Machine learning is one of the strongest trends in today's tech scene. A recent survey revealed that 61% of organizations picked machine learning as their most critical data initiative for 2018.
Moreover, between 2013 and 2017 machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR), becoming the third-fastest growing category of all patents granted – with giants like IBM, Microsoft, Google, and Facebook as the most prominent patent producers last year.
Tech companies are re-orienting themselves around machine learning and Artificial Intelligence (AI) technologies, dedicating resources to the development of algorithms that promise to bring about the machine intelligence revolution.
Developing machine learning algorithms is a challenging and time-consuming process. However, there are some things developers can do to become more productive and deliver machine learning solutions faster.
Read on to learn what factors impact development speed in machine learning and how developers can boost it.
Here are 3 critical factors that set the pace for developing machine learning algorithms:
Most organisations delve into machine learning algorithms when they can't find any available implementations of the algorithm they need, or the implementation they find doesn't match their criteria (for example, it’s not fast enough).
Standard machine learning libraries that help developers boost their speed, but developers need to know how to utilise them properly for their particular use case. If you need something different, you can either try general-purpose libraries, or have developers build an algorithm from scratch – which takes us to the next point...
Developers who aim to take advantage of the sophisticated nonlinear methods that are part of machine learning need to prepare for acquiring much more data than in the case of their linear counterparts – and have a lot of work ahead in front of them!
To process all this data, algorithms need to be fast, and that in itself is a serious engineering challenge that requires adequate knowledge. Moreover, machine learning algorithms always give results, but it's the job of developers to make sure that these outputs are correct. And for that, they need to have a deep understanding of machine learning techniques to show that the implementation is correct.
To train a machine learning model, developers need a set of labeled data. That’s why humans need to first identify and label data points we want our algorithm to recognize. The more labeled data we feed into our system, the more comprehensive its training, and the better its results. Getting hold of a large volume of clean and consistently labeled data for algorithm training is time-consuming and expensive.
For example, if you want to analyze user reviews of products, you’ll need at least 90,000 reviews to build a model that performs adequately, according to AltexSoft. If we assume that labeling a single comment takes a worker 30 seconds, a single personthey will need to spend 750 hours to complete the task.
Use machine learning tools and libraries
Traditional software engineering tools don't bring the results we want in machine learning. However, tech giants are now busy building machine learning-specific platforms that offer end-to-end functionality to speed up the process.
Moreover, developers can take advantage of various off-the-shelf implementations available in open-source libraries that were built specifically for speed. Using a standard machine learning library helps to speed up the development process, provided that developers know how to use them properly. Some libraries are meant for general-purpose use and operate correctly on a wide range of problems, but their robustness comes at the cost of speed.
Boost performance by changing the training data
Sometimes creating a different perspective on your data helps to show the structure of the problem you're trying to solve to the learning algorithms the structure of the problem you're trying to solve. Developers can boost their performance by getting more or better quality data.
If you can't get your hands on more data, perhaps you can generate it by augmenting or permuting the existing data? Data cleaning is also an essential step to improving performance.
Another strategy is changing the type of prediction problem you're trying to solve. You can do that by reframing your data as a regression, anomaly detection, recommendation engine, etc. to serve your new problem type.
Take a closer look at your algorithms
By checking which algorithms perform better than average, you'll be able to improve performance. You can evaluate algorithms using metrics that capture the requirements of your problem and domain, check the baseline performance for similar algorithms, analyse which linear and non-linear algorithms work well, and review the available literature to see which algorithms work best for the problem you're trying to solve.
The next step is algorithm tuning which is time-consuming, but well worth the effort because it allows making the most of high-performing algorithms. Delve into algorithm diagnostics to see how it performs., Check parameters or parameter ranges used in the literature on the topic, and see which parameters you can optimise (for example, tune your algorithm's structure or learning rate with a direct search procedure or stochastic optimisation).
The above is just the tip of the iceberg when it comes to performance optimisation of machine learning algorithms.
Partnering with a team of talented developers who have in-depth knowledge of machine learning is also critical to boosting the development speed in this area.
Have you got any questions about improving development speed in machine learning? Give us a shout out in comments; our machine learning experts will be happy to answer your questions and help you reach new levels of performance.