Full Stack MLOps Career Path

Become an MLOps engineer in just 4 months through intensive hands-on training and learn how to build your own machine learning system from scratch.
20 Videos
No Coding Experience Required
45 Assignments
Self Paced

About This Course

Dive into the powerful world of data manipulation with Python libraries for data science like NumPy and Pandas. Explore how to efficiently work with multidimensional arrays, perform data wrangling operations, and analyze large datasets. These skills are crucial for anyone aspiring to become a data scientist, data analyst, or machine learning engineer.
What You'll Learn
Core MLOPS You Will Learn
  1. Gain a thorough understanding of MLOps concepts and practices, enabling efficient management and deployment of ML models.
  2. Master techniques for collecting, storing, versioning, and validating data to ensure high data quality and integrity.
  3. Learn to track experiments, tune hyperparameters, and version models to develop high-performing ML solutions.
  4. Understand how to use containerization and orchestration tools to deploy models in scalable and reliable ways.
  5. Implement CI/CD pipelines to automate testing, validation, and deployment of models, ensuring continuous improvement.
  6. Set up systems to monitor and log model performance, detecting issues proactively and maintaining model accuracy.
  7. Ensure ethical AI practices and compliance with regulatory standards, maintaining transparency and accountability.
  8. Apply MLOps principles in a real-world project, demonstrating practical skills and readiness for industry challenges.
Mlops tools and platforms
  1. Kubeflow: Simplifies deploying ML workflows on Kubernetes, supporting all major ML frameworks.
  2. MLflow: Manages the ML lifecycle, including experimentation, reproducibility, and deployment with components like Tracking and Model Registry.
  3. TFX (TensorFlow Extended): Provides end-to-end ML pipeline management, from data ingestion to model deployment
  4. DVC (Data Version Control): Tracks large files, datasets, and models, ensuring reproducibility and collaboration.
  5. Seldon: Deploys, scales, and monitors ML models in production, offering features like canary deployments and drift detection.
  6. Docker: Containerizes ML applications for consistent environments and easier deployment across different platforms.
  7. Streamlit: An open-source app framework for creating custom ML tools and data science dashboards with minimal effort.
  8. Gradio: Allows quick creation of interactive demos and user interfaces for ML models, facilitating easy sharing and testing.
communication and business skills for MLOps
  1. Stakeholder Alignment: Ensure project goals align with business objectives through effective communication.
  2. Clear Documentation: Maintain comprehensive and accessible records of processes and workflows.
  3. Project Management: Plan, execute, and monitor MLOps initiatives to meet deadlines and standards.
  4. Data Presentation: Translate complex data insights into understandable information for non-technical audiences.
  5. Cross-Functional Collaboration: Coordinate seamlessly among data scientists, engineers, and business teams.
Problem Solving and Design Thinking Skills You Will Learn in MLOps
Curriculum Designed For Career Success
Module 1: Introduction to MLOps
3 Lectures

Introduction to MLOps offers a comprehensive overview of the core concepts and practices essential for managing and deploying machine learning models in production environments. It covers the unique aspects of MLOps, its benefits, and how it differs from traditional DevOps practices. Mastery of MLOps enables individuals to streamline ML workflows, enhance collaboration between data scientists and operations teams, and ensure the reliability and scalability of ML models, making it a crucial skill set for modern machine learning practitioners.

  • 1.1 Overview of MLOps:
    • MLOps (Machine Learning Operations) integrates machine learning with software engineering and operations to streamline the deployment, monitoring, and maintenance of ML models. MLOps ensures continuous integration, delivery, and automated deployment of ML models, fostering collaboration between data scientists and IT teams
  • 1.2 MLOps vs. DevOps:
    • While DevOps focuses on continuous integration and continuous delivery (CI/CD) for software applications, MLOps extends these principles to include data management, model training, validation, and deployment. MLOps deals with challenges specific to ML, such as versioning data and models, retraining models, and monitoring model performance.
  • 1.3 Benefits of MLOps:
    • Explore the advantages of adopting MLOps, including improved model reliability, scalability, and collaboration.MLOps enhances model reliability through continuous monitoring and automated testing, ensures scalability by leveraging cloud infrastructure and container orchestration, and fosters collaboration by integrating tools that streamline communication between data scientists and operations teams.
Module 2: Data Management for ML
3 Lectures

Data Management for ML focuses on the strategies and tools required for collecting, storing, versioning, and validating data, essential for ensuring data quality and integrity in machine learning projects. By mastering data management techniques, individuals can handle complex datasets, prepare data for model training, and maintain high standards of data quality, enabling them to derive actionable insights and build robust ML models.

  • 1.1 Data Collection Methods:
    • Learn methods for efficiently collecting data from diverse sources such as databases, APIs, and streaming platforms.Efficient data collection is essential for obtaining comprehensive datasets that adequately represent the problem domain. Techniques such as web scraping, API data extraction, and real-time data streaming are commonly used in industries like e-commerce for gathering customer behavior data, in finance for collecting market data, and in healthcare for capturing patient records from IoT devices.
  • 1.2 Data Storage Systems:
    • Understand the importance of choosing appropriate data storage systems for different types of datasets and use cases.Effective data storage is crucial for maintaining the integrity, availability, and scalability of large datasets. Relational databases are commonly used in enterprise applications for structured data storage, while NoSQL databases are preferred for unstructured or semi-structured data storage in applications like social media platforms and IoT systems. Cloud storage solutions offer flexibility and scalability, making them suitable for storing diverse datasets in industries ranging from retail to entertainment.
  • 1.3 Data Versioning and Management:
    • Understand the importance of versioning data to track changes and ensure reproducibility in machine learning workflows.Data versioning is critical for maintaining a historical record of dataset changes, facilitating collaboration, reproducibility, and experimentation in machine learning projects. Version control systems like Git are widely used in software development and data science for tracking changes to code and datasets. Data versioning practices are essential in research, healthcare, and autonomous systems development, where reproducibility and auditability are paramount for ensuring data integrity and model reliability.

Module 3: Model Development and Training
3 Lectures

Model Development and Training covers the essential processes of experiment tracking, hyperparameter tuning, and model versioning. These practices are crucial for developing high-performing and reproducible machine learning models. By mastering these techniques, individuals can streamline the model development process, ensure consistency, and improve model performance, ultimately leading to more effective and reliable ML solutions.

  • 1.1 Experiment Tracking:
    • is vital in industries like pharmaceuticals for documenting drug efficacy, dosage levels, and treatment protocols, ensuring reproducibility and collaboration among researchers.
  • 1.2 Hyperparameter tuning:
    • is extensively used in e-commerce for optimizing recommendation systems, enhancing customer satisfaction, and driving sales through improved product recommendations.
  • 1.3 Model versioning:
    •  is crucial in autonomous vehicle development to maintain safety and performance under various driving conditions, allowing developers to track improvements, rollback changes, and ensure consistency across different iterations of the autonomous driving system.
Module 4: Model Deployment
3 Lectures

Model Deployment focuses on the processes and tools required to move machine learning models from development to production environments. This module covers model serving architectures, containerization, and deployment strategies to ensure models can be quickly and reliably deployed. By mastering these techniques, individuals can ensure that ML models are scalable, maintain high performance, and are easily accessible for real-world applications.

  • 1.1 Model Serving Architectures:
    • Learn about different architectures for serving ML models in production.Model serving architectures find application in various real-world scenarios, such as online streaming platforms, where recommendation models are deployed to personalize content recommendations for users, ensuring a seamless and engaging user experience.
  • 1.2 Containerization with Docker:
    • Understand how to use Docker for containerizing ML models to ensure portability and scalability.Containerization with Docker is extensively utilized in healthcare systems for deploying medical imaging models, enabling healthcare providers to efficiently analyze and diagnose medical images while ensuring scalability and interoperability across different healthcare facilities.
  • 1.3 Deployment Strategies::
    • Explore various strategies for deploying models to production environments.Deployment strategies are essential in finance for deploying fraud detection models to detect and prevent fraudulent activities in real-time transactions, ensuring financial security and compliance with regulatory standards.

Module 5: Continuous Integration and Continuous Deployment (CI/CD)
3 Lectures

CI/CD for ML projects involves designing and implementing automated pipelines that streamline the testing, validation, and deployment of models. This module covers the necessary steps to maintain continuous integration and continuous deployment, ensuring models are always up-to-date and performing optimally. Mastery of CI/CD practices enables individuals to automate repetitive tasks, reduce deployment time, and improve model reliability.

  • 1.1 CI/CD Pipelines for ML:
    • Learn how to design and implement CI/CD pipelines specifically for machine learning projects.CI/CD pipelines for ML are crucial in industries like autonomous driving, where updates to perception models need to be seamlessly integrated into production vehicles to ensure safety and performance.
  • 1.2 Automated Testing and Validation:
    • Explore techniques for automating the testing and validation of ML models.Automated testing and validation techniques are extensively used in healthcare for validating medical diagnosis models, ensuring accuracy and reliability in clinical decision-making processes.
  • 1.3 Continuous Deployment
    • understand the process of continuously deploying updated models to production environments.Continuous deployment practices are vital in e-commerce for deploying product recommendation models, ensuring that users receive personalized recommendations based on the latest data, enhancing user experience and engagement.
Module 6: Monitoring and Logging
3 Lectures

Monitoring and Logging teaches techniques for setting up systems to continuously track model performance and log relevant metrics. Continuous monitoring is crucial for detecting issues such as model drift, performance degradation, and data inconsistencies. By mastering these techniques, individuals can ensure that models maintain high accuracy and reliability over time, enabling proactive maintenance and timely updates.

  • 1.1 Monitoring Techniques:
    • Learn how to set up monitoring systems to track model performance in production.Monitoring techniques are crucial in healthcare for monitoring patient risk prediction models, ensuring timely intervention and patient safety.
  • 1.2 Logging Best Practices:
    • Understand the best practices for logging model metrics and performance data.Logging best practices are widely applied in finance for logging trading algorithm performance metrics, enabling risk assessment and compliance monitoring.
  • 1.3 Model Drift Detection:
    • Explore methods for detecting and addressing model drift to maintain accuracy.Model drift detection is essential in manufacturing for detecting deviations in quality control models, ensuring product consistency and minimizing defects.
Module 7: Model Maintenance and Governance
3 Lectures

Model Maintenance and Governance covers strategies for ongoing model maintenance, retraining, and ensuring ethical AI practices. This module emphasizes the importance of maintaining and updating models to keep them relevant and compliant with regulatory standards. By mastering these techniques, individuals can ensure that their ML models are ethical, transparent, and continuously improving.

  • 1.1 Model Maintenance:
    • Learn strategies for regularly maintaining and updating ML models to ensure ongoing performance.Model maintenance strategies are crucial in the retail industry for updating demand forecasting models to adapt to changing market trends and consumer behavior.
  • 1.2 Retraining Models:
    • Explore techniques for automating the retraining of models based on new data.Retraining models techniques are extensively used in telecommunications for automating the retraining of customer churn prediction models, ensuring accuracy and reliability in customer retention strategies.
  • 1.3 Ethical AI and Compliance:
    • Understand the principles of ethical AI and how to ensure compliance with regulatory standards.Ethical AI and compliance principles are vital in healthcare for ensuring patient privacy and confidentiality in medical diagnosis models, complying with HIPAA regulations and ethical guidelines.
Case Studies

Industry Case Studies You Will Work On

Advanced Fraud Detection Techniques in Banking
Learn the skills and knowledge to detect fraudulent activities in credit card transactions, ensuring financial security for banking institutions and customers.
Skills learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Anomaly Detection Techniques
Operational Efficiency Optimization in Banking
Develop the tools and techniques to forecast call volumes in banking call centers, improving resource allocation and enhancing customer service efficiency.
Skills learned:
Time Series Forecasting
Model Evaluation
Operational Optimization Techniques
Customer Segmentation Strategies for Banking Marketing
Learn how to segment bank customers based on their behavior and demographics to optimize marketing campaigns for targeted customer engagement and retention.
Skills learned:
Data Visualization
Clustering Algorithms
Customer Profiling techniques
Sales Prediction and Optimization in Banking
Learn how to predict sales performance and optimize sales strategies for banking products and services that drive revenue growth and customer acquisition.
Skills learned:
Predictive Modeling
Sales forecasting
Model Interpretation Techniques
Real-time ATM Fraud Detection in Banking
Equip yourself with the skills to detect fraudulent activities in ATM transactions, safeguarding banking institutions and customers from financial losses.
Skills learned:
Data Preprocessing
Model Evaluation
Anomaly Detection Techniques
Predictive Modeling for Loan Default Prediction in Banking
Predict loan default probabilities that enable banks to assess credit risk and make informed lending decisions.
Skills learned:
Feature engineering
Risk Assessment
Model Evaluation Techniques
Predictive Analytics for Customer Lifetime Value Estimation
Learn how to estimate the lifetime value of bank customers, facilitating targeted marketing strategies and personalized customer experiences.
Skills learned:
Customer Segmentation
Predictive modeling
Model Evaluation
Personalized Cross-Selling Recommendations in Banking
Gain the skills to build recommendation systems for cross-selling banking products and services based on customer behavior and preferences.
Skills learned:
Collaborative Filtering
Recommendation Algorithms
Customer Profiling Techniques
Machine Learning for Mortgage Risk Assessment in Banking
Learn the tools to assess mortgage loan risks, enabling banks to make informed lending decisions and manage credit risk effectively.
Skills learned:
Feature Selection
Risk Modeling
Credit Scoring Techniques
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Make A Life-Changing Career Choice

IN-DEMAND CAREER
45%
Growth in demand for Data Analysts in the next 5 years
MASSIVE JOB OPENINGS
1M +
Job openings and counting for Data Analysts worldwide.
BIGGEST GROWING INDUSTRY
$349.6 Billion
Amount industry is set to grow by 2030
HIGH ENTRY- LEVEL SALARY
₹8-14 LPA+
Current average CTC for entry-level Data Analysts in India.
Don't Just Learn. Specialize.
India's only course with industry specialization in the domain of your choice.
50+
Industry case studies
10+
Problem solving frameworks
Experience 360° deep specialized learning
50+
Assignments
10+
Industry Projects
100+
Hours of Learning
Learn with Ai
Our program incorporates modern Gen AI based workflows for data science so that you are equipped with the tools of the future.
Made for working professionals
Enjoy flexible learning options. Go at your own pace or learn through live classes with industry experts.
Placement support from dedicated counselors
Mock interviews with senior industry leaders
Craft the perfect resume
Access our network of partner companies

Land Your Dream Job With
Full Placement Support

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Craft a Winning Resume

Get expert help building a resume that showcases your data skills.
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Nail Your Interview

Practise mock interviews with our experienced mentors to ace the recruitment process. 
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Company Screening & Selection

Benefit from our extensive industry network and connections to unlock exciting career opportunities.
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The team was thrilled with the quality of instruction provided. We have requests from teams from other departments to undertake the training as well. 
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Avinash Purohit
DGM, Canara Bank
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Is This Bootcamp Right For You?
Are you looking for a career change?
Do you want to switch from your current job to a more lucrative career as a data analyst in finance ?
Do you want a promotion?
Are you a banking or finance professional looking to upgrade your career with the most sought after skill in today’s market - data analytics ?
Are you a beginner to data analytics?
Are you a recent graduate looking for a comprehensive program to teach you everything you need to know to launch your career as a data analyst in the financial sector ?
If you answered "Yes" to any of these questions, SkillCamper's Data Analyst Bootcamp is the perfect fit for you!

What makes us different

Youtube Tutorials& Courses
Live classes
No learner support
No access to any mentor
No live classes
No accountability
No time commitment
SkillCamper
16 weeks course
1:1 Mentorship
Access to industry experts
Live classes with experts
Dedicated academic counselors to ensure you complete course requirements
15-20 hours of time commitment per week - designed for working professionals
Other Bootcamps &Degree Programs
20-60 weeks course
1:1 support may or may not be available
Access to industry may or may not be available
Live classes only
Accountability through assignments and grading
Full-time commitment - not made for working professionals
Online CertificationProgram
3-4 weeks course
No learner support
No access to industry experts
No live classes
Limited accountability
8-10 hours per week of time commitment- suited to working professionals

“The mentors at SkillCamper teach very well, making all the concepts easy to understand. ”

Their teaching style is clear and effective, and I am grateful for their guidance. I hope they continue this excellent approach in the future.
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Ravi Prakash
Automation Test Analyst

“SkillCamper's Data Analytics Bootcamp is fantastic. ”

I had tried learning some data analytics tools through free platforms, but it wasn't enough to get a good opportunity. SkillCamper goes beyond just teaching tools; they focus on domain expertise, which is essential today. The course material is very practical, and I feel like I'm gaining valuable skills. Highly recommended!
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Sanjay Shah
Graduate in BSC-IT

“I'm doing the Data Analytics Bootcamp at SkillCamper, and it's great. ”

Even though I don't have a tech background, the mentors explain things in a simple way that I can understand. The projects and the friendly community make learning fun and helpful. I highly recommend SkillCamper for anyone new to data analytics!
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Ashish Lodhe
Graduate in BSC-IT

“The course is going well and the mentors are very supportive. ”

As a student from a non-tech background, I find their teaching style easy to follow. They explain everything in simple terms and help with any questions. I highly recommend SkillCamper for anyone starting from scratch! 
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Suman M-
Graduate in BSC-IT
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Testimonials

Alumni Success Stories

From career switchers to college grads, we have helped a diverse range of learners kickstart & progress rapidly in their data science careers. 
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Live Interaction

Self paced

Fee Structure

₹ 75,000

₹ 50,000

Curriculum & Course Materials

Live coding environment

AI-based learning platform

100+ hours of instruction

20+ assignments

10+ banking & finance case studies

Banking & finance domain focused curriculum

Capstone projects

Live Classes

Flexible study options

Cancel anytime in first 7 days, full refund

Mentors

15+ hours of sessions with industry veterans & experts

Personalized mentorship by course instructors

Unlimited 1:1 doubt solving sessions

Career Support

Personalized placement assistance

1:1 mock interviews with industry experts

Soft-skills training module

Essential digital tools for digital workplace module

Interview preparation module

Masterclass on resume building & LinkedIn

Access to curated companies & jobs

Live Interaction

Self paced

Fee Structure

$599

$299

Curriculum & Course Materials

Live coding environment

AI-based learning platform

100+ hours of instruction

20+ assignments

10+ banking & finance case studies

Banking & finance domain focused curriculum

Capstone projects

Live Classes

Flexible study options

Cancel anytime in first 7 days, full refund

Mentors

15+ hours of sessions with industry veterans & experts

Personalized mentorship by course instructors

Unlimited 1:1 doubt solving sessions

Career Support

Personalized placement assistance

1:1 mock interviews with industry experts

Soft-skills training module

Essential digital tools for digital workplace module

Interview preparation module

Masterclass on resume building & LinkedIn

Access to curated companies & jobs

Frequently Asked Questions

What technologies will I learn in the Full Stack MLOps Bootcamp?
You’ll learn key tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS and Azure. You'll also dive into model monitoring and automated deployment systems, all of which are essential to managing machine learning in production environments.
Is prior experience required for the MLOps Bootcamp?
No, this bootcamp is designed for beginners. You don’t need any previous experience in machine learning or DevOps. The course will guide you step by step, starting from the basics of MLOps and gradually advancing to more complex topics, ensuring you’re well-equipped to succeed.
How long is the bootcamp, and can I learn at my own pace?
The boot camp lasts 4 months with flexible learning options. You can learn at your own pace with recorded lessons or attend live classes with experienced mentors. This structure makes it ideal for working professionals, with a recommended commitment of around 15–20 hours per week.
What practical skills will I gain from this bootcamp?
You’ll learn how to manage the entire machine learning lifecycle, including model development, deployment, monitoring, and maintenance. You'll also build and manage CI/CD pipelines, ensure model governance, and perform hands-on tasks related to data management and model operations.
Will I have lifetime access to the course materials?
Yes, you will have lifetime access to all course materials, including recorded sessions, tutorials, and project files. This allows you to revisit the content whenever you need to, even after completing the course.
What kind of career support will I receive?
You’ll receive personalised career coaching, including 1:1 mentorship, resume-building sessions, and mock interviews. We’ll also connect you with our network of partner companies, offering you direct access to job opportunities in MLOps and related fields.
What makes this MLOps Boot Camp different from others?
This bootcamp emphasises real-world projects and practical experience. You’ll work on projects that simulate real industry scenarios, get personalised guidance from industry experts, and have access to flexible learning options. Plus, our 7-day money-back guarantee ensures that you can explore the program risk-free.
What is the cost of the bootcamp, and are there payment plans?
The bootcamp costs ₹75,000, and we offer easy EMI payment options to make it more affordable. You can also try the course risk-free with our 7-day no-questions-asked money-back guarantee.
What kinds of projects will I work on during the bootcamp?
You’ll work on hands-on projects like setting up MLOps pipelines, deploying models using Docker and Kubernetes, and creating monitoring systems for live machine learning models. These projects will equip you with the practical skills needed to excel in real-world MLOps roles.
How will this bootcamp help me transition into a career in MLOps?
By the end of the bootcamp, you’ll have built a portfolio of MLOps projects, demonstrating your ability to manage machine learning models in production. With our career support and mock interviews, you’ll be fully prepared to enter the job market and succeed as an MLOps engineer.
What are the requirements for taking this course?
Familiarity with basic machine learning concepts and Python programming will help, although we cover essential topics.
What type of job support do you provide after completing the course?
We offer comprehensive placement assistance, including resume building, mock interviews, and leveraging our network of industry partners.
How do you prepare students for the job market?
We equip students with industry-relevant skills, help craft winning resumes, and provide mock interview practice with experienced mentors.
Do you guarantee a job after completing the course?
While we do not guarantee a job, we provide extensive support to help you become highly competitive in the job market.
Can you help me find a job in any specific industry?
Our job assistance is generalized; we prepare you for a variety of roles in the data science field rather than focusing on specific industries.
What is the cost of the Data Science bootcamp?
The cost varies depending on the program. Our full bootcamp is priced between ₹50,000 and ₹75,000, depending on whether you choose self-paced or live instruction.
 Are scholarships available for the courses?
Yes, we offer scholarships that can cover up to 70% of the tuition fees, making our courses more accessible to a wider range of students.
What is included in the course fee?
The fee includes access to all course materials, live coding sessions, AI-based learning platform, case studies, capstone projects, and mentorship from industry experts.
What payment options are available for the course fees?
We offer flexible payment options, including easy EMIs, and you can cancel anytime in the first 7 days for a full refund.
Is financial aid or other support available aside from scholarships?
While our primary financial support is through scholarships, our enrollment advisors can also assist you with payment plans and financing options to help manage the cost of your education.

Ready to become a Data Scientist that industry loves to hire? Apply Now. 

Explore Courses
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What technologies will I learn in the Data Science bootcamps?
You will learn Python, R, SQL, Power BI, Tableau, Excel, Pandas, Numpy, Matplotlib, Seaborn, and PySpark.