What are machine learning’s primary performance challenges?
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While machine learning has become a powerful and exciting tool for a diverse range of industries, there can be significant challenges when it comes to scaling these algorithms for wider purposes. Understanding these roadblocks can help you anticipate them before they arise and design algorithms from the get-go that are better suited to be scaled. Here are some of the biggest challenges around scalability and performance for machine learning tools.??
1. Data management: Machine learning models need massive data sets in order to be accurate, and managing this data can be difficult. Data needs to be collected, curated and stored in a way that makes it easily accessible to the machine learning algorithms. It also needs to be cleaned and pre-processed so that it can be used effectively. As the volume of data being used becomes larger, data management can also become more complicated.?
2. Computational power: Machine learning algorithms can be computationally demanding, requiring a lot of resources to train and run. As data sets get larger and models get more complex, the amount of resources needed will only increase. This can limit the scalability of machine learning systems and may slow down performance.
3. Interpretability: Machine learning models can be "black box" models, which means it can be difficult to understand how they work or why they make certain decisions. This can be a problem when trying to use these systems in applications where transparency is important and having an inside look at operations is essential.
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6. Bias: Machine learning models are only as good as the data they are trained on. Therefore, if the data is biased, the model will be biased as well, which can lead to inaccurate predictions and poor performance. Ensuring that data sets are representative and unbiased is crucial for scalable machine learning systems.
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This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.
Software Engineering Manager
2 年There are so many challenges, but in my view some of the key ones include: 1) Interpretability: Understanding why your model performs the way it does and how it can be fixed if its performance is unexpected. 2) Dealing with unbalanced and biased datasets: How do you find a needle in a haystack when you really only know what hay looks like and you'll perform quite well if you just say everything is hay 3) Feature engineering and data conditioning/combining
Professor Of Physics at University of Michigan
2 年Access to affordable computing time.
Business Intelligence Manager | Architect | Data Enthusiast | Doer
2 年#1 . Trust in Data #2. Adaptation by Business #3. Transparency in process for business to understand and make decisions #4. results against common beliefs gained over many years of experience
Engineering Leader | CS Ph.D. | Big Data and Cloud SME | Co-author of a Wiley Data Science textbook.
2 年Challenge #1: Data trustworthiness A well-known 80-20 rule in the data analytics lifecycle says people typically spend 80% of their effort on data preparation and only 20% of the effort on building and running machine learning models. Despite the data preparation takes most of the time, data quality remains to be a key challenge for Machine Learning to achieve scale and maintain performance. After all, Machine Learning can only perform as well as the data that's given to the models. Challenge #2: Model training for supervised learning Although it's easier to find a pre-built model these days compared to 10+ years ago, the pre-built model is being built over specific training sets that may or may not resemble the characteristics of the testing sets you run the model against. To address the problem, you would need to train and build your own model. But training takes time and it requires well-labeled output variables in your training sets.