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MLOps Tools and its Processes | The Complete Guide

Navdeep Singh Gill | 22 December 2022


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Introduction to ML Project Life Cycle

The steps involved in the ML Life cycle, and MLOps is all about advocating for automation and monitoring at all the above steps. Machine learning project development is an iterative process that means we continue to iterate from each of the above processes (except scoping) during a life cycle of a model to improve the efficiency of the process.

  • For instance, We improve the data when new data comes in or feature engineering new features out of the existing data.
  • We iterate through the modeling process according to its performance in production.
  • Accordingly, the deployed model gets replaced with the best model developed while iteration.
  • This process goes on with the iteration, but one should follow some best practices while iterating through the process. We will talk about these here.
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What are the MLOps best practices?

Developing a machine learning mode and deploying it fast and cheaply but maintaining it over time becomes difficult. Any team developing ML solutions must follow best practices to get the best out of the machine learning models. It helps to avoid “machine learning technical debt.”

The best practice that needs to follow while developing ml solutions:

Data Validation

In the Ml system, data is the most crucial part. It does not validate correctly and may cause various issues in the model. It is necessary to validate input data that is fed to the pipeline. Otherwise, as data science, one concept says, garbage in, garbage out. As a result, data must be considered a top priority in the ML system. It should be continuously monitored and validated at every execution in the ml pipeline.

Experiment and track experiments

To get the best accuracy, one needs to do experiments. Machine learning is all about experimentation. It may involve trying out different combinations of code, preprocessing, training, evaluation methods, data, and hyperparameters tuning. Each unique combination produces different metrics you need to compare with other experiments and keep track of them. So, later you can compare which combination is performing better.

Model validation across segments

The performance of machine learning models can degrade over time. And they need to be retrained to maintain good performance. Before deploying a model into production, the model needs to be validated. Model validation includes producing metrics (e.g., accuracy, precision, rmse, etc.) on the test datasets to check the model performance so it can fit business objectives.

The model should also be validated on various data segments to ensure they meet requirements. Otherwise, the model can be biased in the data, and several incidents have happened where the model was biased and performed inadequately for some users.


Reproducibility means in machine learning that every phase should produce the same results given the same input. It can be data preprocessing, model training, and model deployment. It’s challenging and requires tracking model artifacts such as code, data, algorithms, package, and environment configuration.

Monitoring predictive service performance

The practice mentioned above can help you deliver a robust ml model. In operations, different metrics need to be measured to evaluate the performance of the deployed model. These metrics evaluate model performance regarding business objectives. Users might need a good performance and better accuracy of the model, but they also need as fast as possible and availability all the time. To monitor operational metrics such as:

  • Latency: measured in milliseconds,
  • Scalability: how much traffic can the service handle at the expected latency
  • Service update: how much downtime is introduced during the update of service.

For instance, delaying any service can impact the user, and it can cause loss to the business.

Automate the process

Managing machine learning tasks manually becomes difficult and time-consuming when the models get into production. Data preprocessing, model training and retraining, hyperparameter tuning, and model deployment can be automated. If data drift, model drift, or the performance of the model degrade. So it can be retrained automatically. It just needs to be triggered. After automating the process, the error margin becomes less, and more models can be deployed. ML pipeline can be used to automate the process. So, the model can follow continuous training and continuous delivery. 

Every process of MLOps is defined below with their best practices

Best Practices for Scope Management

Scoping is defining the project goals in terms of Machine learning development goals. For instance, the business team might ask us to develop a conversational AI or agent for our website that will answer the FAQs of the user. Now the development of a FAQ answering agent is a business goal. Once this is clear, we need to define our goal: developing a question answering algorithm based on the FAQs present.

Best Practices to follow while scoping

  • Understanding the Business Problem

This is a crucial step though it seems like a simple step, due to the lack of understanding of the business problem, all development processes may go in vain. So the development team needs to be on the same page with the business team (or the team handing out the problem). Understand the problem properly clearly and get it verified with the stakeholders. Note: Do not proceed with the development plan until the problem is clear.

  • Brainstorming within the team

Once the problem is defined, one should brainstorm and accumulate all the solutions' ideas. The goal here is to think outside the box and explore all the ideas suggested by the team members.

  • Research About the problem

At this stage, we have clarity of the problem and ideas from the team, now do thorough research at your end about the problem, the research should be solution-oriented, keeping in mind that we need to come up with a road map and approach doc for the solution (elaboration to these are given in next sections).

  • Define the Development plan concretely, aka “Roadmap.”

Once the problem is defined, one needs to come up with a Roadmap, i.e., visual representation for flow for the development of the solution to the problem. The roadmap should contain the following things:

  1. Proposed processes and steps to deliver the solution.
  2. Estimated time for each process, i.e., Timeline.
  3. Special remarks that you think should be given with each process. For example, some dependencies need to be fulfilled, such as data dependency from the data engineering team before the EDA process in data preparation steps.
  4. Once the roadmap is developed, get it verified with the concerned person. In your case, it might be Subcoach, Coach, etc., and get the inputs.
  5. The template can be found here.
  • Prepare Approach Doc
  1. Once the Roadmap is clear, one needs to prepare an Approach doc. This document contains information about the approach you will use to solve the business problem you are given. For example, suppose you are given a business problem that involves classification, then in the approach doc. In that case, you need to tell the initial algorithm(s) you are going to select for the implementation with the implementation flow.
  2. The purpose of Approach Doc is to give visibility of our approach to the stakeholders so that we can take them in our confidence for the development process we are going to follow.
  3. An example template of the Approach Doc can be found here. Once Approach Doc is prepared, get it verified and get the inputs from the stakeholders.
The market for Machine Learning Oerations solutions is expected to reach $4 billion by 2025. Click to explore about, MLOps: What You Need To Know

Best Practices for successful Data Processing

Here, we will discuss the best practices while processing the data before the modeling stage.

Types of Data problem

The data types for any machine learning problem can be divided into the below categories.

The above figure shows the datasets we can see while developing the ML solution for a business problem. Let’s see the best practices while handling both types.

Best Practices for Defining the dataset for Structured data

Here we will see the best practices for defining the dataset.

  • Information of each column: Maximum efforts should be put in getting the information on each column of the dataset if it’s not present to remove the ambiguity from the dataset if the dataset is present in the tabular format. If data is Unstructured, metadata(information on each field of the dataset) should be fetched and asked from the team providing the dataset to you. It’s solely the responsibility of the team to get the info on the dataset if it’s not present.
  • A clear distinction between features and labels: The first important step in data processing should be defining the dataset, i.e., for the ML problems, we should know what the features(X) need to be considered and what should be a label(Y) for the problem if this is not clear don’t proceed for the other steps this is a prerequisite. For unstructured data, the labels must also be defined. For example, if it’s an image classification problem, the images become features, and the labels should be given.
  • Consistency in Labelling format for Unstructured data: Sometimes, what happens with Unstructured data (text, image, audio) is that we need to label it manually or give the task of labeling to the labelers (these can be anyone who is assigned with the task of labeling the dataset). If more than one labeler is involved in the dataset, we must ensure a consistent labeling strategy. For instance, consider labeling the image of Smartphones with defects or not. In case 1, the labeler has been labeled as given in figure 1, and for the similar case, the other labeler has labeled it as it is given in figure 2. So there is inconsistency in labeling, which must be avoided by providing clear instructions to the labelers.

Best Practices while preprocessing the dataset

Remember This “Always Keep track of the dataset aka Data Versioning,” let’s dive into the best practices of it.

  1. Use Data versioning tools: For data versioning with each experiment done with any dataset version, we should use the data versioning tools like DVC.
  2. Text files for data versioning: If due to some reasoning the data versioning tools can’t be used, use text files or google sheets to maintain the records of the dataset used in the experiments but maintaining versioning records is the responsibility of the developer and he/she needs to reproduce it when asked.
  3. Tracking and Reproducible Experiments: The main purpose of data versioning is that when required, one can easily reproduce the experiments conducted with any version of the dataset, this is not possible if one never does the versioning of the dataset.

Consistency in Data pipelines

  1. Make data pipelines consistent both for development, testing, and production: It is tempting for ML developers to kick start the development process without giving focus to data pipelines, for instance, the data preprocessing script used for the training model in the development stage can’t be used in production or even during scoring Always keep in mind to make consistency data pipelines which means you can one pipeline everywhere for data processing.
  2. Fault tolerance capacity of production pipeline: Give these pipelines the ability to handle any exceptions that may occur while the model is deployed in production. For instance, one needs to handle the scenario if one or more values go missing from the inference data( data in production).
The debate about Continuous Integration vs Continuous Deployment has recently been the town's talk, and there are quite mixed thoughts on which one is better. Click to explore about, Continuous Integration vs Continuous Deployment

Other Miscellaneous points to keep in mind for the data processing stage

  • Balanced Train/Val/test: The train/dev/test should represent the dataset. Let us understand it with an example, consider a dataset with 100 examples of smartphones, and out of 100, 30 are positive(defective) other negatives:

Row 2 shows how split can be non-representative of the actual dataset as every.
Set must contain the 30% samples from the positive class. But Row 3 shows the correct way in the table.

  • Prevent Data leakage: When your training data contains information about the target, but similar data is not available when the model is used for prediction, data leakage (or leaking) occurs. This results in an excellent performance on the training set (and potentially even the validation data), but poor performance in production. To put it another way, leakage makes a model appear correct until you start making decisions. See more here.

What are the best practices for Data Modelling?

The best practices of data modelling are described below:

Define Baseline and Benchmark the model

Once you reach the Modelling part, we need to set up a baseline to compare the performance of our model in different experimentations.

  1. Human-Level Performance (HLP) as a baseline: For unstructured data like images, humans can be used to set the baseline accuracy of the model (if data is small enough and you have labelers). For example, For the computer vision problem of detecting defects in smartphone images, the human can detect the defect in smartphones screen then be tested with a model.
  2. Quick implementation: The other most-followed option is a quick implementation with a basic algorithm and considers it as a baseline. But the baseline is necessary.

Model Versioning and Tracking

  1. Use Model versioning tools: For Model versioning, with each experiment done with any model version, we should use the model versioning tools like mlflow.
  2. Text files for Model versioning: If the model versioning tools can’t be used due to some reasoning, use text files or google sheets to maintain the records of the models used in the experiments.
The system needs continuous learning and training from the real world. Click to explore about, DevOps for Machine Learning , Tensor Flow and PyTorch.

Error Analysis once Model is trained

Once the model is trained, Error analysis is the process of getting visibility about where the model did not perform well. For example, a classification problem model might not be performing in the class. This allows us to improve the model performance and to audit its performance at every iteration. The process can be understood with the below diagram.

Let’s see Best practices for the error analysis process.

  • Accuracy is not always the best checkout confusion matrix: Always consider various evaluation metrics while evaluating the model's performance. Confusion matrix and classification reports give these metrics like precision, recall and f1 score consider these also.
  • Brainstorm how things can go wrong with the model and test it:

- Performance on different subsets of dataset known as cross-validation.

- Performance on rare class.

-Fairness and bias of model (checkout fairness section).

Use Data-centric Approach not Model-centric Approach

It becomes tempting for ML solution developers to use cutting-edge algorithms for solving the problem given on hand. Still, it is always better to have a simple model with better explainability than a complex model on bad data.

Best practices for improving the dataset, i.e., following data-centric approach:

  • Data Augmentation for Unstructured data: For unstructured data like images data and audio data, augmentation is an excellent approach to have more datasets but keep in mind these things while performing augmentation:
  1. Create more examples on which algorithms show poor performance in error analysis.
  2. If possible, see if the baseline model is performing well on this dataset.
  • Feature Engineering for structured data: It might not be possible to create new samples for structured data such as online user data as it is impossible to add new users. For structured datasets creating new features can be a great option to explore.
Java vs Kotlin
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Developing Fair and Unbiased ML algorithms

It focuses on building Fair and unbiased ML algorithms so that every end-user using the served by us in production should have equal opportunities. This means they are not discriminated against based on race, sex, religion, socioeconomic status, and other categories. For example, a credit card approval application using the ML model at the backend may reject a person based on his race if Bais was not eliminated from the data. To avoid such unfair events, follow the best practices regarding Bias and Fairness given below:

Analyze the data for biases: One should properly analyze the data, so there is no representational bias in the dataset. This means one group of people is left intentionally for some reason, such as if the dataset used to train the models excludes darker skin tones. We have mentioned bias only. Other biases can be present in ML workflow. We need to reduce all of them. See the figure below and follow this link for more information. Following the above procedure, the model is ready to go for production. For deployment Best practices, see ModelOps best practices section.

Which tools are used in MLOps?

Its tools enable organizations to implement DevOps processes in the process of creating and implementing AI and machine learning (ML) models. These tools are commonly used by machine learning engineers, data scientists, and DevOps engineers. As machine learning is widely used for various needs, its tools are not limited to specific industries.

Its technologies are frequently used to manage and integrate machine learning pipelines with data and software deployment pipelines. Various tools have been developed and are now available to help and manage the process. Many MLOps solutions include a limited free version, which may include partial feature access or a limited number of compute hours. Here is a list of popular and community-approved MLOps tools for various stages of model development are

  • Kubeflow
  • MLFlow
  • Metaflow
  • Kedron
  • Melun
  • Neptune
  • Autokeras

Further, more tools exist that one can use depending on the problem, requirements, and needs.

tinyML is a branch of ML that focuses on creating and implementing machine learning models on low-power, small-footprint microcontrollers such as the Arduino. Click to explore about, MLOps for Scaling Tiny ML and its Applications

Which MLOps Tools one should choose?

There are tools available based on the purpose for which one wishes to use them. So to decide which tools to use, firstly, one must have a clear and concrete understanding of the task for which they will use that tool. Before choosing any tool, one should carefully consider the benefits and drawbacks of each tool before deciding on one for the project. Furthermore, this must ensure that the tools are compatible with the rest of the stack in use. There are tools available for performing the tasks, such as

Model Metadata Storage and Management

It provides a central place to display, compare, search, store, organize, review, and access all models and model-related metadata. The tools in this category as an experiment tracking tools, model registry, or both. The various tools that one can use for metadata management and storage are-

  • Comet
  • Neptune AI
  • ML flow



Neptune AI

ML flow

Launched in




24×7 vendor support

Only for enterprise customers

Only for enterprise customers

Serverless UI


Video metadata

Audio metadata

Data and Pipeline Versioning

Every team needs the necessary tools to stay updated and aligned with all version updates. Data versioning technologies can aid in creating a data repository, track experiments and model lineage, reduce errors, and improve workflows and team cooperation. One can use the various tools for this such as;

  • DagsHub
  • Pachyderm
  • LakeFS
  • DVC


Akira AI





Launched in






Data format-agnostic

Cloud agnostic

Simple to use

Easy support for big data

Hyperparameter Tuning

Finding a set of hyperparameters that produces the best model results on a given dataset is known as hyperparameter optimization or hyperparameter tuning. Hyperparameter optimization tools are included in MLOps platforms that provide end-to-end machine learning lifecycle management. One can use various tools for hyperparameter tuning such as:

  • Ray tune
  • Optuna
  • HyperOpt
  • Scikit-Optimize



Ray Tune



Algorithms used

Random Search, Tree of Parzen Estimators, Adaptive TPE

Ax/Botorch, HyperOpt, and Bayesian Optimization

AxSearch, DragonflySearch, HyperOptSearch, OptunaSearch, BayesOptSearch

Bayesian Hyperparameter Optimization

Distributed optimization

Handling large datasets

Uses GPU 

Framework support

Pytorch, Tensorflow

Pytorch, Tensorflow, XGBoost, LIghtGBM, Scikit-Learn, and Keras

Tf, Keras, PyTorch

Built on NumPy, SciPy, and Scikit-Learn

The primary role of DevOps is to take continuous feedback of the process at every step. Click to explore about, Role of ML and AI in DevOps Transformation

Run Orchestration and Workflow Pipelines

A workflow pipeline and orchestration tool will help when the workflow contains many parts (preprocessing, training, and evaluation) that can be done separately. Production machine learning (ML) pipelines are designed to serve ML models to a company's end customers that augment the product and/or user journey. Machine learning orchestration (MLO) aids in the implementation and management of process pipelines from start to finish, influencing not just real users but also the bottom line. The various tools that one can use for running orchestration and workflow pipelines are:

  • Kedro
  • Apache Airflow
  • Polyaxon
  • Kubeflow








Reproducible, maintainable

Kubeflow pipeline & workflow

Create concurrent, scalable, and maintainable workflows

End-to-end ML pipelines

UI to visualize and manage workflow

Server interface with REST API

Scheduled workflows

Model deployment and Serving

The technical task of exposing an ML model to real-world use is known as model deployment. Deployment is the process of integrating a machine learning model into an existing production environment in order to make data-driven business decisions. It's one of the last steps in the machine learning process, and it's also one of the most time-consuming. The various tools that one can use for model deployment and serving are:

  • Seldon
  • Cortex
  • BentoML





User interface





Prometheus metrics

Prometheus metrics

Prometheus metrics

API Auto-Docs

Swagger/Open API


Open API



Python and go wrapper


Businesses of all sizes are increasingly requesting real-time information, warnings, and predictions. Click to explore about, Streamlining ML Projects with MLOPs and Azure ML

Production Model Monitoring

The most crucial part after deploying any model to production is its monitoring and if done in a proper way can save a lot of time and hassle (and money). Model monitoring includes monitoring input data drift, monitoring concept drift, and monitoring hardware metrics. The various tools that one can use for model monitoring after production are:

  • Akira AI
  • AWS SageMaker model monitor


Akira AI

AWS Sagemaker MM


Detect data drift

Data integrity

Performance monitoring



Now that the list of the most excellent MLOps tools is compiled, all you have to do is figure out how to put them to use in the setup. These tools make it easier to keep track of modifications and model performance, allowing us to focus on domain-specific tuning and model performance. It will continue to improve in the future, with new functionality added to the tools to make the life of data science teams handling the operational side of machine learning projects that much easier.