Inference • Last Updated 1/30/2024

Inference in machine learning: Challenges and solutions

Overcome some of the main challenges of inference in machine learning to improve the accuracy of your machine learning projects.


By Kelsie Anderson

In machine learning, the journey from concept to real-world application hinges on one critical phase: inference. Inference is the process where a meticulously trained machine learning models face the ultimate test of interpreting new, unseen data to make predictions or decisions.

Navigating the inference landscape comes with its own set of challenges. From the daunting task of managing large-scale data to grappling with hardware limitations, each hurdle requires a nuanced understanding and strategic approach.

For developers and business owners alike, these challenges can seem overwhelming, but they also present an opportunity to innovate and optimize. In this blog post, we'll explore the common obstacles encountered during machine learning inference, as well as practical, effective solutions. With this knowledge, you can overcome these challenges and enhance the efficiency and accuracy of your machine learning projects.

Key components of inference

The inference process includes three main pieces:

  1. Model deployment: Implementing the trained model in a production environment.
  2. Input data: Providing new, unseen data to the model.
  3. Prediction: The model's output based on the input data.

A process with just three key components might seem relatively simple. However, there are several common challenges organizations face when trying to leverage and implement machine learning inference.

Common challenges in machine learning inference

Inference is the phase of machine learning where models are put to the test to see if they’re actually useful. Ultimately, inference is all about facing and solving challenges. But there are challenges inherent to inference itself that developers must figure out how to overcome before putting their models to the test.

In this section, we’ll take a look at the main challenges developers face with inference, as well as their solutions.


Generative AI applications, such as real-time content creation or augmented reality, demand instantaneous inference. To ensure timely predictions, inference needs to occur close to the end-user and the data source. Relying on centralized computational hubs can introduce unwanted delays.


Deploy or leverage networks of GPUs distributed closer to your data and end-users rather than centralizing them at a single hub. By processing data closer to its points of reception, this decentralized approach can significantly reduce latency.

Additionally, consider edge computing solutions that process data near its source, ensuring faster response times for real-time applications.

Scalability concerns

Scaling machine learning inference is challenging due to the increasing data volumes and model complexity which require more computational power and memory. This raises costs— particularly for specialized hardware and cloud resources—which can heavily impact smaller organizations.

Additionally, managing distributed computing across various locations introduces complexities in data synchronization and network latency. Finally, fluctuating workloads demand sophisticated resource management and auto-scaling solutions to maintain efficiency and performance.


Addressing inference scalability challenges involves a comprehensive strategy. Model optimization, through techniques like quantization and pruning, reduces computational demands, while efficient algorithms manage complex workloads. Cloud computing offers scalable resources for fluctuating demands and large datasets, and edge computing reduces latency by processing data near its source.

Specialized hardware (like GPUs and TPUs) accelerates inference tasks. Distributed computing enhances processing power by spreading the workload across multiple locations. Auto-scaling services and batch processing efficiently manage resources and data volumes, respectively. Finally, effective data management, including caching, and regular system monitoring and analysis, further optimize performance.

This integrated approach ensures effective scalability to meet various operational needs.

Model drift

Over time, the distribution of input data may change, causing a model's performance to degrade. As a result, the predictions made by the model become less accurate because the data it was trained on no longer represents the current situation.

This phenomenon, known as model drift, is a significant challenge in machine learning because it can degrade the performance of a model over time—often without immediate or obvious signs. Addressing model drift is crucial for maintaining the accuracy and reliability of machine learning systems, especially in dynamic environments where the data can change rapidly.


Continuously monitor the model's performance to detection accuracy decline early. Implement feedback loops for expert evaluation and anomaly detection techniques to identify and correct drift. Regularly update or retrain the model with the new data and feedback you receive to ensure it adapts to current trends.

Employing adaptive learning models and data stream analysis can also help in automatically adjusting to changes in data patterns. Feature engineering, to keep the model relevant, and ensemble methods, for robustness, are also effective. These combined efforts ensure sustained model accuracy and reliability.

Hardware limitations

Hardware limitations significantly challenge machine learning inference due to the high computational power and memory requirements of advanced models, particularly deep learning networks. These models demand extensive resources for efficient real-time processing, leading to issues in environments with limited computational capacity or energy constraints.

Additionally, the cost of high-end hardware like GPUs can be prohibitive, especially for smaller organizations, impacting scalability and access to necessary resources. Furthermore, intensive computation generates substantial heat, necessitating effective cooling solutions. These factors collectively require a balance between hardware capabilities and the practical demands of machine learning inference, often requiring optimization and alternative solutions.


Opt for lightweight setups that use simpler or compressed machine learning models. They require less computational power, making them suitable for devices with limited processing capabilities. These models maintain a balance between performance and resource usage, ideal for applications where speed and efficiency are crucial.

Edge computing, on the other hand, involves processing data directly at or near its source rather than relying on distant cloud servers. This approach reduces latency, as data doesn't need to travel far, and decreases the load on central servers. It's particularly effective in real-time applications and in scenarios with limited connectivity or bandwidth.

Inconsistent data quality

Inconsistent data quality directly impacts model accuracy. When models are trained on poor-quality data, they struggle to learn accurate patterns, leading to biased or incorrect predictions. This issue is compounded by the increased complexity in preprocessing required to clean and normalize inconsistent data, which can be both time-consuming and resource-intensive.

Additionally, diagnosing performance issues becomes more challenging, as it's difficult to discern whether problems arise from the model or the data quality. Furthermore, training on such data increases the risk of overfitting, where the model learns noise instead of the underlying patterns, diminishing its ability to generalize to new data.


To counteract inconsistent data quality, it’s crucial to implement robust data preprocessing and validation steps. Data preprocessing involves thoroughly cleaning the data by identifying and correcting errors, handling missing values, and normalizing data to ensure consistency. Validation steps include checking for accuracy, completeness, and relevance of the data to the problem at hand.

These processes help create a reliable dataset that accurately represents the real-world scenario the model is intended to address. By ensuring the input data's consistency and quality, models are more likely to learn the correct patterns, leading to improved accuracy and reliability in their predictions.

Access to infrastructure

Access to infrastructure poses a significant challenge in machine learning inference due to the escalating demand for powerful computational resources, particularly GPUs. These resources are essential for efficiently processing the complex calculations required in machine learning models. However, the surge in demand has led to widespread shortages, leaving many companies in a bind.

They often face extended waiting periods to acquire the necessary GPUs, which in turn causes considerable delays in initiating or scaling AI projects. This bottleneck not only slows down the development and deployment of machine learning models but also impacts the overall innovation and competitiveness of businesses relying on AI technologies. The scarcity of these critical resources underscores the need for alternative solutions or strategies to mitigate the impact of these infrastructure constraints.


Consider cloud-based machine learning platforms to address the challenge of accessing infrastructure for machine learning, especially due to GPU shortages. These platforms offer GPU access as a service, providing scalable computational power on demand without the need for companies to invest in and maintain physical infrastructure.

This approach circumvents the issue of hardware shortages and offers flexibility and scalability. Additionally, explore alternative hardware accelerators like TPUs (Tensor Processing Units) or FPGAs to provide efficient processing capabilities, often at a lower cost or with different performance benefits compared to traditional GPUs.

Choose Telnyx for inference infrastructure

From the high demands of processing power to the ever-present specter of latency, each inference hurdle requires a thoughtful, innovative approach. The solutions we've discussed can pave the way for smoother inference operations. But they also highlight the incredible pace at which machine learning continues to evolve. By embracing these solutions to the challenges of machine learning inference, your business can ensure its machine learning models are functional and optimized for the high-speed, data-driven demands of today's digital landscape.

Finding a partner that prioritizes high-quality inference solutions is one way to ensure you’re operating on the cutting edge of the industry. We designed our Inference solution to meet the high standards of today's tech landscape, ensuring models run efficiently, your data is processed swiftly, and your business stays ahead of the curve.

As you consider the next steps for your machine learning projects, remember that Telnyx stands ready to deliver the cutting-edge infrastructure and support you need. With our robust platform, expert guidance, and unwavering focus on quality, you can build high-quality solutions that incorporate the latest in machine learning technology.

Contact our team to learn how you can leverage Telnyx Inference for your generative AI applications.

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