Make your Machine Learning team working easier, focus more on business and quick deployment with AWS managed service SageMaker.
Today, Machine Learning(ML) is resolving complex problems which make more business values for customer and many companies also apply ML to resolve robust business problems. ML have more benefit, but also more challenges to building the ML model with high accuracy. I currently working on the AI team to help the company deliver AI/ML project quickly and help Data Scientist(DS) team developing Data Pipeline, Machine Learning pipeline which helps project grow and quick delivery with high quality.
Overview of Machine Learning development
Figure1. Machine learning process
Here is the basic machine learning process which is the practice model of big companies. We are including multiple phases (business analyst, data processing, model training and deployment), multiple steps each phase and a fleet of tools that we use to result in dedicated steps.
Business problems: is including problems that challenge the business and we can use ML learning as a better solution to resolve it.
ML Problem Framing: is phase help DS and Engineering definition for ML problems, propose ML solutions, data pipeline and planing.
Data processing(Collection, integration, preparation and cleaning, visualization and analysis): This phase including multiple steps that help to prepare data for visualization and ML training.
Model Training(Feature engineering, Model training and parameter tuning, model evaluation): DS and developer will working on this phase to develop engineering features and prepare data for specific model and training model using framework such as Tensorflow, Pytorch.
When we don’t use any platform such as AWS SageMaker or Azure ML Studio then we take more time to develop with complex stack skills. We need to have many skills in compute, network, storage, ML frameworks, programming language, engineering features …
Actually, when we develop an ML model with a lack of skills and complex components that take more time to handle tasks about programming, compute and challenges for the engineering team which using the model and deploy it. In Figure 2, we have multiple instants of cloud computing (Infrastructure as a Service, Platform as a Service, Application as a Service) that provide resource type according to business necessary at the level of control. We can choose a specific instant layer for business necessary or compact of layers to meet the business objective. So, which Machine Learning projects and research environment, I would like to highly recommend for any DS and Develop should use PaaS and AaaS first to meet your business requirement first to quick delivery, reduce cost and effort. That is the main reason I want to describe AWS SageMaker service which can use as a standard platform for ML team to quickly develop/deploy ML model, focus on resolve ML business problems and improve the quality of the model.
AWS SageMaker offers the base of end to end machine learning development environment
Figure 3. AWS SageMaker benefits
When we developing an ML model, we need to take care of multiple partitions and sometimes we want to try with a new model and responsibility accuracy response as quickly as possible. So, that also depends on “Is there enough data for processing and training?” and “How many time will take for training a new model?”. AWS SageMaker is using in thousands of AI companies and developed by experts and best practices of ML which help to improve the ML process, working environment. In my opinion, when I want to focus on building a model and resolve the challenge problem then I want to make another easy at the basic level and spend more time to resolve the main problems at first. SageMaker provides me a notebook solution and a notebook is a great space that I coding and analyzing data for training. Working together with SageMaker SDK, I can easy to connect and use other resources inside of AWS such as S3 bucket and training jobs. Everything helps me quickly develop and deliver a new model. So, I want to highlight the main benefit which we got from this service and also have disadvantages.
– SageMaker provide solution for training ML model with training jobs that are distributed, elastic, high-performance computing using spot instance to save cost up to 90%, pay only for training time in seconds. (document)
– Elastic Inference: This feature help to save cost for computes need GPU to processing deep learning model such as prediction time. (document)
* 🎯 Reduce lack of skills and focus to resolve business problems. We can easy to setup training environment from notebook with “click” for elastic of CPUs/GPUs
* 🌐 Connectivity and easy to deploy
– AWS SageMaker is AWS manged service and it easy to integrate with other AWS services inside of private network. Which also impact to big data solution, ETL processing with data can be processing inside of private network and reduce cost for transfer.
– EndPoint function help DS/Develop easy to deploy trained model with “clicks” or from SDK. (document)
* 🏢 Easy to management: When working with multiple teams on AWS, more resource will come everyday and challenges with IT team to manage resources, roles which will impact to cost and security. AWS manged service will help to reduce resource we need to create.
* AWS SageMaker is a managed service, it implements with best practices and focuses on popular frameworks, sometimes it does not match your requirement and should be considered before choosing it.
* 🎿 Learning new skills and basic knowledge of AWS Cloud: When we working on AWS cloud, basic knowledge about cloud infrastructure is necessary and new knowledge with AWS managed service which you want to use.
* 👮 It also expensive than normal EC2 instance because it supports dedicated for ML, we need to choose the right resource for the development to save cost.
AWS SageMaker is the best suite service for the production environment. It helps to build a quality model and standard environment. Which reduces the risk in product development. We trade-off to get most of the benefit and quickly to achieve the goal of the team. Thank you so much for reading, please let me know if you have any concerns.