MLOPS Training in Hyderabad
With
100% Placement Support
- Certification Support
- Interview Guidance
- Daily Live Recordings
- Resume Building with Real-Time Project
- Live Projects
- Resume & LinkedIn Optimization
- Interview Mock Rounds
- Job-Ready Skills with Project Portfolio
- Daily Class Access
- Career Mentorship
MLOPS Training in Hyderabad - Upcoming Batches
MLOPS Training in Hyderabad - Course Curriculum
Understand the fundamentals of Machine Learning Operations and how it integrates ML, DevOps, and Data Engineering.
Why MLOps Matters in Real-world AI Projects
Explore its role in automating, scaling, and managing ML workflows.
Key Components of MLOps
Model training, deployment, versioning, monitoring, CI/CD, and collaboration.
Job Roles & Career Paths in MLOps
MLOps Engineer, ML Engineer, AI Architect, Data Engineer, DevOps for ML.
From data collection to model retraining – real-world end-to-end ML project steps.
Development Stage in MLOps
Experimentation, feature engineering, model selection, and evaluation.
Designing Reusable ML Pipelines
Build scalable, automated pipelines using Airflow, MLflow, or Kubeflow.
Data & Model Artifacts
Learn how to track and manage data versions, models, metrics, and results.
Parameters, Hyperparameters & Configs
Streamline experiments with YAML configs and automated tracking tools.
Understand what makes a complete MLOps stack: orchestrator, artifact store, deployer, and more.
Popular Orchestrators & Workflow Engines
Kubeflow, Airflow, ZenML, Prefect – how they manage and automate ML pipelines.
Artifact Stores & Data Versioning
Use AWS S3, Google Cloud Storage, or local stores to manage datasets and outputs.
Model Deployment Strategies
REST APIs, streaming, batch jobs with Flask, FastAPI, TensorFlow Serving, TorchServe.
Containerization & Virtualization
Deploy ML models using Docker and Kubernetes for scalable production environments.
Detect drift, monitor accuracy, and visualize performance using MLflow, Prometheus, and Grafana.
Metadata Management & Audit Trails
Automatically log models, datasets, hyperparameters, and training outcomes for reproducibility.
CI/CD for ML Models
Automate testing and deployment with Jenkins, GitHub Actions, and GitLab CI.
Collaboration Across Teams
Role-based access, shared workspaces, and Git integration for ML teams.
Dashboards for Real-Time ML Metrics
Create visual reports and alerts with tools like Streamlit and ZenML Dashboards.
MLOPS Training in Hyderabad
Why choose us?
- Hands-on Experience with Multiple Real-Time MLOps Projects
- Live Cloud-Based Campaigns for Practical Learning
- Gain Expertise Equivalent to 1 – 2 Years in just 2 Months
- Expert Trainer from MNCs with 15+ Years Experience
- Starts from Foundation Level Training
- 100% Job-Oriented MLOps Program
- One-on-One Mentorship with Complete Project Support
- Interview Questions
- 360-Degree MLOps Training
- Covers most Advanced Topics needed in industry
- Continuous Support till you get job
- Exclusive Access to Paid MLOps Tools
- Accredited Certification
- Professional Resume Creation & Profile Marketing
- HR Team support for placement assistance
MLOPS Training In Hyderabad
Key Points
- Learn from Ms. Iqra Fathima (certified trainer), who has over 7+ years of industry experience and 5 years focused on MLOps.
- We offer a range of flexible learning options. Choose from self-paced video courses, live online sessions, or classroom training.
- Our training is not just about theory. You will get the chance to work on real-time projects, using actual datasets and practical examples.
- If you miss any classes, don’t worry. We record every session and give you lifetime access to the videos via our LMS and Google Drive.
- Before you commit, we offer a few free demo sessions to help you experience the course and see if it’s the right fit for you.
- We have global partnerships with companies, starting in the U.S. and expanding worldwide, to offer customized training solutions.
- We provide 100% placement support, covering resume building and interview preparation assistance to project updates for your profile.
- Get access to over 100 MLOps interview questions along with certification practice materials to help you prepare thoroughly.
MLOPS Training in Hyderabad
Modes
Online Training
- Recorded Video Access Lifetime
- Certification-oriented
- Affordable course fee
- Basic to advance level
- Including live project
- 100% Placement Assistance
- Interview Guidance
- Whatsapp Group Access
Offline Training
- In-Person Classes
- Comprehensive Curriculum
- Mentorship Programs
- Assistance till Placed
- Certification training dumps
- Course Materials
- Project based learning
- Interview Guidance
Corporate Training
- Live Project Training
- Advance Level Course
- Batch as per Company Requirement
- Flexible class timing
- Doubt-clearing sessions
- Video Materials Access
- Certification Dumps
- Whatsapp Group Access
MLOPS Training In Hyderabad - Tools Covered
MLOps Tools Covered in Our Training
- MLflow For tracking experiments, managing models, and ensuring reproducibility of your ML projects.
- Docker Learn containerization to package and deploy machine learning models in isolated, consistent environments.
- Kubernetes Master container orchestration for scalable, reliable, and automated deployment of ML models in production.
- Kubeflow Understand end-to-end ML pipelines using Kubeflow for scalable model development, training, and serving.
- CI/CD Tools – Jenkins & GitHub Actions Automate your machine learning workflows with Continuous Integration and Continuous Deployment pipelines designed for AI projects.
- Prometheus & Grafana Implement monitoring solutions to track system performance, resource utilization, and model health in real-time.
- Airflow Orchestrate data pipelines and automate workflow scheduling for machine learning and data engineering tasks.
- Cloud Platforms (GCP / AWS / Azure) Hands-on experience with cloud infrastructure to deploy, monitor, and scale ML solutions.
- Feature Store (Feast or Similar) Manage and serve consistent, production-grade features for ML models.
- Model Monitoring Tools Learn how to track model performance, detect drift, and ensure your deployed models stay accurate and reliable.
- Data Version Control (DVC) Understand how to manage datasets and track changes to ensure collaboration and reproducibility.
What is MLOPS ?
- Generative AI is a groundbreaking branch of artificial intelligence that creates new content—such as text, images, audio, code, and video—based on the data it’s trained on.
- It empowers machines to generate human-like responses, creative designs, and intelligent solutions, transforming how we interact with technology.
- Generative AI offers solutions across content creation, design, customer service, product development, and automation—drastically reducing manual effort and time.
- It removes traditional barriers by enabling individuals and businesses to produce high-quality, on-demand content without specialized skills or expensive resources.
- Known for its scalability, accuracy, and adaptability, Generative AI is reshaping industries like healthcare, education, finance, entertainment, and software development.
- Whether you're a startup building smart assistants or an enterprise streamlining operations, Generative AI helps you innovate faster, automate smarter, and deliver at scale.
- Popular tools like ChatGPT, Midjourney, Bard, and DALL·E, along with platforms such as Hugging Face and Google's Vertex AI, simplify the development and deployment of GenAI models.
- Generative AI platforms often operate on flexible, pay-as-you-go pricing, making them accessible to creators, developers, and businesses of all sizes.
- As a key driver of digital transformation, Generative AI is redefining creativity and intelligence, delivering powerful experiences across the globe.
MLOPS Training in Hyderabad - Learning Objectives
Understand the core components of MLOps, including its lifecycle stages, tools, and how it supports scalable and reliable machine learning in production environments.
Learn to manage and store ML data efficiently using tools like MLflow, TensorFlow Extended (TFX), and cloud-native storage services such as Google Cloud Storage and BigQuery.
Grasp data ingestion and preprocessing workflows crucial for machine learning pipelines, including automated extraction, transformation, and loading (ETL) with Apache Beam and Dataflow.
Master data querying and feature engineering, leveraging SQL and data visualization tools to prepare and analyze training data with performance optimization techniques.
Acquire skills in user access control and model governance, implementing secure practices using Identity and Access Management (IAM), audit logging, and compliance with standards like GDPR and HIPAA.
Get hands-on with model monitoring and performance tuning, learning to detect data/model drift, automate retraining workflows, and optimize cloud resource usage.
Build and deploy end-to-end ML pipelines using tools like Kubeflow Pipelines, Vertex AI, and CI/CD practices, gaining practical experience through real-world MLOps scenarios in our MLOps Training in Hyderabad.
MLOPS Training in Hyderabad - Pre-Requisites
- Basic understanding of AI and machine learning concepts, including supervised and unsupervised learning, will help you grasp how generative AI models are built and trained.
- Familiarity with neural networks, natural language processing (NLP), or computer vision is useful to understand how generative models create content like text, images, or code.
- Understanding data fundamentals, such as structured vs. unstructured data, and experience with SQL or NoSQL databases, will aid in preparing quality datasets for training generative models.
- Basic programming knowledge, especially in Python (commonly used in AI), will help in working with AI frameworks, APIs, and prompt engineering tasks.
- Comfort with development tools and environments, including Jupyter notebooks and model training platforms, will enhance your ability to experiment with and deploy generative AI models.
Who Should Learn Mlops
Cloud Engineers
Professionals managing cloud infrastructure can benefit from learning MLOps to deploy, monitor, and maintain machine learning models in production environments efficiently.
IT Managers and Architects
Those in strategic roles will gain from understanding MLOps pipelines and lifecycle management to make informed decisions on AI infrastructure, scalability, and compliance.
Data Scientists and Analysts
MLOps enables data professionals to operationalize their models, automate workflows, and ensure seamless collaboration between data science and engineering teams.
Business Analysts
By understanding MLOps, business analysts can better interpret model-driven insights and collaborate with AI teams to align machine learning outcomes with business goals.
Software Developers
Developers benefit from MLOps by learning how to integrate machine learning models into real-world applications, manage APIs, and ensure model performance over time.
Students and Fresh Graduates
Aspiring professionals can build strong foundational skills in model deployment, monitoring, and lifecycle management—key for roles in AI/ML engineering and DevOps.
DevOps Engineers
DevOps professionals can upskill with MLOps to bridge the gap between ML and production, utilizing tools like Kubeflow, MLflow, and CI/CD for ML models.
System Administrators
Admins can benefit from MLOps knowledge to manage the infrastructure and environments needed for scalable and secure machine learning workflows.
Outline of MLOPS Training in Hyderabad
Introduction to MLOps and its core principles, covering how ML models are developed, deployed, and managed in production environments.
Hands-on training to build ML pipelines, covering containerization, deployment strategies, and orchestration tools like Kubeflow and MLflow.
Explore data ingestion, preprocessing, and transformation techniques to build scalable pipelines for large-scale model training and inference.
Best practices in ML workflow security—access control, governance, monitoring, and ensuring compliance in production setups.
Design scalable and modular MLOps architectures tailored to business goals using industry-relevant strategies and modern frameworks.
Learn CI/CD for ML: from experimentation to automated model deployment and continuous delivery of AI solutions.
Integrate ML pipelines with BI tools and dashboards to deliver data-driven insights and real-time model performance visualization.
Work on real-world case studies and projects to build practical skills and tackle actual business problems using MLOps methods.
Earn certification from Upskill Generative AI to validate your MLOps expertise and improve your job prospects in the AI industry.
Overview Of Mlops Training in Hyderabad
The MLOps course offered by the MLOps Training Institute in Hyderabad provides a solid introduction to Machine Learning Operations, equipping participants with essential skills to manage ML models in production.
The course begins with an overview of the MLOps lifecycle, including model development, deployment, and monitoring. Participants will gain practical experience using tools like MLflow, Kubeflow, and Docker to build, track, and deploy machine learning models.
Hands-on labs will guide learners through automating ML pipelines, managing datasets, and ensuring model reproducibility across environments. Real-time scenarios will make it easier to grasp the end-to-end flow of production-grade ML systems.
As the course progresses, key concepts such as CI/CD for ML models, model governance, and infrastructure monitoring will be explored. The course also emphasizes resource optimization and cost-effective deployment strategies for scalable solutions.
By the end of the program, students will be equipped with the expertise needed to implement and manage MLOps practices in real-world machine learning projects.
GCP Training in Hyderabad
Career Opportunities
MLOps Architects design and manage machine learning infrastructure, ensuring models are scalable, maintainable, and secure.
Build robust pipelines with tools like Apache Airflow and Kafka to support ML training and inference across cloud environments.
Automate CI/CD, containerize models, and streamline deployments using Jenkins, Docker, and Kubernetes.
Develop, fine-tune, and deploy ML models using TensorFlow, PyTorch, and production-ready ML pipelines.
Protect ML pipelines with access control, data governance, and compliance with regulatory security standards.
Monitor ML systems, analyze model performance, and provide data-driven insights for strategic decisions.
Evaluate and optimize ML infrastructure and workflows, guiding teams on best practices and efficiency.
Ensure uptime and reliability of deployed ML systems by monitoring and proactively resolving production issues.
Maintain and support tools like MLflow, Vertex AI, and Kubeflow while troubleshooting platform-level concerns.
Teach MLOps principles, deployment workflows, and real-world skills required for scaling machine learning systems.
MLOPS Salaries in Hyderabad
- Entry-Level Salary:
MLOps professionals starting their careers can expect to earn between ₹5 to ₹7 lakhs per year, depending on their skills in DevOps, ML frameworks, and automation tools. - Mid-Level Salary:
With 3–5 years of experience, MLOps engineers typically earn around ₹10 to ₹15 lakhs per year, especially if they have hands-on expertise in CI/CD for ML, Docker, Kubernetes, and MLflow. - Senior-Level Salary:
Experienced MLOps professionals with 5+ years in production-grade ML systems can earn ₹18 to ₹30 lakhs per year or more, particularly if they are leading teams or infrastructure. - Specialized Roles:
Roles such as MLOps Architects, ML Platform Engineers, and Site Reliability Engineers (SREs) often command higher packages, frequently exceeding ₹25 lakhs per year for senior professionals.
Skills Developed post-Generative AI Course
MLOPS Training in Hyderabad Certifications
Upskill Generative AI offers a comprehensive learning journey designed to help you master Machine Learning Operations (MLOps) — a high-demand skill set at the intersection of data science, DevOps, and cloud engineering. Gaining MLOps expertise not only boosts your career opportunities but also prepares you for certifications recognized by top tech organizations and cloud providers.
MLOps Certifications
- Google Cloud Certified – Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- Certified MLOps Engineer – by DeepLearning.AI or other industry leaders
- TensorFlow Developer Certificate
MLOPS Course in Hyderabad
Testimonials
Aarav Mehta
@aaravEnrolling in the MLOps Training at Upskill Generative AI truly transformed my career. The practical sessions and expert-led guidance helped me build strong skills and land my dream role within a few months. Highly recommended for anyone serious about MLOps!
Shruti Menon
@shrutiI found the content at Upskill Generative AI to be both deep and highly relevant. The real-time projects and hands-on labs made complex topics easy to understand. The supportive instructors made learning an enjoyable experience.
Sahil Kapoor
@sahilChoosing Upskill Generative AI for MLOps training was a turning point for me. The program was in-depth, and the placement team went above and beyond. I'm now working as a Cloud ML Engineer thanks to their support!
Pooja Reddy
@poojaThe trainers are fantastic — always approachable and technically sound. The MLOps training didn’t just teach me tools; it gave me the confidence I needed to crack interviews and succeed in my role.
Vikrant Rao
@vikrantI came in with no prior cloud or ML experience, but the way Upskill Generative AI structures its MLOps course made everything easy to grasp. Just weeks after finishing, I received a job offer from a top tech firm.
Nikita Verma
@nikitaThe course is hands-on and mirrors real-world challenges. Working on live ML pipeline scenarios gave me the edge I needed to transition into the MLOps field confidently. It was the best investment in my career so far.
Aditya Jain
@adityajI took the Data Engineering path within the MLOps course and found the experience top-notch. The trainers were highly supportive and shared valuable insights that prepared me well for the industry. I now work as a Data Engineer.
Ritika Sharma
@ritikaUpskill Generative AI’s MLOps curriculum is extremely well-structured. The added benefit of lifetime access to course materials and recordings makes it a great long-term resource. Perfect for anyone wanting to build a strong foundation.
Kunal Desai
@kunalThis training gave me the right push at the right time. From quality instruction to excellent placement support, Upskill Generative AI provided everything I needed to start my MLOps career with confidence.
MLOPS Course in Hyderabad
Trainer
👨🏫 INSTRUCTOR
Ms. Iqra Fathima
7+ Years of Experience
About the Tutor
Iqra Fathima is a highly experienced MLOps trainer with over 5 years of industry expertise. Throughout his career, he has guided hundreds of students and working professionals in mastering the tools and workflows required to succeed in Machine Learning Operations (MLOps).
Known for his friendly and practical teaching style, Ramu simplifies even the most complex MLOps concepts—whether it’s ML pipeline automation, CI/CD, or container orchestration—so that learners from all backgrounds can understand and apply them confidently.
He integrates real-world case studies, live project simulations, and step-by-step implementation strategies to ensure each learner builds hands-on expertise. Whether you’re just starting out or looking to advance your career in ML infrastructure, Ramu’s training will equip you with the clarity, confidence, and tools needed to thrive in today’s AI-driven tech landscape.
MLOPS Training in Hyderabad - Benefits
🔹 Enhanced Skill Set
Learning MLOps equips you with cutting-edge skills at the intersection of machine learning, DevOps, and cloud engineering. You'll gain expertise in automating model deployment, monitoring, and lifecycle management—making you a valuable contributor in the AI/ML ecosystem.
🔹 Efficient Model & Data Management
MLOps frameworks like Kubeflow, MLflow, and Airflow streamline the process of managing large datasets and ML models. They ensure smooth version control, reproducibility, and efficient execution from development to production.
🔹 Increased Employability
Proficiency in MLOps significantly increases your job prospects. Organizations actively seek professionals who can bridge the gap between data science and production engineering, especially as demand for scalable AI solutions continues to grow.
🔹 Cost Efficiency
MLOps helps optimize infrastructure usage through automation and containerization. Tools like Kubernetes and serverless compute reduce unnecessary resource consumption, allowing teams to build and deploy models cost-effectively.
🔹 Scalability
With MLOps, you can scale model training, testing, and inference seamlessly across environments using tools like Docker, Kubernetes, and cloud-based ML pipelines. This ensures high performance under growing workloads.
🔹 Robust Security Features
MLOps practices include access control, model governance, audit trails, and compliance standards—ensuring secure handling of models and data throughout the ML lifecycle.
🔹 Seamless Integration
MLOps integrates easily with CI/CD tools (like Jenkins, GitHub Actions), cloud platforms (GCP, AWS, Azure), and ML frameworks (TensorFlow, PyTorch). This allows teams to build consistent, end-to-end workflows with their existing stack.
🔹 Collaboration and Productivity
MLOps improves collaboration between data scientists, engineers, and DevOps teams. Through automated pipelines, shared environments, and reproducible results, teams can work more efficiently and deliver results faster.
🔹 Global Adoption
MLOps is being embraced by companies around the world. With support for distributed training, cloud orchestration, and global deployment capabilities, MLOps ensures AI projects are scalable, robust, and production-ready—no matter where your team is located.
Mlops Market Trend
Our Accomplishments
200+
Students Trained
8+
Batches Completed
7+
Years of Experience
2+
Professional Trainers