In today’s data-driven world, machine learning models play a huge role in developing sectors like healthcare, finance, transport, e-commerce, and so on. However, building and deploying these models is just the beginning. Ensuring their accuracy, reliability, and performance over time is a significant challenge. This is where MLOps (Machine Learning Operations) comes into play.
MLOps is an emerging discipline that aims to unify and streamline the machine learning system development (Dev) and operations (Ops) lifecycle. It involves collaboration between data scientists, ML engineers, and IT professionals to automate and optimize the end-to-end process of building, deploying, and maintaining machine learning applications.
For some, MLOPS might be a completely new topic, but there are no worries. Whether you are a newbie or an experienced individual, if you want to explore more about the concepts of MLOPS, then you just click on the right blog.
But before we begin, Let’s have a look at what we will be covering in this blog:
What is MLOPS?
MLOps refers to the practice and discipline within machine learning that aims to unify and streamline machine learning system development (Dev) and operations (Ops). It involves collaboration between data scientists, ML engineers, and IT professionals to automate and optimize the end-to-end lifecycle of machine learning applications.
Now, Let’s make it simpler to understand by taking an example.
Imagine that you cook really good rolls, and you want to make a business out of it. In your initial days, there were about 2-3 customers per day, and you used to cook manually for them, which was easy and also manageable for you, but now, as your business grows, the number of customers has increased, and now manually cooking for all of them becomes very difficult for you. So, now, in order to deal with this, you brought an automated cooking machine that can automatically cook rolls, but you want the machine to cook rolls that exactly tastes like your cooked rolls. So, what you did was train this machine by giving it your recipe.
Now, this method of teaching your system (which is the cooking machine in this case) to learn from the data and make decisions without any explicit programming or human intervention is called machine learning.
So now, I hope you have an idea about machine learning. Now, moving forward with our example, Now after getting trained, the automated cooking machine is doing its job efficiently, it’s making good rolls like you, but now, with time, the customer’s preferences change, and for this, you have to make changes in your recipe, and again you have to train your machine with the new recipe. But for how long?
How can you maintain the quality of your product over time? We know that some or the other day, you will have to make changes to your recipe and train your model with that, which really becomes hectic.
MLOPS is used to solve this problem. It is the set of practices used by the ML developers and the operation team to ensure that the model maintains its accuracy over time.
Orthobaltic, became one of the famous examples of MLOPS in healthcare, It teamed up with EasyFlow to create an automated, data-driven system for making implants. This new system uses advanced technology to reduce human errors and save time.
Before this, engineers had to rebuild 3D models of body parts from CT scans manually. This old process was slow and could lead to mistakes. With the new automated system, Ortho Baltic can produce customized implants for patients much faster and more accurately.
Why do we need MLOps?
If we look at the traditional ML model-building cycle, it comprises the following steps:
1.Exploratory Data Analysis (EDA):
The first crucial step is exploratory data analysis. This involves thoroughly understanding the data before model building begins. EDA helps uncover valuable insights and patterns that guide further analysis and development of the machine learning model.
2. Feature Engineering
Next is feature engineering, where new features are created or existing ones are modified. This step aims to enhance the performance of the final model by providing it with the most relevant and informative data.
3. Model Training
Once the data is prepared with engineered features, it’s time for model training. This is the core step where the machine learning algorithm learns from the data to make accurate predictions or decisions.
4. Model Deployment
After successful training, the model is deployed or made available for use. Deployment allows the model to be integrated into applications, systems, or workflows to start providing value.
5. Model Monitoring
The final step is monitoring the deployed model’s performance and behavior in real-time scenarios. Continuous monitoring helps identify any degradation in the model’s accuracy over time.
Now, after building the model, we know that with time, the model’s accuracy and performance will degrade, and for that, again, we have to repeat this whole process of building a new model, which becomes very hectic and on top of that, this whole process needs to be carried out manually which makes it even worse.
Now, to help with this situation, MLOPS comes into the scene. MLOPS also known as Machine Learning operations, implements DevOps principles, tools, and practices into machine learning workflow, and also integrates it with the concepts of data engineering. And here’s how it will work to make your job easier.
MLOPS offers automation, Continuous integration/ continuous deployment, monitoring, and collaboration to the process of ML model development and deployment. Let’s check them one by one.
Automation:
Automation is a key aspect of MLOps, streamlining various steps in the machine learning lifecycle. It automates the training process, so whenever new data becomes available, or the model’s performance declines, retraining can be automatically triggered without manual intervention.
Continuous Integration/Continuous Deployment (CI/CD):
CI/CD ensures that any changes made to the code or “recipe” automatically trigger a build and deployment process for the updated machine learning model. This enables seamless integration of code changes into the production environment.
Monitoring:
Monitoring allows for tracking the performance of deployed models in real time. If a model’s performance drops below an acceptable threshold, alerts can be generated to automatically trigger the retraining process, maintaining optimal accuracy.
Collaboration:
MLOps promotes collaboration between cross-functional teams like data scientists, ML engineers, and developers. It provides a common platform and toolset for these teams to work together cohesively throughout the ML model lifecycle.
By automating and streamlining the machine learning lifecycle, MLOps helps teams save time, reduce manual effort, and ensure that their models are always up-to-date and performing optimally.
What are the components of MLOps?
MLOPS basically contains eight components which are Data management, version control, automation, Experiment tracking, ci/cd, monitoring or retraining, provisioning, and Governance. Now, let’s check them one by one:
Data Management:
Data management focuses on keeping data organized, clean, and accessible. It involves understanding data sources and storage methods. Ensuring high-quality, reliable data is crucial for training accurate machine learning models.
Version Control:
Version control systems track changes made to models and code over time. This enables collaboration between team members and maintains a history of reproducing experiments or results when needed.
Automation:
Automation, or setting up pipelines, saves significant time and effort. Instead of manually handling repetitive tasks like data prep and model training, the entire workflow can be automated for efficiency, consistency, and reliability.
Experiment Tracking:
Experiment tracking allows logging details like models used, configurations applied, and performance metrics. This facilitates analyzing and comparing different experiments to gain insights for improving models.
Continuous Integration/Deployment (CI/CD):
CI/CD automates the process of testing models against quality standards before rapidly and consistently deploying them to production environments, ensuring expected performance.
Monitoring and Retraining:
Continuous monitoring of deployed models’ accuracy and performance is vital. If issues are detected, models can be retrained or updated as needed to maintain effectiveness in production.
Provisioning:
Provisioning focuses on efficiently allocating compute resources for training and deploying models based on demand. This enables managing development/deployment environments and scaling resources optimally.
Governance:
Governance establishes rules, guidelines, and best practices for developing and deploying machine learning models compliantly with regulations, standards, and ethical principles like data privacy and fairness.
MLOps Roadmap for 2024
Now, after the MLOPS components, let’s finally check out the Step-by-Step Roadmap for MLOPS to make you prepare for the exciting journey toward the domain of MLOPS.
Step 1) Learn Programming Language
Start by choosing a programming language you’re comfortable with, such as Python, Java, Scala, or Ruby. Proficiency in programming is essential for automating processes as an MLOps engineer.
Step 2) Understand Machine Learning Algorithms and Libraries
Get a solid understanding of machine learning algorithms and libraries like TensorFlow, PyTorch, or scikit-learn. As an MLOps engineer, you’ll be working closely with machine learning models, so it’s crucial to understand how they work.
Step 3) Gain knowledge about databases
Learn about databases and their management systems, like SQL and NoSQL databases. MLOps engineers need to maintain and manage data pipelines, so understanding databases is vital.
Step 4) Model Deployment
Study how to deploy machine learning models on cloud platforms like AWS, Google Cloud Platform (GCP), or Microsoft Azure. Most companies use these platforms to host their applications, so familiarity with them is essential.
Step 5) Experiment Tracking
Learn how to track experiments, including parameters and metrics. This helps in organizing results, reproducing them, and maintaining comprehensive logs.
Step 6) Metadata Management
Understand the importance of metadata (data about data) and how to manage it effectively. Proper metadata management helps in understanding, grouping, and sorting data.
Step 7) Data and Pipeline Versioning
Learn about data versioning and how to store different versions of data created over time. This is essential for testing model efficiency, improving models, or making changes to the information flow.
Step 8) Model Monitoring
Study how to monitor deployed models for degradation and data drift, ensuring optimal performance.
Step 9) Projects
Work on projects to gain hands-on experience and build a portfolio showcasing your MLOps skills.
Step 10) Interview Preparation
Prepare thoroughly for interviews by practicing mock interviews and refreshing your knowledge of crucial MLOps concepts.
And to finally help you will you with all of these, Edureka offers MLOPS training certification course. You can enroll for it and start your MLOPS journey together with us.