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5 Lessons to Ensure Successful Big Data Training for AI ML Projects

One of the biggest reasons for the failure of Artificial Intelligence (AI) and Machine learning projects is the poorly executed Big Data training strategy.

More than 90 percent of AI ML projects suffer from data quality issues, including unlabeled data, missing or incomplete data, and so on. Another reason why AI ML projects fail during Big Data training is related to poor choice of tools and platforms, and a lack of vision into data project management. In order to successfully execute an AI ML project, you need to have the complete script of Big Data training and how various stages influence the efficacy of your projects.

In this article, I have highlighted the seven critical points that you need to evaluate before, during, and after the execution of your AI Machine Learning project.

# 1 Build a Holistic Data Management Strategy

There are countless case studies available online and across training curriculum on why Big Data experiments fail.

The top reason why organizations have to attempt at least 2 iterations to succeed with their AI ML tools is associated with their heavy dependence on decentralized data warehouses and servers. The lack of a central data repository creates multiple views of the same data, leading to duplication of information and efforts in handling such data. In such a case, analysts often find themselves warring against time and space, merely to get hold of accurate data.

Therefore, an AI ML project’s execution heavily depends on how simplified your data management strategy is! You have to ensure that your data engineers and big data analysts have an easy access to necessary data points at all times.

As a trainer in Big Data, you have to ensure your AI ML team has a centralized approach and a single data management repository to manage data, execute analysis, and archive documents across the organization.

#2 Remove Risky Cases with Machine Learning Tools

The next step in the AI ML lifecycle is to choose carefully your various machine learning cases based on business demands, the size of the team, and the duration for which your project is expected to last (timeline).

Given the current scenario of remote workplace collaboration and Cloud computing trends, we have witnessed the compounding effect of machine learning projections. By using Enterprise AI, which we have discussed in another article, we can synchronize the various use cases across various departments involved in the development of the AI ML project.

By using APIs, integrations, and open source programming, you can embrace success with a wide range of case studies, especially in domains where data scientists are working with full-code programming languages like R and Python.

#3 Advanced Team Integrations and Collaboration

AI ML teams often suffer from a lack of clarity through their project timeline. It is mostly due to the fact that technical experts and domain leaders are disconnected from each other. In this critical stage, your business leader ought to play a strategic role and bring together all the participants — data analysts, scientists, AI Engineers, IT, and business decision making groups. They have to be empowered with a collective accountability and collaboration with a formal training on Big Data and Analytics.

Also, high performing data science teams play a leadership role by enabling each team with automated enterprise content documentation, cloud management, and data visualization, taking the Enterprise reporting and monitoring standards to new heights.

#4 Corporate Training and Digital Culture

Companies that have a strong digital culture are far more agile in their AI ML executions with Big Data Training.

These companies lay extreme emphasis on executive training to foster a culture of experimentation with digital tools such as predictive intelligence, security, automation, Cloud computing, and AutoML.

In fact, by training only 13 percent of your total staff in digital experiments, companies can enhance the success ratio of their data science projects by 32%. The results compound positively, delivering 2x-3x higher value within the first 3 years of the AI ML project timeline.

#5 Eye Long term Goals

It’s still too early to accurately decide and plan how long an AI ML project with Big Data training should run. It could last for weeks, months, or for a year — especially if your data points are gleaned from citizen data science pools harvested to improve utility services.

Strategic AI needs long term planning and this shouldn’t stop you from leveraging technology specifically deployed and embedded to improve the various business process involved in Big Data operations.

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