Architecting Effective Data Labeling Systems for Machine Learning Pipelines
Artificial Intelligence (AI) and Machine Learning (ML) have become more than mere buzzwords. These two technologies are currently being used in almost every industry and the stats are evident for that. About 48% of businesses are using ML and data analysis in some capacity, whereas about 65% are considering adopting machine learning pipelines for better decision-making.
Instead of manual efforts, ML offers a wide range of perks for organizations. The technology can enable businesses to learn from their past data, so they don’t repeat the same mistakes over and over again. How does Machine Learning do this? Well, ML does so by analyzing massive chunks of data, extracting them, and interpreting them. However, to make machine learning pipelines work at their peak efficiency, organizations need some additional technologies, and this is where data labeling comes into the big picture.
What is Data Labeling?
In data labeling, raw data- such as images, text, or audio is tagged with informative labels, and these labels enable ML models to learn better and make accurate predictions about buyer behavior, potential whitespaces, market trends, industry demands, forecasts, etc. Some popular data labeling examples include identifying objects in images, sentiment analysis for text, transcribing words in audio, or labeling different actions or sequences in clips or videos.
For instance, in a dataset of images containing cats and dogs, each image can be labeled as either “cat” or “dog,” so the machine learning pipelines can clearly distinguish between the two. With high-quality and accurate data, businesses can positively impact a machine learning model’s ability to generalize and perform well on unseen data. Whereas, inadequate or incorrect labeling can lead to less accuracy, biased models, and ultimately poor decision-making.
A Brief Overview of Machine Learning Pipelines
Machine learning pipelines are responsible for automating the ML workflow and transforming and combining data into an analysis model for generating the output for decision-making. These pipelines manage the flow of data from raw formats to valuable information and support parallel systems for evaluating different ML methods.
With these pipelines, organizations can:
- Improve their predictive analysis capabilities
- Build recommendation systems that can suggest to customers a related product or service when they purchase one.
- Detect fraud, security breaches, or anomalies across the enterprise’s IT ecosystem
- Facilitate real-time decision-making
Every machine learning pipeline is made up of different stages and every stage in a pipeline is fed with data.
Understanding the Role of Data Labeling in the ML Pipeline
Typically, machine learning models are trained using three methods-
Supervised
Unsupervised, and
Reinforcement training
In supervised training, data labeling is used where each input is paired with the correct output label. The model learns to associate input features with output labels, so it can predict outcomes for new, unseen data. On the other hand, unsupervised learning works with unlabelled data to identify hidden patterns or clusters within datasets, and reinforcement training involves a trial-and-error method, where human evaluators provide feedback.
The majority of contemporary machine learning models are developed using supervised learning techniques because accuracy is a key component of ML, and the supervised model offers accuracy efficiently.
Development Lifecycle of Machine Learning Models
Creating Machine Learning models is no cakewalk. It takes hours of hard work and multiple stages. To break the entire process down for you, here’s what goes in the development lifecycle of machine learning models-
Data Collection and Pre-processing
Before the data labeling process can happen, data must be collected and pre-processed. In the pre-processing phase, raw data is gathered from a wide range of sources like log files, databases, sensors, and APIs.
Using this data in raw format isn’t possible as it lacks a standard structure or format and could be riddled with inconsistencies like outliers, missing values, or duplicate records. In addition, during the pre-processing stage, data is also cleaned, formatted, and processed or transformed, so it can be compatible and consistent with the data labeling process.
Analysts use different techniques like eradicating rows without any values or deploying imputation to evaluate values through statistics and identifying and flagging outliers.
Data Labeling and Annotation
After the pre-processing phase, the transformed data moves on to the labeling or annotation stage. Here, data is assigned labels or annotations, so the machine learning model has the information it needs to learn.
However, remember that the labeling approach can vary depending on the type of data being processed. For instance, annotating text and images requires two distinct methods. There’s no denying that automated labeling tools are available to streamline machine learning pipelines, but human intervention is indispensable to ensure accuracy and minimize any biases that automated systems like Artificial Intelligence might introduce.
Once the data is labeled, it proceeds to the QA checks, where the labeled data is checked for consistency, precision, and completeness. In addition, at times the QA system also includes double-labeling, where multiple annotators independently label a data subset and review it to resolve any discrepancy.
Model Training and Evaluation
During this process, the labeled data is used to identify any connections between the labels and the inputs and learn about the patterns. Here, the parameters of the ML model are altered iteratively to enhance the prediction precision relative to the labels.
Then, the ML model is tested with a separate set of labeled data that it hasn’t faced before to assess the effectiveness of the model. If the performance isn’t as great as it should be for metrics like recall, accuracy, etc. adjustments have to be made before retraining. To do so, professionals refine the training data to eliminate biases, noises, or any potential data labeling issues.
Deployment and Monitoring
If the ML model passes all the QA checks` and performs well, it is deployed into a production environment where it is bombarded with real-world data. Even in this final stage, professionals monitor the performance of the model to detect issues like data drift or degradation in accuracy, so they can identify when updates or retraining is necessary to maintain the model’s effectiveness.
Types of Data Labeling in Machine Learning Pipelines
In machine learning pipelines, data labeling can be done in different ways, and each method has its own pros and cons based on factors like data complexity, size, and expected accuracy. These include:
- Manual Labeling: Here human annotators assign labels to data points. Although manual labeling is highly accurate, it is time and cost-intensive.
- Automated Labeling: In the automated processes, ML models are used to pre-label data, which can be refined by human viewers.
- Crowdsourcing: This involves distributing the labeling task across different individuals, via platforms like Amazon or Mechanical Turk to handle large datasets.
- Programmatic Labeling: It uses rules or heuristics, such as NLP, keyword matching, or image recognition algorithms to label data systematically.
- Semi-supervised Labeling: Lastly, this technique combines labeled and unlabeled data. The labeled datasets are used to label unlabelled ones through algorithms like clustering, or similarity analysis.
It’s a Wrap!
If you are considering labeling data for your machine learning pipelines, it’s crucial to understand that data quality plays a crucial role in this. Ensure to choose a data labeling service that can provide you with high-quality data and be elastic enough to maintain the data quality when you are trying to scale your workforce up or down as per your business or project needs.
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