The AWS Machine Learning Framework: A comprehensive guide to bringing models to production

Data scientists develop a functioning model using a machine learning framework and some data. The developed model has a low error rate and a set of hyperparameters optimized. But when companies decide to apply this model to a production environment, they realize model building as the first step.

There is a greater challenge. Converting functioning code from a notebook to a scalable, secure, and available production environment.

Also, there has been an explosion of artificial intelligence and machine learning developer tools. You have a wide range of options to integrate AI into their applications. From cloud-based cognitive APIs to libraries, frameworks, and pre-trained models.

Several platforms claim that they democratize AI and data science. You will be able to solve your data science problem without coding. While others claim to provide the fastest and easiest way to deploy a solution in the cloud.

In this article, you will see why AWS and Sagemaker is the right machine learning framework to bring your model to production. As part of the assessing process you will be answering the ‘WHY’, ‘HOW’, and ‘WHAT’ sequences of questions.

Why a Machine Learning framework?

Machine learning (ML) frameworks make it easier and faster for data scientists and developers to build and deploy machine learning models. By using these tools, you can maintain an efficient ML lifecycle while scaling your machine learning efforts.

A typical machine learning lifecycle involves a cyclical process with three phases:

  1. Data pipeline development involves defining the problem, collecting data, and analyzing the data. Then cleaning, transforming, and transforming the data into the desired format.
  2. Train (fit) the model by leveraging one or more machine learning algorithms. Based on the model’s performance, you tune hyperparameters, train again, and do this until results are acceptable.
  3. Inference: The model is deployed into a production system that caters to the larger ecosystem.

In this iterative process, changes can loop back to any point in the process.

As the data, training iterations, and inference become complex post-deployment. This workflow evolves into three distinct workflows. It is the transition of  “Build” to “Train” and then “Deploy”.

Most companies hire data scientists to research, experiment, prototype, and develop a Proof of Concept (PoC). PoC validates a machine learning business use case. Then a software development team converts the PoC into a scalable web service or software product. But, it is not that easy to collaborate between software engineers and data scientists.

The task of understanding the other group’s requirements, technical language, and ideas is definitely too overwhelming for each group.

As a result, you need a framework that provides a data scientist with the tools needed to execute a machine learning project in an end-to-end manner.

How can AWS and Sagemaker solve the problem?

The 3 phases ( “Build” to “Train” and then “Deploy”) in the project are diverse and you need a solution.

To enable the data scientist to leverage his skills for engineering and studying data, training and tuning machine learning models, and finally deploying the model as a web service, you need a framework.

Besides provisioning the required hardware, orchestrating the entire flow, transitioning for execution with a simple abstraction, and providing a robust solution which scalable and elastic.

AWS and Sagemaker fit best for this problem.

What can AWS do?

With over 200 fully-functional services from data centers globally, Amazon Web Services (AWS) is the world’s largest and most used cloud platform.

It offers the following benefits:

  • Trade fixed expenses for variable expenses – Instead of investing in data centers and servers before you know how you will use them, you can pay only when you consume computing resources.
  • Utilizing cloud computing allows you to achieve massive economies of scale, resulting in lower variable costs than you would otherwise incur. AWS, which aggregates usage from hundreds of thousands of customers, can achieve economies of scale, resulting in lower pay-per-use prices.
  • Cut guessing about your infrastructure capacity needs. You often end up with expensive idle resources or limited capacity when you decide on capacity before deploying an application. AWS eliminates these problems. You can access as much or as little capacity as you need, and scale up or down as needed with a few minutes’ notice.
  • Since new IT resources are only a click away, you can reduce the time it takes for those resources to become available from weeks to minutes. As a result, there is a dramatic increase in agility for the company.
  • Focus on projects that differentiate your business, not the infrastructure. Instead of handling the heavy lifting of racking, stacking, and powering servers, you can concentrate on your own customers with cloud computing.
  • With a few clicks, you can deploy your application across many regions around the world. You can thus offer lower latency and a better experience to your customers at a minimal cost.

What can Sagemaker Machine Learning Framework do?

On-demand, Sagemaker provides Jupyter NoteBooks running R/Python kernels with a compute instance that meets our data engineering requirements. You can visualize, process, clean, and transform the data into your required forms using the traditional methods.

Then, after data engineering, you can train the models using a different compute instance based on the model’s workload, for example, a memory-optimized or GPU-enabled instance.

Take advantage of smart default settings for tuning high-performance hyperparameters. Take advantage of the rich AWS library or develop your own algorithms by using industry-standard containers.

Deploy the trained model also as an API, again using a different computing instance appropriate for business requirements and scalability.

It is also easy to provision hardware instances and run data jobs with high capacity, orchestrate the entire process with simple commands while abstracting the mammoth complexities, enable serverless elastic deployment with a few lines of code, and do it cost-effectively.

To conclude, AWS and AWS Sagemaker is the valid solution for the problem of bringing the model to production.

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