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A Beginner’s Guide to MLOps

  • September 30, 2021
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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So, what really is MLOps?

MLOps is a portmanteau of the words Machine learning (ML) and DevOps (Ops). It basically means DevOps for Machine Learning applications.

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Why MLOps?

There has been an incredible surge in the number of organisations attempting to use data science and machine learning to benefit their operations as a result of the explosion of data around us and the development of accessible, low-cost computing power in the form of cloud computing. The creation of new and better algorithms and their implementation as standalone libraries or as packages in the Python and R languages have accounted for the majority of advancements in data science and artificial intelligence.

Enterprises have, however, swiftly come to the realisation that although performing a POC on a laptop (or other on-premises device) is simple, deploying these POCs into production and at scale is really rather difficult. As businesses have grown and focused on enhancing their array of data science applications, it has become clear that they must find solutions to the following issues:

  • Production deployment of models
  • Infrastructure considerations - cloud, on-prem or hybrid?
  • Scaling the models up and down
  • Continuous (re)training
  • Continuous deployment
  • Model management
  • Metadata Management

and several other similar factors. Companies are now realising that data science is more complex than first appears. The field of data science is in dire need of innovation, particularly in terms of getting the models to market rapidly.

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Enter MLOps

Wikipedia defines “MLOps or ML Ops as a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.” According to this report, a staggering majority (88%) of companies are struggling to put ML/AI models into practice. It also suggests that the companies that actually put ML/AI models into production are expected to see a 3-15% increase in their profits.

Businesses are beginning to see the enormous benefit in spending money to create a scalable MLOps platform. This problem has also been encountered by Google and Facebook, two leaders in the AI and ML industries. Some of them created technologies for internal use that they subsequently made open source. Google came up with and made open-source software like Kubernetes, Kubeflow, and Tensorflow Extended. With others following suit, there has been a huge snowball effect, resulting in an explosion of MLOps-based frameworks and tools.

The main goal of MLOPs is to automate the related pipelines and data engineering activities while also significantly enhancing the quality of ML models currently being used in production. While MLOps and DevOps and DataOps share a balanced emphasis and certain commonalities, MLOps has undergone an unexpected and extremely evolutionary endeavour. This is largely because MLOps has evolved into a comprehensive framework that covers the entire machine learning model development lifecycle (MDLC), which combines model development with continuous deployment, integration with software apps, and continuous training, governance, and diagnostics.

This has resulted in a purposefully independent path forward for managing the lifecycle of ML models which has spawned an engineering culture that unifies ML model development with operations by applying best practice guidelines from DevOps culminating in what we now know as MLOps.

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But, isn’t MLOps the same as DevOps?

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Yes and No. MLOps shares a lot of similarities with DevOps but it has distinctive features that separate it from DevOps in very significant ways.

  • MLOps has an exploratory/experimental outlook : The core function of both DevOps and MLOps is to ensure that a software can be produced in a repeatable and fault tolerant workflow. But MLOps also has to contend with added uncertainty in terms of hyperparameters, managing data and code to ensure reproducible results etc.
  • Automated Deployments: Continuous retraining requirements exist for MLOps which are not present for DevOps.
  • Stateful applications: ML Models have the added complexity of ensuring that the data on which inferences are issued have comparable distributions relative to the training data. This is to avoid a phenomenon called Train/Serve skew.
  • Monitoring requirements: In DevOps you simply needed to monitor code and/or infrastructure, but in MLOps the models need to be continuously monitored for performance decay etc and retrained periodically.

The following illustrates how the typical CI/CD pipelines in DevOps have taken on new significance in MLOps:

Integrating continuously (CI): Testing and verifying data, schema, and models are included in continuous integration (CI), in addition to testing and validating code components.\

Continuous Deployment (CD): CD develops into a framework for rolling back model deployments as needed.

Continuous Training: MLOps deployments are the only situations in which this idea is used. Depending on how well a model performs in applications in the real world, it might need to be updated. Retraining it to respond to time-based or event-based triggers can be necessary.

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MLOps maturity index: Based on the degree of automation involved, the sophistication of the pipeline architecture, and the implementation of various features like model management, experiment tracking, etc., MLOps implementations may be widely thought of as having three stages.

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  • Manual Processes (Level Zero)
  • Pipeline Automation (Level One)
  • Pipeline Automation with CI/CD/CT (Level Two)

Let us take a look at each of these phases in detail.

Level Zero :

Usually prevalent in companies that are just testing their ML models and applications. It is usually characterized by the following features:

  • Manual processes : Highly dependent on Data Scientist involving use of multiple scripts etc, this level of maturity is notable for all of the steps being performed manually - data ingestion and preparation, model training, tuning and deployment etc. No concept of experiment tracking, model management etc
  • No CI/CD/CT
  • ML and Ops are highly disconnected resulting in non-smooth workflows during deployment and updates

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Level One:

At this stage, automation of the ML pipeline streamlines continual training. When a customer's behaviour is constantly changing and the model needs to adjust quickly, this kind is often good.

  • Continuous Training: The models could be refreshed and retrained continuously or as required
  • Lightning fast experimentation: All the steps of the machine learning model development life cycle (MLDC) are automated
  • Pipeline Automation: The entire ML training pipeline is deployed into production and serves the predictions automatically via pipelines

Level Two:

This is the most advanced MLOps framework, replete with an automated CI/CD system that aids in the quick and dependable deployment of ML pipelines. This is an end-to-end MLOps platform that includes a feature store, ML metadata store, source code version control, testing capabilities, deployment capability, option for maintaining model registries, and pipeline orchestrator tools like Kubeflow, Apache Airflow, etc.

  • Model development: Different models are developed via experiment management
  • Pipeline CI/CD : During the CI phase of the pipeline, the source code is built and tests are run. These artifacts are then deployed during the CD phase resulting in a new model deployment.
  • Triggering the pipelines: This is automated in this level and could be time based or event based triggers
  • Monitoring: Model is monitored for both data and concept drift
  • Continuous Training: Model is being continuously refreshed based on the monitoring results from previous phase

MLOps Frameworks:

  • TFX + Pipeline orchestrators (Kubeflow, Airflow etc): Tensorflow Extended is GOogle’s open source version to write production grade ML code and simplify the process of going to production. One drawback of this is that it needs a pipeline orchestrator such as Kubeflow or Airflow to fully automate and orchestrate its ML pipelines
  • Kubeflow: Kubeflow is a wonderful open source project whose main goal is to deploy ML workloads on Kubernetes based infrastructure with ease and scale. It provides a best of the breed open source systems for machine learning in highly diverse environments such as Cloud, On Prem and Hybrid landscapes
  • MLFlow: Another awesome open source framework which manages the end to end ML model lifecycle. It provides various steps crucial in ML lifecycle through its components namely, Tracking, Projects, Models and Model Registry
  • AWS Sagemaker Studio (Pipelines): The full service solution of AWS cloud, it is quickly becoming one of the best managed services options provided by a major public cloud provider
  • Google Vertex AI: The Google Cloud counterpart to Sagemaker, this has been recently released and trying to compete with Sagemaker
  • DataRobot: A UI based (no/low code) alternative to perform MLOps at scale
  • Cnvrg.io : A low code DAG based MLOps platform geared towards Data Scientists

You now have a comprehensive understanding of what MLOps is, why it is important, its advantages, platforms that offer MLOps capabilities, and their open-source equivalents. I hope you find this post beneficial.

FAQs

An MLOps engineer is a developer who mainly focuses on the operations and management of algorithms, processes, and machine learning models. They collaborate with a data scientist to make projects more effective and monitor the health of the models they create. They are in charge of everything that happens in a machine learning model and its working. An MLOPs engineer must possess the following skills:

  • Ability to design and implement cloud solutions
  • Experience with Kubernetes and Docker
  • Ability to build MLOps pipeline
  • Experience in software development
  • Knowledge of frameworks like Keras, PyTorch, TensorFlow, etc.

MLOps and DevOps are both software development strategies focusing on collaborating developers, data scientists, and operation teams. DevOps mainly focuses on application development, whereas MLOps focuses on Machine Learning. DevOps aims to shorten the System Development Cycle, whereas MLOps concentrates primarily on automation and production, analyzing ML applications and workflow.

The essential advantage of using MLOps is to remove silos within the collaborative spaces and add features like sharing scaling with continuous development and monitoring for drifts. MLOp platforms like Iguazio make them accessible. The only disadvantage or factor to reconsidering MLOps would be the initialization cost involved. The long-term benefits are nothing compared to the amounts invested but bearing the price might be expensive for some organizations.

MLOps is one of LinkedIn's top emerging careers of 2022. Companies worldwide are starting to understand that only a Data Scientist cannot bring sufficient value out of ML models, and thus the role of the MLOps engineer comes into play. An MLOps engineer ensures the application can manage a large amount of data entered. They are in charge of everything once the machine-learning model gets built.

Skills required to become an MLOps engineer are:

  • Ability to design and implement cloud solutions (AWS, Azure, or GCP)
  • Experience with Kubernetes and Docker
  • Ability to build MLOps pipeline
  • Understanding on LINUX
  • Experience in software development
  • Knowledge of frameworks like Keras, PyTorch, TensorFlow, etc.
  • Understand the tools used by Data Scientists

This is the list of existing resources that you need to learn to kick off your journey in MLOps:

  • Advanced Mathematics is vital to understand Machine Learning algorithms and equations
  • Programming languages like Python
  • Machine Learning and Deep Learning
  • DevOps
  • Big Data

Since it's a vast and dynamic field, sign up for MLOps courses where experienced professionals can help you learn it better. Knowing and understanding the prerequisites is also essential for getting real hands-on experience in MLOps.

A data engineer deals with data lifecycle management, while an MLOps is more concerned with deploying Machine Learning systems. MLOps is a set of practices that help data scientists and Machine Learning professionals and make working more efficient and reliable. Even though MLOps is a great career choice, data scientists and engineers fall on a higher pay grade and vertical hierarchy.

The average salary of MLOps engineers in India is around 11 lakh per annum. An entry-level MLOps engineer can make about 4 to 6 lakh per annum.

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