Pointwest is both a consulting and technology partner of AWS so we bring a rich mix of product development and managed services experience with a consultative approach.

Our ML Modernization approach can help you quickly migrate and build an enterprise-grade ML environment so you can make data-driven decisions faster and more cost-efficiently.

With our systematic ML Modernization Framework, we will help you properly adopt Machine Learning Operations and ensure that a sustainable ML environment is embedded in your organization.

We have product offerings that can automate the pipeline to accelerate time to business value.


If you’re experiencing any of the pain points below, we have just the solution to address them.

Pain Point Solution
Does training ML models for improving overall equipment effectiveness (OEE) take too long due to the large volumes of historical data? AWS SageMaker allows you to train ML models on powerful virtual machines that can make ML model training quicker
Are your ML models unable to process large volumes of collected data from equipment monitoring instruments in real time? With the power of the cloud, your ML systems can take advantage of automatic scaling to adopt to fluctuations in the volume of transactions while remaining cost-efficient
Are your ML models unable to correctly detect equipment status or make correct predictions some time after production deployment? We will help you develop automated model monitoring pipelines on AWS SageMaker so that model drift is automatically detected and model re-training is performed as needed.


  • As both a consulting and technology partner of AWS, we can bridge the gap between product and service offerings.
  • We have a rich product development experience with offerings that can accelerate time to business value.
  • Intelligent automation is one of our superpowers.
  • We have implemented ML projects with custom model training in the fields of intelligent document processing (IDP), computer vision, natural language processing, and forecasting.
  • We have developed an IDP SaaS platform hosted on AWS.
  • We match our strong engineering background with a deep understanding of MLOps maturity levels.


At Pointwest, we follow a systematic ML Modernization Framework to minimize friction and ensure successful migration:

1. Assess

We start with a discovery workshop to assess where you are in the ML modernization journey. We will also help identify
the best pilot projects to migrate to the cloud to quickly deliver business value.

2. Plan

We create a plan for modernizing and migrating your ML systems to the cloud, with concrete steps and timelines
customized to your existing ML environments. We also identify the success metrics that will indicate a successful ML
modernization project, and create a transition plan to turn over the management to your resources towards the end of
the project.

3. Prepare

We ensure that the people, processes, and environments are mobilized for the ML modernization activities to ensure a
smooth and frictionless transition to the cloud. We help with change management, process engineering, and
infrastructure preparations.

4. Execute

We start the ML modernization activities as planned. We begin with a small number of pilot projects and ensure that the
success metrics are achieved, and then we scale up and extend it to the rest of your ML systems.

5. Monitor

Once on the cloud, we monitor the performance of your ML environments in production to make sure that all your ML systems work as expected. We keep an eye on the success metrics and ensure that they are consistently achieved.

6. Transition

We perform enablement sessions to equip your resources to manage and govern your ML environments following industry best practices. We turn over the different components to the identified resources and carry out validation activities to ensure that your resources are prepared to operate the systems moving forward.


To get started, contact us at ask@pointwest.com.ph so we can schedule a call.

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