Overview of MLOps Challenges and Solutions

Q: What are the Best Practices for MLOps?

Shift to Customer-Centricity, Automation, Manage Infrastructure Resources, and scalability, Monitoring.

What is MLOps?

Artificial Intelligence and ML applications are no longer the buzzwords of research institutes; they are becoming an essential part of any new business growth. According to business analysts, most organizations are still unable to deliver AI-based applications successfully. They are stuck in applying data-science models (which were trained and tested on a sample of historical data) into applications that work with the real-world and massive data.

An emerging engineering practice called MLOps can address such challenges, as the name indicates that it aims to unify ML system development (Dev) and ML system operation (Ops). Automating MLOps means automation and monitoring at all ML system construction steps, including integration, testing, releasing, deployment, and infrastructure management.

According to the survey, it is observed that data science is not focussed on data science tasks. They spend most of the time on other relevant tasks such as data preparation, data wrangling, management of software packages and frameworks, infrastructure configurations, and integration of various other components.

Data scientists can quickly implement and train a Machine Learning model with an excellent performance on an offline dataset by giving relevant training data for particular use cases. However, the real challenge is not to build an ML model. But the problem lies in creating an integrated ML system and continue operating it in production.

Explore The Emergence Of MLOps - Forbes

Challenges in MLOps

The machine learning Life Cycle starts with the business problem. After understanding the business problem and establishing the success criteria, delivering an ML model to production involves the subsequent steps. These steps can be performed manually or can be accomplished by an automatic pipeline.

Data extraction - Data scientists collect the relevant data from various data sources for the ML task.
Data analysis - EDA (Exploratory data analysis) is performed to understand the available data for building the ML model.

This process leads to the following -

Understanding the data schema and characteristics that are expected by the model. Identifying the data preparation and feature engineering that is needed for the model.

Data preparation - The data is prepared to perform ML tasks. This preparation involves data cleaning in which Data scientists split the data into train/test/validation sets. There is a need to make missing values imputable, multiple encodings, many transformations, feature engineering, feature interaction, feature selection, and zillion other things to solve the particular task. The output of this step is the data split in the prepared format.
Model training - Once preprocessed data is ready, data scientists implement different algorithms with the prepared data to train various ML models. They don't know which model will perform best on the dataset on which they are working. So they started applying some hypotheses based on their understanding of problems and mathematical knowledge of algorithms. The output of this action is a trained model.
Model evaluation - The model is evaluated to check the performance of model.
Model validation - The model is verified to be fit for deployment. Its predictive performance will be tested against a specific baseline model.
Model serving - The validated model is then deployed and productionize to an environment to serve predictions.
Model monitoring - The predictive model performance is monitored to invoke a new iteration in the ML process potentially.

Here the challenge is when Data scientists deploy the model from a business problem statement; Data scientist loses focus on how managing is more difficult than building and deploying.

In real life, business applications need to handle constantly changing an enormous amount of real-time data. ML is an iterative process. It takes a lot of time as Data scientists have to repeat it again and again. They must meet adequate response times, along with supporting a large number of users as well. Here, the challenge is that the team must focus on the process only. But when dealing with hundreds or thousands of code lines, they have their own set of difficulties to manage.

Earlier, the Data Science team's goal was to produce an ML model. But today, by seeing the productionize challenges, it seems like the first step to bringing data science models to production.

Explore more about Data Intelligence vs Data Analytics

Emerging Challenges of Big Data

Data scientists begin with sample data followed by various ML pipeline steps such as data analysis, data preparation, feature engineering. Usually, they work on Jupyter notebooks or use AutoML to train/test/validate models and identify hidden patterns. At a particular point, they need to prepare the models on large data sets. This is where situations start to become complicated. They came to know that most of the tools that give excellent performance while working on CSV files or small data and can load data into memory can't work at scale, and they need to re-built everything to fit models in distributed platforms.

The other challenge team is facing that they are spending most of the time creating features from raw data, and in several cases, the same feature extraction task is repeated for multiple projects or by diverse teams. The expenses are further increased if there is any change in datasets, the derived data, and models' changes. The experiments need to repeat every time to get the required accuracy.

Further, new challenges arise when the data science team tries to deploy models into production. They find that data exist differently and can't use the same Machine learning methodologies on dynamic data.

Use 3 MLOps Organizational Practices to Successfully Deliver Machine Learning Results - Gartner

What are the best practices for MLOps?

Shift to Customer-Centricity – Today's end customer does not want to know about the brand, product, selection, or model. Still, their target is how they can achieve their goals by working on real data business challenges.
Automation – Automates data pipelines to ensure continuous, consistent, and efficient business value delivery to avoid rewriting custom prediction code.
Manage Infrastructure Resources and scalability – Applications should be deployed so that all resources, infrastructure, and platform-level services should be appropriately utilized.
Monitoring - Track and visualize all models' progress across the organization in one central location and implement automatic data validation policies.

What are the best MlOps Tools?

The listed below are the best MlOps Tools:

Neptune.ai
Amazon SageMaker
Valohai
Iguazio
MLflow
Domino Data Lab
H2O MLOps
Cloudera Data Platform

Read more about MLOps Roadmap for Interpretability

Akira AI for MLOps

For the ideal adoption of ML across organizations, there requires a standardization of the machine learning workflows, so there is no difficulty in implementation.

ML Model Lifecycle Management - Akira AI provides MLOps capabilities that help build, deploy, and manage machine learning models to ensure business processes' integrity. It also provides consistent and reliable means to move models from development to production environment.
Model Versioning & Iteration - As models are utilized in a particular industry, they need to be iterated and versioned. To deal with new and emerging requirements, the models change based on further training or real-world data. MLOps solutions provide capabilities that can create a version of the model as needed, provide notification to users of the model about changes in version, and maintain model version history.
Model Monitoring and Management - As the real world and its problems continuously change, it is challenging to match up to the world where Data scientists still struggle with small data. MLOps solutions help monitor and manage the model's usage continuously, its consumption, and results to ensure that accuracy, performance, and other results generated by that model are acceptable.
Model Governance - Models that are used in the real-world need to be trustworthy. MLOps platforms provide capabilities to audit, compliance, access control, governance, testing and validation, change, and access logs. The logged information can include details related to access control such as publishing models, why modifications are done, and when models were deployed or used in production.
Model Security - Models need to be protected from unauthorized access and usage. MLOps solutions can provide the functionality to protect models from being corrupted by infected data, being destroyed by denial of service attacks, or being inappropriately accessed by unauthorized users.
Model Discovery - MLOps platform provides model catalogs for models produced as well as a searchable model marketplace. These model discovery solutions will provide sufficient information to track the data origination, significance, quality transparency of model generation, and other particular model circumstances.