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Tanisha Bhayani
MLOps tutor
US$25.00 /15 min8 years of experience in building and managing AI Systems. * I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems. * Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs. * Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline. * Provide mentoring to junior developers for ML projects. Summary: I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well. I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well. I have a cumulative experience of 8 years working in the product and service-based industry for creating Machine Learning projects. I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on. Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences. \- Hands-on experience across several advanced AI and graphics-related domains, including: * **3D graphics and reinforcement learning pathfinding** for autonomous driving systems, including work related to the **NASA Rover Challenge**. * **Brain tumor segmentation and classification** using computer vision and deep learning techniques. * **Video analytics and video classification**, including in-video action recognition and classification workflows.
- MLOps
- SQL
- Git
- Database
- Machine Learning
- NLP
- Teaching
- Gunicorn
- Cython
- Kubernetes
- AI
- Generative AI
- LLM
- Python
- MySQL
- MongoDB
- Angular
- Flask
- PostgreSQL
- OpenCV
- NumPy
- Matplotlib
- Pandas
- Tkinter
- Scipy
- Web Scraping
- Computer Vision
- TypeScript
- Scrapy
- Web Development
- React
- Research
- Deep Learning
- TensorFlow
- Keras
- Responsive Web Development
- Chatbot
- Mobile App Development
- Deep Reinforcement Learning
- PyTorch
- Open Source Software
- OpenAI
- GPT
- GPT-3
- GPT-4
Adrian Segui
MLOps tutor
US$25.00 /15 min**📝 Profile Context / Summary** With 10+ years across enterprise IT and global freelancing, I am an AI-Stack Architect—not just a full-stack developer. In 2026, the industry has shifted decisively: software is no longer just written; it is architected, governed, and continuously learned. The most valuable engineers are no longer those who simply write code, but those who can turn code into cognition—engineers fluent in the AI-stack. I am that engineer. I design and build agentic platforms—systems where AI doesn't just assist but actively plans, builds, tests, and releases software. I have successfully delivered complex solutions across Medical, Fintech, Employee Management, and SaaS industries, handling everything from AI-native frontends to hyper-scalable microservices with autonomous agent orchestration. **🤖 AGENTIC AI & MACHINE LEARNING** 2026 is the breakout year of AI inferencing—where trained models generate predictions and outputs from new data. Most AI computing is now spent on inference rather than training. I specialize in: Agentic AI & Orchestration: LangChain, LlamaIndex, AutoGen, CrewAI—building multi-agent systems that plan, act, and adapt in real time. Full-stack agent platforms combining proprietary training data, planning models, and custom actuation layers. Generative AI & LLMs: OpenAI GPT-4/API, Google Gemini, Anthropic Claude, HuggingFace Transformers. Retrieval-Augmented Generation (RAG) with vector databases (Pinecone, Chroma, Weaviate, FAISS). AI-Native Development: 42% of committed code is now AI-generated, projected to reach 65% by 2027. I don't just use AI tools—I orchestrate AI agents across the entire SDLC: planning, design, build, test, deployment, and maintenance. Deep Learning & Classical ML: TensorFlow, PyTorch, CNNs (Computer Vision), RNNs/LSTMs (Time-Series/NLP), Scikit-learn, Pandas, NumPy. Model Deployment & Optimization: Model compression, quantization, edge computing, cost-per-prediction optimization. AI Governance & Security: AI-generated code carries roughly double the security risk violations of human-written code. I implement robust governance, testing, and security controls for AI systems. **⚛️ MODERN FRONTEND** React remains the most widely used UI library, with 67% of new enterprise React projects now built on Next.js—a 300% increase since 2023. React Server Components are now the default: Core: React 19, Next.js 16 (App Router, Server Components, Turbopack), Remix, Svelte 5 (runes), Astro. Styling: Tailwind CSS, shadcn/ui (1.87M weekly downloads), Framer Motion, Material-UI. State: Zustand (35% adoption, 35ms updates) vs Redux (38% adoption, 65ms), TanStack Query, Jotai. Build Tooling: Vite 8 with Rolldown (Rust-based, 10-30x faster builds), Turbopack. TypeScript: Used by 38% of professional developers, required in 72% of frontend job postings. **📱 MODERN MOBILE** React Native's New Architecture (stable since 2024) delivers significant performance improvements through synchronous native module calls: Frameworks: React Native (Expo), NativeScript, Flutter. Native: iOS (Swift/SwiftUI), Android (Kotlin & Jetpack Compose). Emerging: GPU-powered rendering for consumer and enterprise apps. **⚙️ MODERN BACKEND & API** Runtimes: Node.js (NestJS, Express), Bun, Deno, Python (FastAPI—57.9% developer usage, up 7% from 2024), Java (Spring Boot 4.0—modularized, Java 25 LTS support). Spring AI 1.0: Built-in support for integrating LLMs and AI services into enterprise Java applications. API Design: RESTful, GraphQL (Federation), gRPC, Webhooks, WebSockets. Event-Driven: Apache Kafka, RabbitMQ, AWS SNS/SQS. **☁️ DEVOPS & CLOUD (2026 Standard)** Cloud-native and Serverless adoption has surpassed 70% penetration in enterprise IT. Multi-cloud is now the standard—Gartner projects over 75% of cloud customers will adopt this model: Cloud: AWS, GCP, Azure (multi-cloud architecture). Containerization & Orchestration: Docker, Kubernetes (K8s), Helm. Serverless: AWS Fargate, Lambda, Cloudflare Workers—extending to data pipelines, real-time streaming, and event-driven microservices. IaC: Terraform, AWS CDK, Pulumi. CI/CD: GitHub Actions, GitLab CI, ArgoCD, Jenkins. **🗄️ DATABASES & SEARCH** Relational: PostgreSQL (PostGIS), MySQL, SQLite. NoSQL: MongoDB, DynamoDB, Firebase. Vector (AI): Pinecone, Chroma, Weaviate (for RAG and semantic search). Search: Elasticsearch, Algolia, Meilisearch. Caching: Redis, Memcached. **🔗 CRM, AUTOMATION & INTEGRATION** In 2026, the CRM market is consolidating around Salesforce, Microsoft, ServiceNow, and HubSpot as a durable challenger. Both platforms have invested heavily in agentic AI, predictive intelligence, and conversational AI: CRMs: Salesforce (Apex), HubSpot (Agentic Engagement Object), Zoho. Low-Code/No-Code: Gartner forecasts the low-code market at $44.5 billion in 2026, with 75% of new enterprise apps built on low-code platforms. Automation: VBA, Excel Macros, Google App Script, Bubble.io, n8n, Make, Zapier. Citizen Development: 80% of low-code users are now "citizen developers". **🚀 Why Partner With Me in 2026?** The software industry has crossed a clear threshold in 2026. Generative AI is no longer just helping developers write code faster—it is reshaping how software is planned, built, tested, and delivered. The role of the developer has evolved from coder to curator of intent, constraints, and outcomes. **I bring:** 10+ years of battle-tested engineering across the full spectrum. Agentic AI fluency—the ability to orchestrate AI agents across the entire SDLC. Systems thinking and architectural judgment—skills that AI cannot replace. Security-first mindset—AI amplifies what's already there; where code quality is managed, AI accelerates delivery; where it isn't, it accelerates technical debt and security exposure. Whether you need an AI-native application, a microservices overhaul, an agentic workflow orchestration, or an intelligent CRM integration—I deliver professional, scalable, and governable solutions. Let's architect intelligence together. **Contact me today.**
- MLOps
- PHP
- Python
- Android
- Java
- C#
- C++
- C
- SQL
- Node.js
- MongoDB
- Angular
- Database
- R
- Bash
- PostgreSQL
- Excel VBA
- Assembly
- Redis
- Machine Learning
- Elasticsearch
- Continuous Integration
- TypeScript
- VHDL
- VMware
- Firebase
- Ansible
- Full Stack
- Docker
- Ionic
- React
- Data Science
- Google Cloud Platform
- Kotlin
- JavaScript
- HubSpot
- Apache Kafka
- Kubernetes
- React Native
- Material-UI
- Deep Learning
- Microservices
- Swift
- Terraform
- TensorFlow
- iOS
- Rnn
- Lstm
- Multi agent systems
- Power BI
- Linux System Administration
- Next.js
- Prometheus
- PyTorch
- Flutter
- Zoho crm
- Transfer learning
- OpenAI
- Event-Driven Architecture
- Astro.js
- Express.js
- AWS
- Scikit-learn
- Prompt Engineering
- Langchain
- Generative AI
- Google gemini
- Pinecone
- Weaviate
- Crewai
Ahmet Özlü
MLOps tutor
US$15.00 /15 min### About Me I am an **Enterprise Domain Architect & AI-Augmented Engineering Lead** with over a decade of experience in software development, backed by a **Master’s degree in Computer Vision** and the **Google Inside Look 2019 Award**. My unique background bridges the gap between **robust enterprise system design** and **cutting-edge AI ecosystems**. I don't just integrate AI tools; I understand them from the ground up—from architecting resilient, scalable enterprise domains to building multi-agent autonomous workflows. ### 🚀 Core Expertise & How I Can Help * **Enterprise Architecture & Backend:** Designing high-performance backend systems, modernizing legacy infrastructure, and establishing Domain-Driven Design (DDD). I specialize in **Java, Spring Boot, gRPC, and Microservices**. * **Autonomous AI & Agentic Workflows:** Accelerating development velocity by integrating AI directly into the sprint lifecycle. I specialize in PBI-driven autonomous development using tools like **Windsurf, GitHub Copilot**, and orchestrating multi-agent collaboration frameworks using **Gemini, Groq, and Phidata**. * **Computer Vision & Deep Learning:** Leveraging my academic and practical background in **TensorFlow, Keras, and OpenCV** to build and optimize custom ML models for Face Recognition, Object Detection, and Video Analysis. ### 🏆 Key Achievements * Architected and led complex enterprise-level system transformations as a Domain Architect in a large-scale financial environment. * Built and deployed multi-agent AI teams to automate data collection, processing, and engineering tasks. * Optimized an object detection pipeline for a startup, reducing processing time by 40%, and deployed end-to-end ML models in production. * Published peer-reviewed IEEE papers in Computer Vision and Artificial Intelligence. ### 💡 Mentorship Philosophy I excel at breaking down complex concepts—whether it is designing a secure microservice architecture, training a custom CNN, or configuring a `skills.md` file for your AI agents. My goal is to empower you with practical, production-ready skills, transforming theoretical concepts into deployable code and autonomous workflows. * **GitHub**: [github.com/ahmetozlu](https://github.com/ahmetozlu) * **Papers**: [Facial Expression Recognition](https://ieeexplore.ieee.org/document/8404767/) | [Automatic Age Estimation](https://ieeexplore.ieee.org/document/8404549/)
- MLOps
- PHP
- Android
- C#
- SQL
- MySQL
- MongoDB
- Database
- Arduino
- Object-Oriented Programming
- Blockchain
- VR
- Ethereum
- Solidity
- AR
- Python
- Java
- C++
- Django
- Git
- Flask
- Linux
- Spring
- OpenCV
- Ubuntu
- JDBC
- NumPy
- JUnit
- JBoss
- Matplotlib
- Jenkins
- Spring Security
- Pandas
- Machine Learning
- Computer Vision
- Spring data
- NLP
- Spring Boot
- Neural Networks
- Swagger
- Deep Learning
- TensorFlow
- Keras
- RESTful API
- OpenAI
- Hibernate ORM
- Huggingface
- AI
- Langchain
- LLM
- Google gemini
Mohammed Kashif
MLOps tutor
US$13.00 /15 min
Hi, I am a Senior Data Engineer with over 6 years of experience working on Python. I have worked on Knowledge Graphs (Neo4j, Spark, NetworkX, d3.js), OS programming, creating data pipelines (Airflow, PySpark, SQL, AWS), data ingestion APIs (Dask, Azure, FastAPI, Postgres, AsyncIO) and data analysis (Pandas, Seaborn, Matplotlib). I have also created multiple courses on Udemy on High Performance Computing in Python, Exploratory Data Anaysis, Functional Programming and Scalable Data Analysis. I also answer questions regularly on StackOverflow. I am currently in Top 5% of people who answer on StackOverflow.
Parth Waidya
MLOps tutor
US$10.00 /15 minBuilt and managed robust and dynamic teams in software engineering, research and development in AI and ML. With a 7-year track record of spearheading technological innovation and driving digital transformation across diverse industries, I demonstrate a unique blend of strategic vision and technical acumen. Adept at aligning technology initiatives with business objectives to foster growth and competitive advantage, I have expertise in leading cross-functional teams, optimizing technology infrastructure, and implementing cutting-edge solutions that enhance operational efficiency and customer satisfaction. Proven ability to manage large-scale projects from ideation to execution, ensuring seamless integration and maximum ROI. Exceptional communicator and leader, committed to cultivating a culture of continuous improvement and excellence.
Vikram Saraswathi
MLOps tutor
US$8.00 /15 minI have started my career in IT in the year 2006. I have worked with various technologies ranging from DBMS, Mainframes, Backend Javascript, NoSQL, Data Architecture, Cloud Computing, Machine Learning, Data Science. I have worked with multiple fortune 500 companies like IBM, TCS & Ericsson. I am currently working as a senior Machine Learning Engineer.
Find your personal MLOps tutor on Codementor today 
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See the power of our MLOps tutors through glowing user reviews that showcase their successful MLOps learning journeys. Don't miss out on top-notch MLOps training.
“Salako was incredibly helpful and reassuring, answering my many questions and follow-up questions with his expertise. He really helped me weigh each option and weigh the pros and cons of each one. Thank you!“
Aaron A / May 2026
SALAKO TESLIM
MLOps tutor
“Wow! I am speechless, Samir is simply the best. He is so smart and fast and knowledgeable. I have never seen such a speedy programming in my entire life. He is just born to be a developer! Really thank you for your help and support!“
vanya / May 2026
Samir Habib Zahmani
MLOps tutor
“I'm very grateful for all the support that I've received from Shawn sofar. I wanted to learn more about the basics of engineering so that I could use coding agents such as Claude Code and Cursor more confidently, and Shawn has acted as a true mentor in this regard. Always patient, solution oriented and taking the time to explain (and repeat) things, I'm really enjoying learning from Shawn.“
Marc Abraham / May 2026
Shawn Freyssonnet-Inder
MLOps tutor
“Great session with Joel discussing my portfolio and job application strategies. I've been working with Joel for around 2 years now and in that time I've built a very strong game dev portfolio. Joel has provided excellent support and guidance throughout this period. Great mentor and very experienced and knowledgeable about game dev and the industry.“
Zeshan Rasul / May 2026
Joel Gompert
MLOps tutor
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Frequently asked questions
How to learn MLOps?
Learning MLOpseffectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
- Understand the basics: Start with the fundamentals of MLOps. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of MLOps, laying a solid foundation for further growth.
- Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
- Seek expert guidance: Connect with experienced MLOps tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
- Join online communities: Engage with other learners and professionals in MLOps through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
- Build real-world projects: Apply your MLOps skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using MLOps in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
- Stay updated: Since MLOps is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
How long does it take to learn MLOps?
The time it takes to learn MLOps depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
- Beginner level: If you are starting from scratch, getting comfortable with the basics of MLOpstypically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
- Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of MLOps’s more advanced features and best practices.
- Advanced level: Achieving proficiency or an advanced level of skill in MLOps generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of MLOps, contributing to major projects, and possibly specializing in specific areas within MLOps.
- Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in MLOps. Engaging with new developments, tools, and methodologies in MLOps is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
How much does it cost to find a MLOps tutor on Codementor?
The cost of finding a MLOpstutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
- Tutor experience: Tutors with extensive experience or high demand skills in MLOps typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
- Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
- Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our MLOps tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
What are the benefits of learning MLOps with a dedicated tutor?
Learning MLOps with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
- Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning MLOps, but exceling in a way that directly aligns with your objectives.
- Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
- Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning MLOps can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
- Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
- Career guidance: Tutors can also offer guidance on how to apply MLOps in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in MLOps in a structured, supportive, and effective environment.
How does personalized MLOps mentoring differ from traditional classroom learning?
Personalized MLOps mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
- Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
- One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
- Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
- Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
- Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering MLOps.
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