5 Ways To Get Started With Artificial Intelligence And Machine Learning

Introduction

The time to get started with artificial intelligence (AI) and machine learning (ML) is now. Organizations across industries — as well as your competitors, suppliers, and partners — are using the technologies to automate their processes, augment existing capabilities, and improve operations. All the while, they are transforming key functions such as sales and marketing, risk mitigation, supply chain management, and manufacturing.

According to Gartner, 59% of organizations already have deployed AI. On average, these organizations have four AI or ML projects in place. By 2022, that number will increase to an average of 35 AI or ML projects, the analyst firm predicts.

If you are not investing in AI and ML right now, you risk being left behind in the Fourth Industrial Revolution. Yet many organizations hold off because of concerns over skills availability, data quality, and a lack of understanding over AI use cases. The reality is that new technology solutions are available today that eliminate the need for organizations to build AI systems from scratch.

AI/ML solutions are available from Dell Technologies that:

• Enable quicker deployment of apps

• Eliminate the need for extensive hardware and software testing

• Reduce integration complexity.

The Dell Technologies AI solutions portfolio includes hardware, software, and services, all of which span use cases across industries and from the edge to core to cloud implementations.

The solutions are rooted in extensive expertise in data science and research. Are you ready to get started? Here are five main ways to get the most from AI.

1. Have a Clear Goal in Mind

To be successful with AI, you need to have a clear idea of the business problem you want to solve. ML can help automate many of your business processes, especially the routine and repetitive ones. It also reduces the need for human intervention so you can assign people in your organization to tasks that deliver higher value.

Take customer service and sales recommendations. Using AI to automate these processes can help speed up response times, reduce errors, and minimize the need for human involvement in assisting or advising customers.

AI can help with natural language processing, recommendation engines, and risk analysis — improving capabilities in application areas where there is too much data for people to be able to handle efficiently. MasterCard, for instance, is using AI to detect and stop fraudulent credit-card transactions and other types of fraud. Its system analyzes card transaction data in real time and determines whether it might be fraudulent based on the card user’s previous usage history and spending behavior. With hundreds of millions of MasterCard transactions taking place daily across the globe, such analysis would be near-impossible to conduct at speed with human resources alone.

Logistics, preventive maintenance, just-in-time supply chain management, and dynamic pricing are other examples of processes that can benefit from the application of AI and ML. In the healthcare and life sciences sectors, organizations are using ML for everything from predicting hospital admissions to diagnosing and treating patients, tracking infectious diseases, and developing pharmaceuticals.

If you are just getting started, look for opportunities where you can automate a process quickly and easily. Go for the low-hanging fruit first because it is easier to demonstrate value.

2. Know Your Data

Once you have identified a viable use case or project for AI at your organization, take a look at your data. Remember, the results you get from using AI and ML are only as good as the quality of the data that is available for the business process or problem you want to tackle.

Before you get started, verify that you have the necessary data, need to acquire more or different data. Figure out whether you need to collect data from entirely new sources or whether you have to collect it more frequently from your existing sources in order to get meaningful results. Is your data relevant and timely, and does it relate to the outcomes you want from the business process you are automating? Sometimes it might become necessary to supplement your existing data with information from external sources — such as traffic or weather information that can impact buying patterns.

Inaccurate or incomplete data can undermine the reliability of any analytics you do with AI and ML. Before you start, make sure your data is clean, accurate, and normalized. You need to remove errors, fill in any missing gaps, weed out redundancies and duplicates, and unify data across systems.

There’s a high likelihood you will need to modify, classify, categorize, and tag your data in order to get it ready for automation. Your AI/ML algorithm will need a good set of training data to learn from so it can make predictions and decisions accurately. Explore standardized, free datasets to build on work others have done.

Data quality is not the only concern; so is data location and access. Giving AI and ML tools direct access to data for training and modeling purposes — instead of copying it out of another system — can have a big impact on performance.

3. Identify the Tools You Will Use and the Skills You Need

The wide range of commercial and open source tools available for ML can make choosing one a daunting task. The best place to begin is to determine which tools you already have in place and whether they are suitable for meeting your objectives. Gauge the expertise of your data scientists and the tools they already know how to use. Figure out what approaches your team is interested in and what new skills they need to acquire.

When embarking on the AI and ML journey, identify processes that can be easily automated. Automating the simple tasks gives you an opportunity to build internal skills without having to invest in new tools and frameworks.

Consider using open source ML environments such as Anaconda Python and the R programming language to develop skills internally where possible. When it comes to the actual toolkits to use, consider platforms such as TensorFlow, Caffe2, and PyTorch. The immense popularity of these platforms will likely make it easier for you to find the skills you will require to meet your objectives with AI and ML.

In addition, explore AI software. As AI software continues to improve, a number of software makers have streamlined their data pipelines, incorporated toolsets and environments, and built in comparison tools to make it easier than ever to see which AI models produce the best results. Consider scale as you’ll need solutions that can grow with you.

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5 Ways to Get Started with Artificial Intelligence and Machine Learning

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