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Machine Learning Failures: Why Only 53% of ML Algos Reach Production?

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Machine learning models can be amazing decision makers. They are a cutting-edge system that simplify analytical thinking for business users who face increasing global competition and strategic challenges.

Production is the final and most crucial step in seeking these decision capabilities. In this stage, the model transitions from experimentation to a practical environment and delivers its intended value. While effective deployment should be planned before the beginning of development, the industry canvas displays an opposing reality. Astonishing results by Gartner reveal that most of the ML algorithms (53%) fail to get deployed because they are just not fit for production.

There are several reasons why most models fail to reach their expected destination. This blog presents four of them and explores in detail the role of each in making ML algorithms unfit for production. This information is useful for both data scientists and business leaders who want to overcome barriers in effective model deployment.

What is Deployment in Machine Learning?

Most often, data scientists build machine learning applications in an offline environment where they tune and test the model on limited data and computation requirements. 

Real-world scenarios are different. The application needs to retain performance on new and increasing data, while also meeting growing user demand. In the deployment stage, a data scientist creates a suitable production environment that considers infrastructure requirements– such as distributed processing, to make the application work optimally. Additional characteristics of a suitable runtime environment include ensuring data quality standards, model performance, scalability and interpretability. Ensuring robust deployment is key to successful model development and creating a plan early on is crucial in achieving production success.

So the Question is: Why Most of the Machine Learning Models Fail to Reach Production?

Successful deployment of machine learning models rely on robust planning and comprehensive assessment of what is required of a model in real world scenarios, where user demand may vary and growing amounts of unseen data arrives. 

In many cases, machine learning models lack key characteristics for a successful launch. Below is an explanation of four of these characteristics that cause a model to fail.

Poor Quality and Irrelevant Data: The first hurdle in effective model deployment is low-quality and irrelevant data for training. In many cases, data is presumed to be ready, leading to erroneous model performance, making it unfit for production.

Data collection and preparation can be complex and time-consuming, requiring experts to carefully create versions of data to determine which one best achieves model performance. Engineering sufficient, relevant and unbiased data is crucial to achieving effective training of the model and is the foundation of successful production.

Lack of Scalability: Machine learning models that are designed for small-scale experimental setups may struggle to scale-up for high data volumes and user demand environments. A robust model design could be created using distributed algorithms and scalable deep learning frameworks, leading to lower computational complexity. Additionally, planning infrastructure with parallel processing capabilities and using containerization technology can efficiently distribute user workload, scaling resources up and down as required. 

Protip: A container isolates the model and its dependent software components into self-contained units that can be easily replicated or removed, facilitating any number of users.

Overfitting and Generalization Issues: Overfitting occurs when a machine learning model becomes overly specialized to the training data, resulting in poor model performance on new, unseen data. Though  an overfitted model will perform effectively on test data, when it comes to data on real-world scenarios, it fails to accurately predict, leading to failed production. 

An overfitted model captures noise and random fluctuations in data, leading to becoming too specific and sensitive to training data. Ensuring effective generalization of the model is crucial for successful deployment, and avoiding overfitting is a critical consideration.

Interpretability and explainability: One major challenge in deployment of machine learning algorithms is a lack of understandability and interpretability of the decision making process. Some machine learning models, especially deep neural networks are highly complex and black box in nature. This lack of explainability can pose a challenge in successful deployment of models in a production environment where insights into the decision making process are crucial.

Industries with ethical and regulatory considerations– such as finance and health care, demand models that have understandable explanations of how predictions are made. This is to ensure that patients get safe treatment and finance decisions remain unbiased and fair. Ensuring use of robust interpretability algorithms such as MIT and IBM’s recent open source code can effectively achieve transparency and trust in model decision making in the production environment. 

Addressing the above challenges requires a comprehensive approach that encompasses data quality improvements, scalability considerations, algorithmic robustness and interpretability techniques. By overcoming these hurdles, there’s a greater chance that machine learning algorithms will successfully reach production.

Ayesha
Ayesha
I engineer the content and acquaint the science of analytics to empower rookies and professionals.
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Machine Learning Failures: Why Only 53% of ML Algos Reach Production?

Machine learning models can be amazing decision makers. They are a cutting-edge system that simplify analytical thinking for business users who face increasing global competition and strategic challenges.

Production is the final and most crucial step in seeking these decision capabilities. In this stage, the model transitions from experimentation to a practical environment and delivers its intended value. While effective deployment should be planned before the beginning of development, the industry canvas displays an opposing reality. Astonishing results by Gartner reveal that most of the ML algorithms (53%) fail to get deployed because they are just not fit for production.

There are several reasons why most models fail to reach their expected destination. This blog presents four of them and explores in detail the role of each in making ML algorithms unfit for production. This information is useful for both data scientists and business leaders who want to overcome barriers in effective model deployment.

What is Deployment in Machine Learning?

Most often, data scientists build machine learning applications in an offline environment where they tune and test the model on limited data and computation requirements. 

Real-world scenarios are different. The application needs to retain performance on new and increasing data, while also meeting growing user demand. In the deployment stage, a data scientist creates a suitable production environment that considers infrastructure requirements– such as distributed processing, to make the application work optimally. Additional characteristics of a suitable runtime environment include ensuring data quality standards, model performance, scalability and interpretability. Ensuring robust deployment is key to successful model development and creating a plan early on is crucial in achieving production success.

So the Question is: Why Most of the Machine Learning Models Fail to Reach Production?

Successful deployment of machine learning models rely on robust planning and comprehensive assessment of what is required of a model in real world scenarios, where user demand may vary and growing amounts of unseen data arrives. 

In many cases, machine learning models lack key characteristics for a successful launch. Below is an explanation of four of these characteristics that cause a model to fail.

Poor Quality and Irrelevant Data: The first hurdle in effective model deployment is low-quality and irrelevant data for training. In many cases, data is presumed to be ready, leading to erroneous model performance, making it unfit for production.

Data collection and preparation can be complex and time-consuming, requiring experts to carefully create versions of data to determine which one best achieves model performance. Engineering sufficient, relevant and unbiased data is crucial to achieving effective training of the model and is the foundation of successful production.

Lack of Scalability: Machine learning models that are designed for small-scale experimental setups may struggle to scale-up for high data volumes and user demand environments. A robust model design could be created using distributed algorithms and scalable deep learning frameworks, leading to lower computational complexity. Additionally, planning infrastructure with parallel processing capabilities and using containerization technology can efficiently distribute user workload, scaling resources up and down as required. 

Protip: A container isolates the model and its dependent software components into self-contained units that can be easily replicated or removed, facilitating any number of users.

Overfitting and Generalization Issues: Overfitting occurs when a machine learning model becomes overly specialized to the training data, resulting in poor model performance on new, unseen data. Though  an overfitted model will perform effectively on test data, when it comes to data on real-world scenarios, it fails to accurately predict, leading to failed production. 

An overfitted model captures noise and random fluctuations in data, leading to becoming too specific and sensitive to training data. Ensuring effective generalization of the model is crucial for successful deployment, and avoiding overfitting is a critical consideration.

Interpretability and explainability: One major challenge in deployment of machine learning algorithms is a lack of understandability and interpretability of the decision making process. Some machine learning models, especially deep neural networks are highly complex and black box in nature. This lack of explainability can pose a challenge in successful deployment of models in a production environment where insights into the decision making process are crucial.

Industries with ethical and regulatory considerations– such as finance and health care, demand models that have understandable explanations of how predictions are made. This is to ensure that patients get safe treatment and finance decisions remain unbiased and fair. Ensuring use of robust interpretability algorithms such as MIT and IBM’s recent open source code can effectively achieve transparency and trust in model decision making in the production environment. 

Addressing the above challenges requires a comprehensive approach that encompasses data quality improvements, scalability considerations, algorithmic robustness and interpretability techniques. By overcoming these hurdles, there’s a greater chance that machine learning algorithms will successfully reach production.

Ayesha
Ayesha
I engineer the content and acquaint the science of analytics to empower rookies and professionals.
RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

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