MLOps Roadmap [2024]: A Complete MLOps Career Guide

Written by: Mayank Gupta - AVP Engineering at Scaler Reviewed by: Anshuman Singh
15 Min Read


MLOps (Machine Learning Operations) is a set of practices that streamline the development, deployment, and management of machine learning models in production. Think of it as the bridge between data scientists who build models and operations teams who deploy and maintain them. MLOps ensures that models are deployed quickly, function reliably, and continuously deliver value in real-world applications.  It’s essential for companies wanting to fully leverage the power of machine learning at scale.

Understanding MLOps

What is MLOps?

what is mlops?

MLOps is a framework that integrates principles and tools from DevOps (software development and operations) and applies them to the unique challenges of managing the machine learning lifecycle. It aims to streamline and automate processes, ensuring models are deployed, monitored, and maintained seamlessly in production environments.

Key Components of MLOps

  • Version control & CI/CD: Tracking code, data, and model changes with version control. CI/CD (Continuous Integration/Continuous Delivery) automates builds, testing, and deployment.
  • Orchestration: Managing complex workflows and dependencies in the MLOps process.
  • Experiment Tracking & Model Registries: Recording experiments, hyperparameters, and results. Model registries store and manage different model versions.
  • Data lineage and Feature Stores: Tracking data sources and transformations for auditability. Feature stores manage and share processed data for model training and serving.
  • Model Training & Serving: Automating model (re)training, packaging, and deployment for real-time or batch predictions.
  • Monitoring & Observability: Monitoring model performance, data drift, and system health to detect issues and maintain model accuracy.
  • Infrastructure as Code: Managing and provisioning infrastructure (servers, storage, etc.) using code for consistency and ease of scaling.

Building Foundational Skills for MLOps

MLOps draws on expertise across multiple fields. Let’s break down the key areas where developing your skills will create a solid foundation:

Programming Proficiency


  • Focus on learning data manipulation libraries like NumPy and Pandas.
  • Familiarize yourself with model-building frameworks such as scikit-learn, TensorFlow, or PyTorch.

Integrated Development Environments (IDEs):

  • Utilize IDEs like PyCharm or VS Code for efficient development.
  • Using features such as debugging, code completion, and visualizations.

Bash Basics & Command Line Editors:

  • Understand basic Bash commands for server interaction.
  • Familiarity with command line editors enhances efficiency in infrastructure management.

Containerization and Orchestration


  • Docker is a must-have skill for MLOps practitioners.
  • Practice creating and packaging MLOps applications as Docker images.
  • These self-contained environments ensure consistency and portability, simplifying deployment across various settings.


  • While Kubernetes may be a later step, understanding its core concepts (pods, deployments, services) is essential.
  • Familiarity with Kubernetes prepares you for managing large-scale, containerized MLOps systems effectively.”

Data Management


  • Develop SQL proficiency to interact with relational databases, where data frequently resides.
  • Beyond basic queries, delve into joins, aggregations, and database optimization for efficient data retrieval.

Data Manipulation and Cleaning Techniques:

  • Master data manipulation techniques using libraries like Pandas.
  • Real-world data requires careful cleaning, transformation, and feature engineering before it’s ready for machine learning models.

Machine Learning Fundamentals

Core machine learning concepts

  • Building solid theoretical knowledge is crucial.
  • Explore different machine learning paradigms (supervised, unsupervised, reinforcement learning) to understand algorithm selection for specific problems.

Algorithms and Libraries

  • Dedicate time to practical usage of libraries like scikit-learn, TensorFlow, or PyTorch.
  • Perform tasks such as data splitting, model training, hyperparameter tuning, and performance evaluation.

DevOps Practices

Understanding DevOps principles in the context of MLOps

  • Learn the concepts of continuous integration (CI) and continuous deployment (CD) within MLOps workflows.
  • Adapt DevOps principles to suit the unique requirements of machine learning operations.

Continuous Integration (CI) and Continuous Deployment (CD)

  • Experiment with tools like Jenkins or CircleCI to automate MLOps workflows.
  • Automate processes from testing code changes to triggering deployments for enhanced efficiency.

Version control systems (e.g., Git)

  • Git remains essential for version control in MLOps.
  • Facilitate seamless collaboration between data scientists, developers, and operations teams through version control.

Phases of MLOps

mlops cycle

Phase 1: Exploration and Pilot Projects

Objective: To introduce the organization to machine learning (ML) and identify potential use cases.

Key Activities:

  • Leadership gives mandate for ML opportunities exploration.
  • Conduct pilot projects for demonstrating potential benefits.
  • Excitement and enthusiasm throughout the organization.

Phase 2: Proof of Concept and Model Development

Objective: Create initial models of ML & verify if they work or not.

Key Activities:

  • Proof of concept projects are completed successfully.
  • Transform models into usable judgments.
  • Establish data pipelines that can be used by models input/output.
  • Deploy models to make predictions in real-time or batch mode.

Phase 3: Handover to IT for Deployment

Objective: Shift model deployment & management responsibility to IT so as scalability, reliability can be achieved.

Key Activities:

  • Deployment takes place in a dedicated production environment managed by IT staffs.
  • Collaborative versioning between data science team and IT department on how best this should be done
  • Data pipeline management will be handled by information technology personnel
  • Jupyter notebooks are used among other tools as data scientists continue developing new models 

Phase 4: Integration and Automation

Objective: Seamlessly infuse ML into business operations and automate model deployment.

Key Activities:

  • Create organised training pipelines for machine learning models.
  • Implementing of DevOps practices in developing and deploying models.
  • Development of business logic by IT engineers to trigger model retraining.
  • Extend ML across different areas in business operations.

Phase 5: Complete Automation and Monitoring

Objective: Attain maximum efficiency through total automation of model deployment and monitoring.

Key activities:

  • Automate deployment of models as well as improvements to production level.
  • Feature stores should be established as single source of truth.
  • Implement advanced monitoring systems for tracking model performance records over time.
  • Enable continuous training and automatic updating of models based on new data inputs or any other relevant changes in the environment where these programs are applied.Allow data scientists devote their time more into the betterment of infrastructure while also ensuring that they deliver real business value.

Gaining Practical Experience in MLOps

Theoretical knowledge is your foundation, but nothing beats rolling up your sleeves. Let’s dive into the practical side of MLOps:

Learning MLOps Tools and Platforms

The MLOps landscape offers a rich array of tools. Explore the most popular ones and experiment to find those that resonate with you:

  1. Kubeflow: An open-source platform for managing complex machine learning pipelines on Kubernetes. Become familiar with its components for experiment tracking, pipeline creation, and model serving.
  2. MLflow: Focuses on experiment tracking, model packaging, and deployment, offering flexibility in how you integrate it into your workflows.
  3. TensorFlow Extended (TFX): Google’s production-level platform for the entire ML pipeline. Especially powerful if you work with TensorFlow models.Learn more about it from here.
  4. SageMaker: Amazon Web Services’ managed platform for MLOps, providing tools for the entire machine learning workflow.
  5. Databricks MLflow: A managed version of MLflow integrated into the Databricks platform, simplifying deployment and management.
  6. Other Emerging Tools: Explore tools like Seldon Core (model serving), DVC (data versioning), Metaflow (managing ML workflows), and more.

Engaging in Hands-on Projects

The best way to learn MLOps is by actively building! Focus on these areas for your projects:

  • End-to-End Deployment: Take a machine learning model through the complete process – data cleaning, model training, packaging it into a container, deploying it as a web service or a batch prediction job, and setting up monitoring.
  • Experiment Tracking and Model Retraining: Use tools like MLflow to track experiments, log results, and deploy the best-performing models. Set up automated retraining pipelines when model performance degrades.
  • Open-Source Collaborations: Contribute to MLOps-related projects on platforms like GitHub. This helps you learn from others, code collaboratively, and build your reputation in the field.

Finding Projects

  • Kaggle: Explore datasets and tackle competitions, deploying winning models and showcasing your MLOps proficiency.
  • Personal Projects: Choose a problem that excites you and apply the entire MLOps lifecycle, from data management and model development to deployment, monitoring, and continuous improvement.
  • Datacamp Projects: Datacamp offers guided projects specifically designed to teach practical MLOps skills.

Certification and Training Programs

Investing in structured learning and recognized certifications demonstrates your commitment and skills to potential employers in this competitive field. Consider these:

  • Certified Kubernetes Administrator (CKA): If you work with Kubernetes for MLOps, this validates your ability to manage Kubernetes clusters.
  • TensorFlow Developer Certificate: Demonstrates strong TensorFlow skills, a powerful framework often used in MLOps pipelines.
  • Cloud-Specific Certifications: AWS, Google Cloud, and Azure offer MLOps-related certifications. Choose based on the cloud platform you primarily use.

Also consider platforms like Coursera and Udemy which often have specialized MLOps courses or programs focused on specific tools (Kubeflow, MLflow, etc.). Datacamp provides dedicated MLOps learning tracks with a strong emphasis on hands-on projects.  Additionally, the vendors behind MLOps tools (Databricks, AWS, etc.) often provide their own in-depth training programs and certification pathways specific to their platforms and technologies.


Choose certifications and training that align with your career goals and the technologies commonly used in your industry.

Industry Networking and Community

MLOps thrives on collaboration and knowledge exchange. Actively engage with the community to learn from others, stay ahead of the curve, and unlock career opportunities.  Benefits of engagement include gaining valuable insights from others’ experiences, troubleshooting problems, discovering new tools and best practices, staying updated on the rapidly evolving MLOps landscape, and connecting with potential employers, collaborators, or finding mentors who can guide your MLOps journey.

Where to Connect

  • Online Forums and Communities: Participate actively on platforms like Reddit (r/MLOps), Stack Overflow, or search for dedicated Slack/Discord channels focused on MLOps discussions.
  • Meetups: Look for local MLOps meetups in your area using platforms like or consider attending relevant virtual meetups for broader networking.
  • Conferences and Workshops: Major conferences like KubeCon + CloudNativeCon, or even industry-specific events, often feature MLOps-focused talks, workshops, and excellent networking opportunities.


MLOps empowers businesses to unlock the full potential of machine learning. It ensures models are seamlessly deployed, monitored, and continuously improved for real-world impact. We’ve laid out a roadmap for your MLOps journey:

  • Build a Strong Foundation: Master programming (Python), machine learning fundamentals, data management, and core DevOps principles.
  • Explore MLOps Tools: Experiment with platforms like Kubeflow, MLflow, or TensorFlow Extended to understand their role in managing the ML lifecycle.
  • Gain Practical Experience: Tackle hands-on projects, collaborate with others, and focus on tasks like model deployment, monitoring, and retraining.
  • Certification and Continuous Learning: Consider certifications that align with your goals and stay updated on the latest trends and advancements in the field.
  • Network and Collaborate: Engage with the MLOps community to learn from others, find support, and discover new opportunities.

The demand for skilled MLOps professionals will only continue to grow. The time to start your MLOps journey is now!

Read These Important Roadmaps: More Paths to Career Success

DSA RoadmapDevOps Roadmap
SDE RoadmapData Science Roadmap
Web Development RoadmapData Engineer Roadmap
Full Stack Developer RoadmapData Analyst Roadmap
Front-end Developer RoadmapMachine Learning Roadmap
Back-end Developer RoadmapSoftware Architect Roadmap


Is MLOps the future of machine learning development?

Yes! MLOps is essential for scaling machine learning and making it a core part of business operations. As more companies rely on ML-driven solutions, MLOps ensures models are reliable and deliver value.

What are the stages of implementing MLOps?

While there’s no single definitive process, common stages include: model development, packaging, deployment, continuous monitoring, and retraining. MLOps platforms often automate and streamline these stages.

How can I start a career in MLOps?

Build a foundation in programming, machine learning, and DevOps. Gain hands-on experience through projects, whether personal or through collaborations. Consider certifications, and actively participate in the MLOps community.

What are the differences between MLOps and DevOps?

MLOps builds on DevOps principles but addresses the unique challenges of the machine learning lifecycle. This includes managing data dependencies, tracking experiments, model-specific monitoring, and handling retraining cycles.

What is the salary of MLOps professionals in India and other regions?

The average annual salary for a MLOps Engineer is ₹11,00,000 in India. Although, MLOps salaries are highly competitive and vary based on experience, location, and company.

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By Mayank Gupta AVP Engineering at Scaler
Mayank Gupta is a trailblazing AVP of Engineering at Scaler, with roots in BITS Pilani and seasoned experience from OYO and Samsung. With over nine years in the tech arena, he's a beacon for engineering leadership, adept in guiding both people and products. Mayank's expertise spans developing scalable microservices, machine learning platforms, and spearheading cost-efficiency and stability enhancements. A mentor at heart, he excels in recruitment, mentorship, and navigating the complexities of stakeholder management.
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