Productionizing Machine Learning -

What does it Mean and its Significance

Machine learning is part of computational science whose aim is to analyze and interpret structures and patterns in data to enable reasoning and decision making. In simple terms, you feed an algorithm to a computer along with an immense amount of data and let the computer analyze and figure out data-driven recommendations based on only the input data.

Machine Learning has gained exponential significance in today’s era as all businesses thrive on data. When decisions are driven by real data, businesses can keep up with competition as they can take decisions based on customer data.

Below is described a typical process of designing a machine learning model:

• Data collection where data is obtained from databases or other sources.
• Analyze the data
• Select an engineering feature from the data to build an ML model.
• Analyze model results.

But how and where this model that you built is used to derive benefits? In the lifecycle of a machine learning project, the biggest issue is not how to create a good algorithm, improve accuracy, get good predictions, or generalize the results. The toughest job is to put the ML code into production or productionizing machine learning.

In this article, we will go through the difference between a machine learning system and a traditional software system, the process of putting machine learning into production, and what it requires.

Difference Between Traditional Software Systems And Machine Learning Systems

• We can deploy traditional software as a service but the same can not be done with ML systems. ML systems deployment is complex as they need a multi-step automated pipeline of deployment for validation, retraining, and deployment of the ML model.
• Apart from regular software tests like unit testing and system integration testing, ML systems also need model training, model validation, etc.
• ML systems need more iterations in the pipeline as they have varying data profiles and the model needs to be refreshed or retrained making ML systems extremely dynamic in performance.

Understand What Kind of Deployment is Needed for the ML Model

The truth about ML systems is that only a small part of it is composed of machine learning code and the bigger part is about model deployment, retraining, ongoing updates, maintenance, auditing, experiments, monitoring, and versioning. For this reason, model deployment strategy is a crucial part of designing the machine learning platform

How to deploy a model is based on your understanding of the system, which you achieve by asking questions like:

• How would the end user interact with the model’s predictions?
• How frequently do you need to generate predictions?
• Whether you need to generate predictions of a single instance or generate them for a batch of instances in one go.
• How many applications would be accessing the model?
• What are the latency requirements of these different applications that would be accessing the model?

All these questions indicate how complex machine learning systems are and large technical organizations with a lot of dependency on ML systems have dedicated platforms and teams that build, train, deploy, and maintain machine learning models. Some examples are:

• Microsoft’s AI Lab
• Databricks MLFlow
• Amazon’s Amazon ML
• FB’s FBLearner Flow
• Uber’s Michaelangelo
• Google’s TFX (TensorFlow Extended)
• Airbnb’s BigHead
• JPMC’s Omni AI

Deploying Machine Learning Model

When we deploy an ML model, we deploy the entire ML pipeline. An ML pipeline comprises all the steps needed to derive a prediction from data (steps discussed above). An ML model is just a piece of this pipeline (or the Model Building Part). While a model is a specific algorithm for using data patterns to generate predictions, an ML pipeline is the entire process of machine learning, from collecting data to generating predictions.

• There are typically three interconnected steps for productizing machine learning:
• Serving the models
• Creating the application’s business logic and presenting it behind an API.
• Building UI to interact with these new APIs.
• An immense amount of engineering work is required while building an end-to-end ML application like working with Flask apps to serve the model, setting up a complex infrastructure to scale properly (such as Kubernetes and Docker), having a separate product engineering work to integrate ML with existing systems, and developing front end for the new interfaces.
• With the range of skills needed in productizing machine learning, it is nearly impossible to establish feedback loops and much time goes into getting ML into a usable product.

Some Best Practices for Productizing ML Models

To streamline the process of deploying ML in production, here are some tips:

• Assessing data — One must first check if they have the right data set for running machine learning models. For example, restaurant chains that have access to millions of registered customers’ data can easily build a model on top of this data. Once the data risks are mitigated one must strive to set up a data lake environment with powerful and easy access to different data sources. Setting up a data lake removes many headaches for the team such as manual and bureaucratic overhead. You also need to experiment with data sets to ensure data contains enough information to bring about the desired change in business.
• Robust deployment approach — Integration and testing will become extremely smooth if you standardize the deployment process. For this, data engineers must aim at polishing the codebase, creating workflow automation, and integrating the model. Any ML model can be successful if you can build a complete environment having the right models and datasets.
• Evaluate the right tech stack — The ML models need to be run manually first to check their validity. For example, if you are sending personalized emails, you might want to find out if these promotional emails are bringing in new conversions or you need to rethink the strategy? Once the manual tests are successful, you decide upon the right tech where the data science team must select a technology stack that would make productization easier.

Learn How to Simplify the Complex Process of Productionizing Machine Learning

Getting top employment opportunities is everyone’s dream and if you are in the data science field, you must learn how you can put ML code id production to grab the best job offers. Univ.AI teaches the concept of Productization ML which will make you proficient in solving real-world problems. Their Live online classes with intense mentorship and hands-on learning are just what you need to master the concepts at the root level.