Machine Learning Operations: The Rise

Challenges of creating Machine learning models

• Data Dependency
  Data varies over time depending on the use case, and a lack of regularity causes ML models to perform poorly.

• Pipelines
  Training a simple model, putting it into inference, and generating predictions is a simple technique to gain business insights. In real-world scenarios, regularity is required, and temporal models must be retrained on new data. There will be numerous models, and human approval will be needed to pick which model to use for production. 

• Production Risk
  There is always the possibility of ML models failing. Thus they must be continuously monitored and evaluated to ensure they perform great. 

• MLOps process
  To manage production-grade ML systems, ML requires diverse specialists, including data scientists, data engineers, business analysts, and operations.

What is MLOps?

MLOps (Machine Learning Operations) is a collection of methods for data scientists and operations experts to collaborate and communicate. These approaches improve the quality of Machine Learning and Deep Learning models, simplify administration, and automate their deployment in large-scale production systems.

source: datasolut​

DevOps vs. MLOps

DevOps inspired MLOps, and the two approaches are quite similar. However, there are a few key differences between MLOps and DevOps:

• MLOps is exploratory. Teams continually change model features to improve performance while managing an increasing codebase.
• Data science teams are hybrid and contain developers (machine learning engineers), data scientists, or researchers who study data and construct models and algorithms. The latter may lack software development experience.
• In addition to the standard testing steps of a DevOps pipeline, such as unit tests, functional tests, and integration tests, an MLOps pipeline must constantly test the model itself, training and verifying its performance against a known dataset.
• In most circumstances, a pre-trained model cannot be deployed in production as is. Ongoing retraining and deployment of the model are required. 
• Unlike ordinary software systems, performance might decline with time, even if a model is entirely functional. 
• Monitoring a model as a software system is insufficient. MLOps teams must also monitor the data and predictions to determine when the model should be updated or rolled back.

Why do we need MLOps?

The necessity of MLOps can be summarized as follows:

• ML models rely on massive amounts of data that are challenging for a single individual to manage. It needs cross-team communication and hand-offs, from Data Engineering to Data Science to ML Engineering.
• MLOps refers to the machine learning lifecycle's experimentation, iteration, and continual improvement.
• It isn't easy to keep track of the parameters changed in ML models. Little modifications might have a significant impact on the outcome.
• We must maintain track of the features with which the model interacts; feature engineering is a specific job that adds significantly to model correctness.
• Monitoring an ML model differs from scanning a deployed software or web service.
• Debugging an ML model is a difficult skill to master.
• When the data changes, so should the model. This entails keeping track of new data changes and ensuring the model learns correctly.
  

Phases of MLOps

Let’s review the basic building blocks and workflow of an MLOps process. 

The process works as follows:

1. The pipeline gets data from storage, which starts the data preparation process, which includes cleaning, validation, and formatting. The data is now ready to be used for training the ML model.
2. Continuous integration and development (CI/CD) pipeline evaluates, builds, and releases model code. The automated pipeline loads the data from the previous stage and starts the model training. Automatic testing is done to ensure that the model is ready for deployment and that its performance fulfills minimal requirements.
3. Model evaluation — the model is reviewed to see whether it’s successful and works well. They are saved in a feature repository and can be utilized to train new models.
4. Model deployment—after successful validation and assessment, the model is immediately moved to deployment and utilized for production inference. The revised model is then saved to version control. This allows you to keep track of model versions and revert to older versions if necessary.
5. Automatic monitoring ensures that everything works as planned, identifies performance issues, and enables speedy maintenance if a problem occurs.
   

All phases in an entire MLOps pipeline are automated but may be paused by operators at any moment for human review or expanded with special procedures required by the business. When new data is available for retraining, the model is updated, or performance concerns in a production model are detected.

source: Arrikto​

What are the best practices for MLOps?

The best practices for MLOps can be delineated by the stage at which MLOps principles are applied.

• Exploratory data analysis (EDA)
  Create repeatable, editable, and shareable datasets, tables, and visualizations to iteratively explore, share, and prepare data for the machine learning lifecycle.
• Data Preparation and Feature Engineering
  Transform, consolidate, and de-duplicate data iteratively to develop enhanced features. Most importantly, use a feature store to make features accessible and shareable across data teams.
• Model training
  To train and enhance model performance, use popular open-source tools such as scikit-learn and hyperopt. As a simpler option, use automated machine learning techniques like AutoML to do trial runs and generate reviewable and deployable code.
• Model deployment and monitoring
  Automate permissions and cluster setup to allow registered models to be produced. Turn on REST API model endpoints.

Use cases

MLOps is used successfully in different areas like healthcare, web development services, etc. 

• Uber developed machine learning for various applications, including meal estimation, forecasting demand for drivers in diverse places, and customer service. They demonstrate that deploying machine learning at scale necessitates more than simply having the right technology. It requires excellent collaboration across various teams. Uber Michelangelo, a machine learning platform, was created to unify workflow across teams.
• ​Booking.com presently uses more than 150 different machine-learning models. They explain that building and launching 150 machine-learning products needs an iterative, hypothesis-driven, cross-disciplinary technique.
• ​Google’s DeepMind Health analyzes many healthcare records to discover and construct prediction models for specific diseases and health problems. Disease propensities will soon be simpler to regulate with the expansion of AI and machine learning in healthcare, from cancer research to diabetic retinopathy. That is an honest approach to developing a machine to treat and cure human sickness.
  

Finally, 

The market for MLOps solutions is expected to reach $4 billion by 2025. We're still in the early phases, but the MLOps strategy will be used in edTech, fintech, custom website development services, etc. Utilizing ML technology will uncover new revenue sources, save time, and reduce operational expenditures.

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