Machine Learning (ML) has been a game-changer for businesses, supporting them in enabling automation, enhancing customer experiences, and improving decision-making. However, developing and deploying ML models can be a challenging and complex task which requires collaborative work of many engineering disciplines. Even so, many ML projects fail to make it to production.This is where ML Ops comes into play.
ML Ops is a combination of Machine Learning and DevOps practices that streamline and automate the end-to-end Machine Learning workflow from model development to deployment and monitoring. The goal of ML Ops is to make the entire process more efficient and less prone to errors.
Vertex AI is a fully managed Machine Learning platform that enables you to develop, deploy, and scale ML models easily. Aliz’s ML Ops consultancy provides end-to-end services to help you migrate your models to Vertex AI and to make the most of its advanced features. Our team of experts can guide you through each phase of the multi-staged migration process, from model assessment to the enablement of ML Ops features, and can help you automate and optimize your machine learning pipelines.
Scaling Your ML Workflows
Due to the level of complexity involved, Machine Learning projects are prone to failure even before making it to production to deliver their promised benefits. To succeed, you need to experiment quickly and have a seamless model deployment process that enables your engineering team to easily integrate machine learning solutions. At our consultancy, we've designed a multistage offering to help your data science team work more efficiently, focusing on the science rather than engineering.
Our offering assumes that you already have some models in proof-of-concept state and have some data science knowledge. Our consulting approach involves the review of your existing ML workflow, providing suggestions for improvements, supporting the migration of a selected model to Vertex AI, and delivering a reusable template to accelerate the model development and deployment process going forward.
The practical steps of bringing your model to Vertex AI
Vertex Readiness Assessment
In this phase, our goal is to help you assess your current machine learning maturity and create a list of models that can be migrated to Vertex AI. During a workshop session, our experts will analyze your existing models, data inputs, pipelines, and integration points to identify the scope of the project and define the stakeholders.
After the workshop, we select one model for migration and create a work-breakdown structure to plan the tasks and responsibilities ahead. A written proposal is created on how to improve your ML workflows on Vertex AI.
Vertex AI Migration
In the next phase, we’ll transform the model training code to a Vertex AI training job and will deploy the trained model ensuring compatibility with downstream tasks such as batch, streaming, or real-time prediction needs. For this phase, our team assumes that your models are built using standard ML frameworks like Tensorflow, Pytorch, XGboost, or sklearn.
Our team will ensure that the model training code is provided via a Python package/container, and that you’ll establish integration with consumer systems of the predictions.
Enablement of ML Ops Features
In the final phase, the goal is to add necessary engineering best practices to manage code changes and improvements. This phase will enable the continuous and confident use of the migrated models by your teams. Our experts ensure that the code is automatically tested, artifacts are built, and are deployed in the appropriate environment.
As a separation point between data engineering and data science, managed datasets or feature stores are used. Pipelines are optimized for resource usage, and artifact lineage is tracked. The model is monitored for data skew and drift, and we’ll automate the recreation of the whole project from infrastructure to application. We also provide a standard data science blueprint for your team to reuse across all their ML projects.
Finding efficient ways to experiment with Machine Learning solutions and to deploy workflows are crucial for businesses to stay competitive. Vertex AI offers advanced features that can make your AI investments more efficient – yet establishing an efficient ML pipeline can be a complex and challenging task.
Our end-to-end ML Ops consultancy services help you migrate your models to Vertex AI and optimize your machine learning pipelines. Our team of experts will guide you through each phase of the migration process, from model assessment all the way to the enablement of ML Ops features in production. Contact us today to learn more about our services and how we can help you enhance your business capabilities with Vertex AI!