Are you struggling with your machine learning project? You’re not alone. Luckily, a recent InfoWorld article outlines numerous techniques that can accelerate machine learning processes and also help companies build a better model, including:
Start with Exploratory Analysis
Examining your data in depth is a critical step for ensuring the ultimate success of any machine learning project. Exploratory data analysis is more than just statistical graphics; the approach helps companies keep an open mind rather than trying to force the data into a model.
Tag Your Data with Semi-Supervised Learning
In a perfect world, companies would manually tag all their data so they could easily predict the target value. In reality, this is an expensive and time-consuming endeavor. Semi-supervised learning offers a more realistic approach, in which some data is manually tagged, and companies try to predict the rest of the target values with one or more models.
Add Complementary Datasets
The addition of complementary datasets can often explain anomalies in the original data. For example, combining data on sales, competitive offerings, advertising changes, economic events, and weather can provide a better picture of retail sales fluctuations than looking at the information in siloes.
Try Automated Machine Learning
It used to be that the only way to identify the best models for your data was by training every possible model and determining which was the right fit. Today, companies can avail themselves of automated machine learning to arrive at this answer much more efficiently and with significantly fewer resources.
Customize a Trained Model with Transfer Learning
Training a large neural network typically requires a correspondingly large amount of data, time, and computing resources. Transfer learning customizes a trained neural network by training a few new layers on top of the network with new data or by extracting the features from the network and using those to train a simple linear classifier. This approach can be completed in a matter of minutes with a single GPU, compared to the several weeks and multiple GPUs associated with the legacy approach.
For more on what machine learning can deliver once it’s been fine-tuned, check out this previous APEX of Innovation post.