For AI innovators, deep learning relies on three key factors: algorithms, computing power, and data. While computing power depends on hardware, model performance hinges on the interplay between algorithms and data.

AI is dominated by machine learning, where algorithms are generally divided into 3 categories: supervised learning, unsupervised learning, and reinforcement learning. Most real-world AI applications currently use supervised learning, which provides feedback on prediction accuracy. This requires manually labeled training datasets to guide the models. With deep learning becoming the mainstream approach, its data-hungry nature calls for high-quality, large-scale training datasets. Essentially, a stellar deep-learning model stems from extensive data. High-quality training data at scale is crucial for model development.

Building AI, however, faces numerous data preparation struggles that impact performance. Let's examine 10 common training data pitfalls and solutions.

10 Training Data Pitfalls and Fixes

1. Raw Data Quality Issues

Raw data may contain noise, missing values, outliers, duplicates, etc. Sensor readings may be inaccurate due to device failures or environment changes. Text data can have spelling errors, inconsistent formatting, or ambiguity. Images can lack pixels, uneven lighting, or distracting backgrounds. Such issues directly undermine model training.

 Solutions:  Perform data cleaning (deduplication, filling in missing values, detecting outliers, etc.), data normalization (min-max scaling, Z-scores, etc.), and use pre-trained word embeddings or image augmentation to handle noise and anomalies.

2. Insufficient Data

As the sole knowledge source for models, accumulating substantial training data is crucial for robust performance. A top cause of AI failures is lacking adequate data to ensure high predictive accuracy. Too little data, especially for long-tail categories, prevents models from acquiring enough knowledge to succeed. Note that data volume needs differ across AI models and industries. Guaranteeing peak deep learning performance requires more quantitative and qualitative data.

 Solutions:  Add more samples for sparse classes, leverage data augmentation, and use transfer learning from related domains with larger datasets.

3. Class Imbalance

Skewed class distributions also undermine the learning of minority classes. For classification tasks, sample sizes may differ greatly between categories. Some classes may greatly outnumber others, causing models to favor frequent labels while performing poorly on rare ones.

 Solutions:  Oversample minority classes, undersample majority classes, or use synthetic minority oversampling techniques (SMOTE). Weighting loss functions to penalize errors on minority classes more can also help.

4. Inadequate Feature Engineering

Feature selection removes redundant or irrelevant variables to identify key features that best capture target dynamics. Common techniques include correlation analysis, chi-square tests, mutual information, and recursive feature elimination. Meanwhile, feature extraction automatically discovers new and more effective representations from raw features using methods like PCA, LDA, or CNN/RNN/Transformers. Getting this right is crucial for model performance, as having redundant or uninformative features can mislead training and waste resources.

 Solutions:  Methodically identify critical feature subsets while discarding redundant ones. Apply feature transformations to yield superior representations and improve separability. Additionally, representation learning techniques like word2vec and BERT have vastly improved automatic feature extraction from text, images, and more. End-to-end deep neural networks can also automatically learn high-level data representations without manual engineering.

5. Improper Data Splits

Before training, data should be divided into training, validation, and test sets, typically in a 70/15/15 ratio. Subsets should adequately represent overall data and prevent leakage causing distorted evaluation. Another common mistake is the overlap between training and test sets. This no longer provides objective evaluations or accurate judgments of model generalization capabilities. Similar issues exist in ensemble learning.

 Solutions:  Splits should strictly control sample independence and avoid repeats. Additional datasets should also be set aside solely for final performance benchmarking. For time series data, follow the “no feedback” principle, where train sets exclude all future time steps.

6. Data Annotation Errors

Alongside model training, AI builders face another hurdle: obtaining properly labeled data. Machine learning for AI model design requires correctly annotated datasets. Inevitable human errors during manual annotation can damage outputs. If data is incorrectly labeled, the final model will be impaired.

 Solutions:  Implement strict quality control workflows in data annotation tools with necessary verification to catch and correct errors. Establish automated auditing systems to examine annotation quality. Combining these techniques better ensures accuracy.

7. Dataset Obsolescence

Existing public datasets often lag model developments. ML models evolve rapidly while quality dataset building requires substantial time investments. This causes distribution shifts where most available datasets gradually become less suited for emerging models.

 Solutions:  Actively source trending and novel sample types lacking in current data. Use the latest models to expose input areas where performance dips, then expand datasets by targeted labeling to enhance coverage. Moreover, incorporate transfer learning techniques to absorb existing knowledge while fusing new data. These initiatives enable quicker dataset iteration to match the relentless pace of machine learning progress.

8. Ambiguity in Visual Data Understanding

Subjective human perception naturally breeds annotation inconsistencies for similar visual samples. Varying interpretations introduce label noise and impede training.

 Solutions:  First, draft detailed annotation guidelines to minimize semantic divergence. Second, spot conflicting labels through cross validation then discuss to align understandings. Third, map visual contents onto standardized semantic spaces automatically to minimize the effects of comprehension differences on annotations. These techniques can unify visual understanding across human labelers, improving dataset integrity to boost model potential.

9. Overly Coarse or Granular Annotations

Annotation granularity directly impacts extractable dataset value. Excessively coarse annotations may inadequately cover required information, while overly fine markings complicate operations and slow progress.

 Solutions:  Use multi-level annotation tools to annotate overall frameworks first to capture key information, then zoom into critical areas for fine-grained annotation. This ensures completeness while controlling workload. Additionally, assisted automatic annotation systems or rule-based annotations can reduce manual efforts while enabling intricate yet full-fledged annotations under time and budget constraints.

10. Prohibitive Data Curation Costs

In-house manual annotation requires extensive labor with lengthy cycles and substantial overheads. Meanwhile, existing public datasets have limited diversity, falling short of tailored demands for particular ML/DL models. Thus, quality training data acquisition becomes the chokepoint hindering performance gains.

 Solutions: Incremental learning can expand specialization edge by building on fundamental knowledge distilled from prior datasets. Another vital lever is optimizing annotation workflows, using auto-labeling to expedite the creation of small but information-rich batches. Exploring avenues to partially recoup expenses from addressing long-tail needs also holds promise. Coordinating these techniques can quality-assuredly reduce overall training data costs.

Finding the ✅ Right Tools to Produce Quality Training Datasets at Minimum Costs

A powerful data annotation platform is critical when facing these training data challenges. For AI students or research teams, a premium toolkit can drastically lift efficiency in data readiness and output. We strongly recommend BasicAI Cloud, an all-in-one smart data annotation platform. With comprehensive, efficient, and automated capabilities, it helps engineers manufacture quality ground truth data at lower costs (even 0!) to concentrate time on model R&D. The highly capable auto-annotation features particularly shine, saving hundreds of hours in repetitive labeling. Collaborative yet granular workflows substantially drive up R&D output.

How BasicAI Cloud turbocharges AI team productivity:

✅ All-Type Data Annotation Tools Enables Broader Dataset Construction

As an integrated hub, BasicAI Cloud furnishes versatile annotation toolkits for 3D point clouds, images/videos, sensor fusion (2D&3D, 4D-BEV), audio, and more. 10+ annotation options facilitate 2D/3D bounding boxes, keypoint marking, lane line annotation, semantic segmentation, etc. for powering object detection, classification, segmentation, and speech recognition models. This provides great flexibility for users to construct rich multi-modal training datasets.

✅ Auto-Annotation and AI Assistance Accelerate Efficiency

Harnessing model-driven auto-labeling and assisted annotation, BasicAI Cloud automatically predicts labels for new samples for review and adjustment by users, substantially cutting repetitive work and lifting productivity. Supported cases include auto annotation / segmentation / object tracking for images and sensor fusion data, plus auto speech-to-text for audio, leaving only human verification necessary to finalize datasets at scale.

✅ Ontology Supports Multi-Level Annotation For Granular Detail Without Compromising Efficiency

Considering annotation granularity directly drives output value, BasicAI Cloud’s data annotation tools implement multi-level Ontology annotation. Users can dive deeper into key area attributes. This strategy balances annotation completeness and workload, fulfilling fine-grained requirements.

✅ Team Collaboration System Optimizes Joint Work With Maximal Quality

The platform incorporates highly efficient collaboration workflows with strict quality control over annotations. Team members or cross-team counterparts can annotate the same data batches in parallel. The workflow also readily integrates auto-annotation models. Moreover, pre-defined rules automatically inspect quality with the identification of deviations for final confirmation by managers. The entire flow tangibly amplifies coordinated productivity while minimizing human errors.

✅ Lower In-House Annotation Costs for Faster Specialized Dataset Acquisition

For AI learners and small research teams, annotation budgets can bottleneck progress. Fortunately, BasicAI Cloud offers exceptionally affordable pricing, even providing full functionality completely free including smart data annotation tools!

Let's benchmark viability for a 5-person crew. The Free Plan already allows each individual seat to prevent editing conflicts. 10,000 model calls sufficiently support medium-scale annotation projects like a 10,000-image dataset for object detection models. The integrated automation tools mean cases like image segmentation may only need manual inspection and correction to finalize datasets.

For heightened needs, Team Plan supplies up to 50 seats with 5,000 cumulative model calls per user. Opting for annual contracts unlocks 20% savings. This should meet most academic group demands. Moreover, subscribers gain technical assistance. For industrial-scale high-volume annotation, BasicAI Cloud provides private deployment and enterprise customization options as well. Users can pick versions that match their exact requirements and budgets.

With flexible pricing, BasicAI Cloud removes barriers for AI learners to hit the ground running – extremely cost-effective for surmounting data obstacles.

Sign Up for a Free BasicAI Cloud Account Now with a 10,000* Monthly Auto-Annotation Quota

*Note: From March 18th, free accounts will come with 10,000 model calls monthly, increased from 1,000 previously.

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