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Manishanker Talusani
MLflow tutor
US$20.00 /15 min
I'm the founder of TryNewAI, an AI-native engineering firm helping AI-native startups and pharma R&D teams ship production AI through embedded senior engineers. Before TryNewAI, I spent six years as a senior software engineer at IPsoft, Justdial, and InMobi, building production systems across enterprise AI, search, and ad-tech. Those years taught me what most AI projects miss: getting something to work in a notebook is a different problem from making it work reliably in production, at scale, on dirty real-world data. I founded TryNewAI in 2020 to focus on exactly that gap. Not staff augmentation. Not throw-it-over-the-wall projects. Instead, embedded engineering: senior engineers placed inside client teams, picking the highest-value AI workflows alongside leadership, and shipping them to production end-to-end. Five years in, the model has held up: → ForwardEdgeAI - what began as a 3-month contract is now a 5-year continuous engagement → Pharma & life sciences - production AI and data engineering with Johnson & Johnson, Charles River Labs, and Haleon → Enterprise - engineering work with Cisco and AusNet → AI-native startups — including Ox.work (San Francisco) Where I personally go deep: * Taking AI systems from prototype to production - eval harnesses, observability, reliability engineering * Data engineering for AI - pipelines, foundations, AI-ready data architectures * Regulated environments - pharma R&D, GxP contexts where "usually works" isn't acceptable * Embedded leadership - working as a fractional senior engineer or tech lead inside client teams Who I'm best for: -> A CTO at an AI-native startup whose AI features are stuck between prototype and production. A pharma R&D leader trying to operationalize AI inside regulated workflows ->A founder who'd rather have one senior engineer in the trenches than a five-person dev shop behind a project wall. Hyderabad-based, working globally. DMs open — happy to talk shop even if it doesn't lead to an engagement.
Akila Thalgahagoda
MLflow tutor
US$29.00 /15 min8+ years of experience in Machine Learning R&D with hands-on work ranging from prototyping code in academic publications to integrating solutions into production. Proficient in the entire lifecycle of a Machine Learning project, with a focus on Deep Learning applications in Computer Vision. Additional experience extends to speech, NLP, and tabular data domains. Skilled in writing clean, efficient, and scalable Python code, with a commitment of adhering to good MLOps practices.
Mariano Schmidt
MLflow tutor
US$15.00 /15 minSoftware Developer. Interests: Machine Learning, BigData, Data Science, Cloud Computing. Passionate about thinking how to solve problems. Trying to give my extra-mile in every opportunity.
Jordi Esteve
MLflow tutor
US$8.00 /15 minI am a Senior ML Engineer with vast experience in big tech and Unicorn startups. I am mainly focused on building highly scalable ML models with a focus on user and business impact.
Ivan CP
MLflow tutor
US$25.00 /15 minWith 10+ years mastering JavaScript, Java, and Python, I architect the engine rooms of the AI era. In 2026, backend engineering is no longer just about handling requests—it's about orchestrating AI inference, building GenAI microservices, and embedding intelligence directly into the data plane. I bridge traditional enterprise scalability with cutting-edge Artificial Intelligence. I have taken monolithic Java and Node.js systems and transformed them into event-driven, AI-augmented platforms for Fintech, Logistics, and Enterprise SaaS—slashing latency by 40% while embedding predictive intelligence into every API call. **The New Backend Reality (My 2026 Focus)** **1. AI-Powered API & Microservices Engineering** I don't just build REST/GraphQL APIs; I build inference-ready backends. I integrate Spring AI 1.0 (Java) and LangChain4j alongside Python's FastAPI to create services that don't just retrieve data—they generate insights. My microservices seamlessly route prompts to the optimal LLM (OpenAI, Gemini, Claude) via intelligent semantic routers, keeping your costs low and response times under 200ms. **2. Production-Grade MLOps & Inference Scaling** Building a model is one thing; serving it to millions is another. I design high-throughput inference pipelines using TensorFlow Serving, TorchServe, and KServe on Kubernetes. I implement model quantization and GPU autoscaling (using Karpenter) to ensure your AI features handle traffic spikes without burning through your cloud budget. I bring MLflow and Weights & Biases into CI/CD to track model drift and trigger automated retraining. **3. AI-Ops: Self-Healing Infrastructure** Just as AI writes code, I use AI to run the infrastructure. I build observability pipelines where AI agents analyze Prometheus/Grafana telemetry in real-time to predict node failures, autonomously execute canary rollbacks via ArgoCD, and dynamically rightsize Kubernetes pods. I treat infrastructure as a data problem—and I solve it with Python and Go-based automation. **4. Distributed Data & Vector Search** Modern backends need polyglot persistence. I architect systems that combine transactional SQL (PostgreSQL, CockroachDB) with real-time vector databases (Pinecone, Weaviate) for RAG workflows. I implement change-data-capture (CDC) pipelines using Debezium and Kafka to keep your vector indexes perfectly synced with your operational data—enabling instant semantic search across millions of records. **My Engineering Palette (Thematic Stack)** **The "Intelligent" Runtime Layer** JVM Ecosystem: Java 25, Spring Boot 4.0, Spring AI, Quarkus (for native GraalVM images). JS/TS Ecosystem: Node.js (NestJS), Bun (for 10x faster scripting), Deno. Python Ecosystem: FastAPI (now 58% developer usage), Django, Pydantic V2, and Celery for distributed task queues. **The Resilience & Connectivity Layer** Messaging: Apache Kafka (streaming), RabbitMQ (RPC), and AWS SQS/SNS. APIs: gRPC (for inter-service speed), AsyncAPI (for event-driven docs), and GraphQL Federation. Resilience: Implementing advanced fault-tolerance with Resilience4j, chaos engineering via Gremlin, and service-mesh governance with Istio. **The AI-Native Cloud Layer** Clouds: AWS (EKS, Lambda, Bedrock), GCP (Vertex AI, GKE), Azure. Container Orchestration: Kubernetes (with Karpenter for auto-scaling), Docker, Helm. IaC & GitOps: Terraform, Crossplane, ArgoCD, Flux. Security: Zero-trust with HashiCorp Vault and automated SBOM scanning (Trivy/Snyk) baked into every PR. **The Observability & FinOps Layer** Observability: OpenTelemetry (collector), Prometheus, Grafana, and Datadog. FinOps: CloudHealth, Vantage—optimizing spot instances and saving 30-40% on cloud bills. **Why My Approach Works in 2026** The industry has crossed a threshold. The engineers who thrive are not just "backend devs"—they are system thinkers who can tame complexity. I combine 10 years of battle-tested engineering with a relentless focus on AI integration and automation. I treat security and cost as primary architectural concerns, not afterthoughts. I excel at greenfield development, but I love rescuing legacy systems—modernizing them into cloud-native, AI-capable powerhouses. Whether you need a high-throughput inference engine, an AI-augmented CRM backend, or a multi-region Kubernetes platform—I deliver predictable, observable, and intelligent systems that your business can rely on. Let's build the intelligent backbone of your application. Reach out today.
Teo Popescu
MLflow tutor
US$20.00 /15 minHi, I am currently a Software engineer with expertise in building REST APIs, running AWS infrastructure and building data pipelines for companies in the banking, pharmaceutical and media industries. Previously, I taught data engineering concepts at University College London. My expertise includes working with the following technology stack: Python, FastAPI, Apache Spark, PostgreSQL, MongoDB, AWS, Apache Airflow, Docker, Jenkins, GitHub Actions, MLflow, Terraform, Kubernetes, ArgoCD, Argo Workflows and React.js.
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See the power of our MLflow tutors through glowing user reviews that showcase their successful MLflow learning journeys. Don't miss out on top-notch MLflow training.
“Kazi was an excellent tutor who gave me great advice and helped build a strong foundation in C and Assembly for my upcoming exam. He explained challenging concepts in a way that actually made sense, focused on the core skills and logic I need to keep improving, and even gave me practice problems to work on after the session so I could keep strengthening my understanding on my own. His patience and ability to simplify the tougher Assembly topics really stood out, and after working with him I feel much more confident in my ability to keep studying and pass my test. I’d definitely recommend him to anyone needing help with C, Assembly, or exam prep.“
David / Apr 2026
Kazi Sohan
MLflow tutor
“Sean is simply amazing! He resolved my task with React and TypeScript so fast and smooth. He is very very smart and knowledgeable. I highly recommend him. And he always give the best solutions. He is just born to be a programmer.“
vanya / Apr 2026
Sean Hurwitz
MLflow tutor
“Erick did a great job explaining key concepts in Flask, JavaScript, and REST APIs. His explanations were clear, structured, and easy to follow. Even in the first session, I gained a solid understanding and felt more confident applying what I learned.“
C / Apr 2026
Erick Alpizar Rivera
MLflow tutor
“Erick has consistently been knowledgeable, supportive, and patient throughout our sessions. He has helped me develop a stronger understanding of the concepts behind building a webpage using Python, JavaScript, and HTML. His ability to clearly explain each topic has made the learning process much more approachable and effective. I appreciate his guidance and would highly recommend him as a mentor.“
C / Apr 2026
Erick Alpizar Rivera
MLflow tutor
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Arrange regular session times with MLflow tutors for one-on-one instruction.
Frequently asked questions
How to learn MLflow?
Learning MLfloweffectively 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 MLflow. 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 MLflow, 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 MLflow 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 MLflow through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
- Build real-world projects: Apply your MLflow skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using MLflow in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
- Stay updated: Since MLflow 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 MLflow?
The time it takes to learn MLflow 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 MLflowtypically 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 MLflow’s more advanced features and best practices.
- Advanced level: Achieving proficiency or an advanced level of skill in MLflow generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of MLflow, contributing to major projects, and possibly specializing in specific areas within MLflow.
- Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in MLflow. Engaging with new developments, tools, and methodologies in MLflow 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 MLflow tutor on Codementor?
The cost of finding a MLflowtutor 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 MLflow 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 MLflow 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 MLflow with a dedicated tutor?
Learning MLflow 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 MLflow, 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 MLflow 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 MLflow 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 MLflow in a structured, supportive, and effective environment.
How does personalized MLflow mentoring differ from traditional classroom learning?
Personalized MLflow 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 MLflow.
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