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Community Blog Optimizing and Improving warehouse outbound-picking process by using Alibaba Cloud Machine Learning Platform For A.I. (PAI)

Optimizing and Improving warehouse outbound-picking process by using Alibaba Cloud Machine Learning Platform For A.I. (PAI)

This article describes how Alibaba cloud environments and Artificial Intelligence – machine learning can be utilized in supply chain management (SCM) / Logistics – warehouse management system.

By Kannan Nova, MVP, Data Scientist

What Is Alibaba Cloud?

Alibaba Cloud is a leading cloud provider based in Asia, rapidly growing in Europe, Africa, and North America.

I have worked with other cloud providers, but Alibaba Cloud provides easier steps and guides for configuring and creating servers/cloud workloads or resources. Each area (such as computing, networking, and storage) is more secure, scalable, and maintainable. Furthermore, it offers cloud-based end-to-end solutions (including private, public, and hybrid clouds).

What Is AI?

Artificial Intelligence (AI) solves business problems by combining computer science and mathematics. It contains machine learning algorithms and is categorized as supervised learning, unsupervised learning, deep learning, and reinforcement learning at the highest level. Alibaba Cloud supports these categories.

AI/ML Project Stages

Alibaba Cloud includes the following stages: data ingestion, data preparation, training model, model deployment/serving and visualization, and resources/components/AI capabilities.

AI Workspace

This version of the AI platform is brand new. We can create a workspace and manage its members and other workspaces. As a container for your enterprise, it provides AI development tools. ML/AI developers and collaborative teams can use these tools throughout the entire lifecycle of an AI project. We can create multiple AI projects using Alibaba cloud resources. After the creation of an AI workspace, the following steps are taken:

1

iTAG (Data Labeling)

Labeling data is a crucial step in the data preparation process. Specifically, the information must identify the raw data and add additional meaningful information to the data so the supervised learning model can correctly label and categorize the data. The iTAG section of Alibaba Cloud is specifically designed for data labeling.

Visualized Modeling-ML/Visualized Modeling – Designer (Model Training)

In this section, we train the model using a visual drag-and-drop designer tool provided by Alibaba Cloud. When we use a designer tool, we do not need to know ML algorithms or programming languages (such as Python or R). It has built-in model validation, so all errors are fixed at design time rather than run time. It also provides the option to optimize the model by adjusting its parameters.

Data Science Workshop (Model Training)

Data Science Workshop (DSW) is a feature of Alibaba Cloud that allows users to run their notebooks in their preferred programming language and see the results immediately. Additionally, we can import and export Jupiter notebooks.

DSW – Demo

I created a reinforcement learning AI application for the warehouse picking process using Alibaba Cloud DSW that optimizes the process, provides the shortest path, and reduces the task time to save time and money. The model can be trained and integrated into robotics, so picking robots always take the shortest route. As a put-away process, we can also use the inverted algorithm to efficiently stock the warehouse with goods. I demonstrate the outbound picking procedure below. Robots select and load goods onto a truck or container.

The Image below Used the Python 3 Notebook

2

Python Code

We can create a new Python Notebook over there or import a Python Notebook from the local.

3

Training and Evaluation

4

Model Deployment

Once the model has been evaluated and determined to meet the requirements, it is ready for end users or production. We can convert the model into a deployment package, such as a binary format (pickle file), and deploy it to Alibaba Cloud compute resources or ECS.

Model Training – Deep Learning

Deep learning requires powerful servers and graphically intensive systems. We can utilize Alibaba Cloud's Super Computing Cluster/RDMA SCC, H-PEC, and Elastic GPU service to run deep learning algorithms (such as convolution neural networks, recurrent neural networks, image or object recognition, and classification) without a hitch.

Model Deployment/Serving

The end user can access the end-to-end solution provided by Alibaba Cloud via online/API/RESTFul API. We require high-end CPU/GPU based on algorithms to deploy trained models into production. However, with Alibaba Cloud's model deployment option/Elastic Algorithm Service (EAS), we can easily spin up these cloud resources. EAS automatically deploys the models according to our convenient schedule. Instead of focusing on infrastructure resources, we can focus on business use cases and algorithms.

Conclusion

Alibaba Cloud is a cloud umbrella that offers all kinds of artificial intelligence solutions and applications on the cloud. It has different billing options and packages for many scenarios. It also provides the best practices and guidelines for AI projects. I love working with Machine Learning Platform for AI (PAI) and DSW. I am using it for all my projects.

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Amuthan Nallathambi June 5, 2023 at 8:45 am

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