State of Machine Learning Survey Results Part Two
Recently, we posted the first article recapping our recent machine learning survey. There, we talked about some of the results, such as what programming languages machine learning practitioners use, what frameworks they use, and what areas of the field they’re interested in. In the second of two articles recapping this survey, we now want to discuss additional findings, such as related skills in machine learning and challenges with implementation.
Which of these related skills in machine learning are important to you?
Machine learning practitioners tend to do more than just create algorithms all day. As the chart shows, two major themes emerged. First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation. Second, there’s a strong trend of data storytelling involved, with communication skills, data analytics, and data visualization being common amongst ML practitioners.
What percentage of machine learning models developed in your organization get deployed to a production environment?
On a percentage scale of 0% to 100%, we asked how many machine learning models actually hit the production environment. The answer was simple — a little under 50%. For those reading this article, what blockers prevent deployment? What are some key things that lead to a model being deployed that could help prevent wasted time? Let us know!
What are the biggest challenges in machine learning? (select all that apply)
Related to the previous question, these are a few issues faced in machine learning. Some of the issues make perfect sense as they relate to data quality, with common issues being bad/unclean data and data bias. Though, some softer issues seem to be an issue such as a lack of communication between teams (often between engineers & management), a lack of domain expertise, and unclear business goals.
Looking forward
If you’re interested in learning more about machine learning, Then check out ODSC East 2023, where there will be a number of sessions as part of the machine & deep learning track that will cover the tools, strategies, platforms, and use cases you need to know to excel in the field. Some sessions include:
- An Introduction to Data Wrangling with SQL
- Resilient Machine Learning
- Machine Learning with XGBoost
- Idiomatic Pandas
- Introduction to Large-scale Analytics with PySpark
- Programming with Data: Python and Pandas
- Introduction to Machine Learning
- Mathematics for Data Science
- Using Data Science to Better Evaluate American Football Players
- How to build stunning Data Science Web applications in Python — Taipy Tutorial
- Towards the Next Generation of Artificial Intelligence with its Applications in Practice
- Introduction to AutoML: Hyperparameter Optimization and Neural Architecture Search
- A Practical Tutorial on Building Machine Learning Demos with Gradio
- Uncovering Behavioral Segments by Applying Unsupervised Learning to Location Data
- Beyond Credit Scoring: Hybrid Scorecard Models for Accuracy and Interpretability
- Advanced Gradient Boosting (I): Fundamentals, Interpretability, and Categorical Structure
- Advanced Gradient Boosting (II): Calibration, Probabilistic Regression and Conformal Prediction
- Getting Started with Hyperparameter Optimisation
- Generating Content-based Recommendations for Millions of Merchants and Products
- Machine Learning Models for Quantitative Finance and Trading
Originally posted on OpenDataScience.com
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