How Data and Machine Learning Model Choices Impact Deployment Rates

When you’re building a machine learning model, there are a few critical components to consider: calculation power, the algorithm, and the data. Often, a company will focus most of its resources on developing the right, bias-free algorithm and investing in more calculation power. Data is often an after-thought or forgotten entirely until it’s time to run the model. When data gets forgotten, it can slow your deployment rate and lessen the success of your machine learning model. Before you deploy your machine learning model, it must be trained with good data, data that has been optimized for the issue that you’re dealing with. Data must be sourced, formatted, cleaned, sampled, and aggregated before being used. Until you’ve got high-quality, annotated data, you can’t deploy your machine learning model. Getting a data set isn’t the problem. The problem is finding quality data that fits your use case. Luckily, creating high-quality, accurately annotated data is getting more efficient and less expensive.

Get the Data Right

For successful machine learning deployment, getting the data right through annotation and from high-quality sources is key. For many teams, this means bringing the data annotation process in-house. And yet, data annotation is a repetitive, time-consuming task. If your data scientists spend time labeling and preparing data, that’s precious time that they could have spent on other projects. Using high-quality data is important, but it’s not cost-effective to bring data labeling in-house for most AI projects. Let your AI team focus on building the AI model, refining the algorithm, and getting ready for deployment. Let someone else create the high-quality data set that you need. While the cost of high-quality data might seem expensive up-front, look at it as a cost-saving measure. By outsourcing your data preparation, you save your team the time it would take to create the data set and properly annotate it. Using the right data at the beginning will also save you time after you deploy your machine learning model. If you don’t spend enough time on the data up-front, you’re likely to run into problems with your algorithm. This can result in expensive model retraining. When your company is already investing a lot of money in an AI project and leaning on it to solve a company-wide problem, you need it to work the first time around. There are a number of real-world examples where cost gets in the way of successful deployment of AI projects. Gartner estimates that only half of all AI projects ever make it to deployment and those that do take an average of nine months. And when it goes wrong, it’s too expensive to fix. OpenAI found a mistake in GPT-3, but the cost of training was so high, that retraining the model wasn’t cost-effective.

Get the Right Data

When working with data, the natural assumption is that more data equals better data. But, when you’re training a machine learning algorithm, you’d be smart to follow the old adage: quality over quantity. Using a high-quality, small data set can save you money overall. You can save a little money from your compute budget and reallocate it to purchasing a high-quality, small data set. By using a small, but high-quality data set, you don’t have to spend as much on computing and can avoid having to retrain your model after using an entire organizational data set. You’ll find that buying the right data is money well spent. With more than 75 percent of companies saying they have AI models that never get deployed, it’s smart business sense to spend your money on the right data to get your machine learning model functional and ready for deployment. For a more successful deployment of your machine learning model, follow these steps to getting the right data.

Find a High-Quality Data Source

The first thing you’ll want to do is find a data source where you can purchase a high-quality data set. Choosing a reliable source where you know you can get good data that fits your use case is key to successful deployment of your machine learning model. When you’re looking for a dataset to fit your use case, you have a few different options. You can hire a company to create a unique dataset for your use case and company or you can build the dataset yourself. Another option is to find an off-the-shelf dataset. An off-the-shelf dataset is a dataset that has already been put together and is available for use. You can even find open-source datasets, though these are often of less quality or size and may not be enough to support your project. An off-the-shelf dataset is a great option for low-budget projects or for teams that don’t have enough team members to build their own dataset. There are a number of different repositories where you can find a variety of off-the-shelf datasets for your needs. An example of how an off-the-shelf dataset can solve a business problem comes from MediaInterface, a language technology company that operates primarily out of Germany, Austria, and Switzerland. When the company was looking to expand to France, they realized they needed a significant amount of new data — in French. We were able to help them get the data they need with a high-quality, off-the-shelf dataset.

Look for Small and Wide Data

While using a large data set to train your machine learning model seems intuitive, using a small and wide data set can actually be more cost-effective and useful in the long run. And, to be clear, a small dataset doesn’t mean a small amount of data. Small data means the right data to solve your problem. Training your machine learning model with a small and wide data set can provide you with more robust analytics, reduce dependency on big data, and deliver a richer, more nuanced algorithm. To create a high-quality, small dataset, you’ll want to focus on:
  • Data relevance
  • Data diversity over repetition
  • Building a data-centric model
Switching to small and wide data sets will make the AI industry less data-hungry over time. Using small data can return useful insights with less time spent computing and training the model.

Use Resources More Efficiently

By using high-quality, small data sets, you can use your company’s resources more efficiently and effectively. Training a machine learning model is complex and uses a number of different resources, including time, money, and computational power. By using your resources more efficiently, you can deploy AI models more effectively. A great resource for building enterprise-scale AI applications is NVIDIA TAO, which stands for Train, Adapt, and Optimize. The application is an AI-model-adaptation framework that helps companies to simplify and accelerate their building of AI models. Essentially, you get to pick from their library of pre-made AI models then customize it to your unique use case. This allows your company to deploy your AI solutions faster and more cost-effectively. Using a tool such as TAO and purchasing a moderately-priced off-the-shelf dataset are both ways in which you can more efficiently use your company’s resources.

AI Implementation Challenges

When it comes to deploying machine learning models and AI, there are many challenges and difficulties. This has been mainly due to the scope, scale and data choices, but the industry remains steadfast and optimistic. People are now focused more on point solutions and internal efficiency use cases for AI, with a data-centric lens, which sets them up for success. By refocusing on using resources efficiently and finding the right data, you can avoid some of these implementation and deployment challenges. A whitepaper by Alation found that 87 percent of employees cited data quality problems as the reason their company failed to embrace AI technology. AI data quality can be fixed by using the right data sources. Don’t waste company resources by bringing your data annotation in-house. Instead, go straight to the source and buy a small and wide data set with high-quality, well-annotated data that’s right for your machine learning model. Focus your company resources on building the AI algorithm and leave the data to the experts.

The Right Data Leads to a Faster to Production Releases

When you start with good data, you get good results. Organizations that pay attention to data early on in their deployment plan make it to production sooner and with less compute waste along the way. With such a strong focus on data, you’re also building a successful long-term strategy for your AI deployment. At Appen, we believe that having data that is fit for your purpose, standardized, and stored in a future-proof system makes it easier to access that data in the future and use it for more projects. It’s part of our philosophy for building and creating responsible AI, which we cover in-depth in our book, Real World AI. When you focus on data from the start, you’re building the foundations of your data ecosystem.

Setting Up a Data Ecosystem

A data ecosystem is a loose system and framework for storing data coupled with a way of sharing data. In your data ecosystem, you’ll need to have data producers, data consumers, and data platforms. Building your data ecosystem is a way to build your company’s data foundations. One very important step in this process is to build trust in data. You must have robust data governance policies and processes in place to ensure that all of the data you’re using is high-quality. When you know all of your data is good data, you know you can trust your data. Being able to trust your data means that you’ll be able to deploy your machine learning projects faster and trust in the results you receive.

How Appen Can Help

If you’re looking for a data partner to add to your data ecosystem, Appen can help. We work with a team of over a million skilled contractors to label and annotate data for thousands of different use cases. We have the data you need to be able to deploy your machine learning models and meet your AI goals.
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