deep Learning modules. The AI training program can be delivered as a comprehensive multi-week bootcamp, as individual stand-alone modules for teams with specific skill gaps, or as a blended learning journey combining self-paced content with live instructor-led sessions and hands-on capstone projects. All modules include practical labs, coding exercises, and real-world case studies to ensure learners can apply concepts immediately in their work.

 Training Delivery Formats

 Instructor-Led Training (ILT): Live classroom sessions (onsite at your location or at our Pune training center) with an experienced instructor, hands-on labs, and real-time Q&A. Best for teams that need focused, immersive learning.

 Virtual Instructor-Led Training (VILT): Live online sessions delivered via video conferencing with screen sharing, breakout rooms, and interactive exercises. Ideal for distributed teams and remote workforces. 

Self-Paced Learning: Pre-recorded video modules, reading materials, and self-assessment quizzes that learners complete at their own pace. Suitable for onboarding, refresher training, and supplementary learning. 

Blended Learning: A combination of live sessions, self-paced modules, hands-on labs, and mentorship. Designed for multi-week programs where deep skill development is the objective.

 On-the-Job Coaching: Embedded coaching where our experts work alongside your team during live projects, providing real-time guidance, code reviews, and best practice mentoring. The fastest path from learning to doing.

 What Makes Our Training Different 

Tailored to Your Environment: We don’t teach on demo instances. Training labs and exercises are built on your actual system configuration, your data, your workflows. This means learners practice in the context they’ll work in every day. 

Role-Specific Learning Paths: Executives get strategy-level overviews. Administrators learn configuration and operations. Developers dive into scripting and integration. End users learn the workflows relevant to their daily work. Every audience gets the right depth.

 Certification Alignment: Where applicable, our training maps to official vendor certification paths (ServiceNow CSA/CIS/CAD, SailPoint certifications, SAP Ariba certifications) so your team can pursue industry-recognized credentials.

 Post-Training Support: Every training engagement includes 30 days of post-training email/chat support where learners can ask follow-up questions, troubleshoot issues they encounter while applying new skills, and get guidance from their instructor.

 Measurable Outcomes: We define learning objectives, administer pre- and post-assessments, and provide a training effectiveness report so you can demonstrate ROI on your learning investment to leadership.