Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...
Full BioNand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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How to Create a Data Strategy for Machine Learning?
Summary
MLpAI can help deliver systems with more automation and less human intervention, but success requires a data strategy to deal with the complexity of real-world data. This research guides technical professionals involved in MLpAI on developing a data strategy to support successful deployments.
Table of Contents
Problem Statement
Introducing MLpAI and Its Limitations
The Gartner Approach
The Guidance Framework
Data Strategy for ML Process Framework
Prework: Building a Rationalization Framework for MLpAI
Defining the End Objective
Defining the Means Objectives
Providing Assessment and Governance to Support the Data Strategy
Defining Influencers Critical to the Success of the Data Strategy
Step 1: Build Problem or Task Taxonomy
Step 2: Design Data Science Pipeline
2.1 Constructing Batch Data Science Pipelines
2.2 Constructing Online Data Science Pipelines
Step 3: Enable Data Science Workflows
3.1 Enabling Supervised Learning Workflows
3.2 Enabling Unsupervised Learning Workflows
Step 4: Create Data Science Stages
4.1 Critical Stages of Preprocessing
4.2 Supporting Computationally Intensive Training Stages
Step 5: Integration
Step 6: Refine With Storage
6.1 Using Memory
6.2 Using Distributed File Systems
6.3 Using Distributed Data Stores (Persistent Data Store)
6.4 Using Relational Databases
Step 7: Operationalization and Maintenance
7.1 Compute-Intensive vs. Data-Intensive Components in Workflows
7.2 Securing Data Science Pipelines
Follow-Up
Introducing DevOps to MLpAI and Vice Versa
Risks and Pitfalls
Risk No. 1: Building DS Pipelines Can Be Especially Challenging When Dealing With Big Data Without the Right Tools
Risk No. 2: Poor Data Quality Will Significantly Impact Performance and Accuracy
Risk No. 3: Techniques for Securing DS pipelines Are Still in Their Infancy
Pitfall: Bounded Rationality Exists Even Within MLpAI Applications