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Top End to End MLOPs Platforms - Intro

Top End to End MLOPs Platforms - Intro

Pavel Klushin
November 18, 2022

Each machine learning model passes through different phases throughout its development. Every Data scientist, ML engineer, or AI enthusiast knows how much work it takes to develop a successful machine learning system. From gaining raw data, processing that data, validation, analysis, training, tuning, architecture, and the final phase, deployment.

As machine learning systems are taking over the world, most models do not pass through the testing stage. Being a data scientist, you would know how hard it is to loop over a cycle repeatedly to get the whole experience for a machine to be fully automated. The pace of developing successful machine learning models is increasing, causing a new trend in the market known as MLOps.

In this article, we will analyze MLOps and the different MLOps platforms in demand as more ML models are developed.

Introduction to MLOps

As the word suggests, MLOps is a combination of machine learning and operations (DevOps) in the software field. It is a collection of techniques for developing Machine learning algorithms and automating their lifecycle. In this manner, all phases of the development of the ML system, from the initial training phase to the final deployment, are automated and monitored.

MLOps is applied and used when a new ML model is in process. Data scientists, ML engineers, and DevOps fuse their capabilities in changing the algorithm to construct the most competitive ML system.

Need for MLOps

MLOps incorporates business and regulatory requirements while improving the quality of production models like DevOps. Moreover, with the fusion of machine learning and operations, it is easy for developers to create models that can learn from data over time. An MLOps strategy enables a quicker time to market with better accuracy and has significant implications for forecasting, anomaly detection, predictive maintenance, and other areas. There are some reasons why MLOps is needed and how it is beneficial.

Deployment issues

Due to the lack of deployed models, businesses do not fully profit from AI. Also, they are not getting deployed at a pace that can benefit businesses. MLOps deployment can help:

  • There are many models in the backlog/queue that need to be deployed.
  • During the deployment phase, data scientists invest a lot of time in troubleshooting models.
  • There is a problem with a defined method for moving models from development to production.
  • The process of putting models into production is complicated and involves modifying many different systems.

Monitoring issues

Manually assessing the health of a machine learning model takes a lot of effort and is very time-consuming. MLOps monitoring can help:

  • Models were constantly in use, but their monitoring has never been done.
  • There is no unified method for tracking model performance throughout the enterprise.
  • A data scientist must manually analyze the model's performance to determine its performance.

Lifecycle management issues

Organizations and businesses cannot change models regularly as the procedure requires a lot of resources. MLOps management can help with the following:

  • Once the initial stage of model deployment passes, data scientists are not reported about the model decay.
  • The updates are done by data scientists actively involved in the process.

Moreover, MLOps model governance can help control production access, traceable results, and audit trails.

MLOps vs DevOps

DevOps is the fusion of (software) development and IT operations, providing continuous delivery with the best quality. It aims to speed up the system development lifecycle process. Meanwhile, MLOps seeks to automate the machine-learning process. MLOps can be seen as a subset of DevOps for ML models. Despite being the subset of DevOps, MLOps still vary in some features. The key differences are listed below.

CodeA more comprehensive range of libraries is used
as the model built to feed inferences.
Standard libraries are used as a generic application
is being made.
ValidationModel performance helps in the validation of the model.Unit, and integration testing is done.
1. Data scientists;
2. Machine learning engineers;
1. Software engineers;
2. DevOps engineers;
Tracking/controlTracks hyperparameters and model performance.Tracks software artifacts.
Delivers a machine learning model.A new version of the software product.

Benefits of MLOps

Some of the critical benefits of MLOps are:

  • It speeds up the data collection and preparation process.
  • It automates the training and deployment pipelines.
  • It speeds up the validation process.
  • It monitors the health of a production model and continues to retrain it.
  • It tests the model with real-life situations and accurate data.
  • It provides effective lifecycle management, which results in rapid innovation.
  • It creates replicable models and processes. These models can learn with time under real-life experiments.
  • It manages the ML lifecycle effectively.
  • It provides a faster response time when experimental conditions are changed.
  • It provides enhanced confidence to organizations using MLOps in their applications or models.

Goals of MLOps

The main objective of MLOps is to develop a self-automated ML model which can work without human intervention. Moreover, automating the development and deployment of an ML system as a core service. Some of the objectives are:

  • Faster model development and experimentation;
  • Updated models are deployed faster into production;
  • Quality assurance;

End-to-End MLOps

An end-to-end learning is the process of training a potentially complicated learning system that a single model represents, more precisely, a Deep Neural Network, which represents the entire target system, omitting the intermediate layers that are typically present in conventional pipeline designs.

The entire end-to-end MLOps process is extensive and takes a long time. It includes all the steps from development to monitoring. The steps are:

  • ML development
  • Training optimization
  • Continuous training
  • Model deployment
  • Prediction serving
  • Continuous monitoring
  • Data and model management

These are some crucial steps in end-to-end MLOps. Data and model management ensures reusability and replicability. Depending on your requirements, these can be added or excluded. These all combined make a potent ML model.

Machine Learning Lifecycle

Software developers put their heart and soul into building, testing, and debugging a feature. It takes time for a feature to behave as fully functional. Similarly, data scientists work to develop models through experimentation in which an optimization algorithm explains the dataset based on the optimal collection of weights. Some Machine learning models are hard to train, and it may take a long time before they can behave fully functional.

Working on data and keeping a check on different features is more complex, literally and figuratively. So, the model used in the process should be ideal and tuned perfectly over time. There are various MLOps platforms available for managing the machine learning life cycle.

Top End-to-End MLOps Platforms

An ML team may use MLOps technologies to accomplish various tasks. Some MLOps platforms simply concentrate on a single task like metadata, and some useful strategies allow fully functional regulation over various areas of the ML lifecycle.

Many emerging platforms like Qwak are best known for unifying ML engineering and data operations, providing control over all aspects of a machine learning model. Algorithmia, Amazon Sagemaker, Azure Machine Learning, Domino Data Lab, the Google Cloud AI Platform, Databricks, and vertex are some of the best choices. We will head over to little details about their functionalities and working.

Amazon Sagemaker

Amazon Sagemaker is one of the earliest MLOps platforms, which helps you automate and standardize procedures throughout the Machine learning lifecycle by using machine learning operations (MLOps) tools. While maintaining the model's performance in production, using MLOps platform tools, you can test, train, deploy, and analyze the model. Sagemaker also provides notifications when anything, for example, a dataset, needs changing over time.

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Automate the learning process

You may arrange model production phases for experimentation and model re-training by automating training workflows. Using Amazon Sagemaker Pipelines, the entire model development procedure can be automated, including the data composition, training and tuning of the model, and validation. Sagemaker Pipelines can be set up to run automatically at predetermined periods or in response to specific events, or you can manually operate them as required.

Sagemaker MLOps provide standardized data science settings/ Data wrangler Standardized ML Environments. Sagemaker helps make new projects simpler and apply the best ML approach; standardizing ML development environments accelerates the speed of innovation and boosts data scientist productivity. With the help of templates from Amazon Sagemaker Projects, you can quickly set up standardized environments for data scientists with CI/CD pipelines, source control repositories, boilerplate code, and current tools and libraries.

  • Collaboration on experiments

Building a fully automated machine-learning model is a repetitive task. Using the Amazon Sagemaker MLOps platform, you can monitor the inputs and outputs during the repetitive training cycles to enhance the collaboration of different data scientists. Sagemaker experiments track your training model's variables, parameters, and datasets. It also provides a unified interface where you can view your ongoing training jobs, collaborate on experiments, and deploy models straight out of an experiment.

  • Replicate the model for troubleshooting

You frequently need to replicate models in real-life situations to troubleshoot model behaviour and identify the underlying problem. Amazon Sagemaker can help with this by logging each stage of your process and producing outcomes of models, including training of data, configuring settings, model variables, and learning gradients. You can replicate models using lineage tracking to troubleshoot any problems.

  • Notebook

A notebook contains the runnable code, which has visualizations too. Sagemaker’s notebook provides the best environment for ML model production. It helps in the deployment and training of machine learning models.

  • Deployment and management

An ML application creation includes models, data pipelines, analysis, and validation. You may keep track of model versions and their information using the Amazon Sagemaker Model Registry. You can use choose the best ML model which suits your needs. For audit and compliance purposes, Sagemaker Model Registry also automatically tracks approval workflows.


On Sagemaker, you have two options for payment:

  1. On-Demand pricing without minimum charges or up-front constraints.
  2. For a specific constraint about a certain usage level, sagemaker offers saving plans.

It can deploy huge training data.System becomes slow if a large amount of data is pulled from solutions.
Data processing is fast, due to which we get results faster.Bit complex for the data engineers to understand for the first time.
Endpoints for API can be made to help technical users use it.Takes much time on large data sets.
Multiple servers for training are also available.It is not cheap; anyone cannot buy it easily.
All models, training, and testing can be accessed from S3 easily.It is not fully custom, so it runs some dataflows on its own.
Good for large models having large processing time.It is expensive and has no free version.
Notebooks can be easily and effectively run on this.


In the current market, Qwak is one of the most effective and efficient options for the production MLOps platform. It was explicitly created to shorten the ML research process, and thanks to its intelligence, it has also sped up manufacturing. The data scientists can monitor their production and deploy the ML model more effectively, which has also decreased risk in this industry.

These days, ML engineers and data scientists use this best emerging MLOps platform because it provides efficiency and an autonomous environment, which can speed up the machine learning production model process. Quack is a valuable platform for data scientists, and they can use it for their work purposes. It helps them build, automate, deploy, and monitor the production of machine learning models.

Data Scientists and Engineers may work peacefully together on this amazing platform, which also helps them concentrate better on their objectives. Today, Qwak is utilized globally as a single platform for creating, deploying, maintaining, and monitoring ML models and features.

Qwak is an all-in-one platform that provides you with the optimal and best solution. It speeds up implementation and reduces the amount of time required to finish the production process.  Additionally, it will offer a secure environment for cooperation on the other side, and the user will be able to fully concentrate on the issues that are crucial to the manufacturing line in question.


  • Customizable infrastructure

Qwak provides a customizable MLOps platform where you can build and train your model. You can deploy your model afterwards on this platform. Qwak has a feature store which allows users to explore different data types and work on numerous data sources. Qwak ensures faster iterations, scalability, and customizable infrastructure, which helps reduce the friction between data scientists and machine learning.

  • Reduce dependency

Qwak is an efficient platform which allows reusability and replicability. It uses the same definition when training and serving the features that only need to be created once. You do not have to recreate the features, Qwak does that for you. It learns from the dataset over time and gets trained. You should not be concerned with how features are sent to your model during inference.

  • Productize features

Qwak produces scalable and high-performing feature pipelines. These pipelines train the model with real-life scenarios and datasets, making it a highly reliable and effective MLOPs platform.

  • Features observability

The capabilities of Qwak Analytics are immediately made available to every feature controlled through the Qwak Feature Store.

Qwak unifies ML and data scientists in a way that helps in better training and deployment of ML models.

Some other notable features of Qwak are:

  • Data Connectors
  • Data Import/Export
  • Data Storage Management
  • Deep Learning
  • Forecasting
  • ML Algorithm Library
  • Model Training
  • Monitoring
  • Multiple Data Sources
  • Natural Language Processing
  • Predictive Analytics
  • Predictive Modeling

It can train and deploy data faster than other platforms.Takes much time for a large number of audiences.
Data can be reused and trained again and again.It is costly.
Qwak works for you as an out-of-the-box solution.It does not have a free version yet.
It builds high-performing data pipelines.It can not be deployed on desktop or mobile phones.
It shortens the processing time compared to other MLOps platforms.It only supports web-based, Saas, and cloud-based systems.


Qwak unifies ML and data engineering, thus saving more time and spending less time on the production of features and tools. It builds secure and scalable ML models that prove efficient to people working in this domain.

Qwak charges you only for the data and what you use! Nothing else. It is cost-efficient for long-term projects and offers multiple editions of ML engineering services.  QPU (Qwak Processing Units) based pricing per minute with no mandatory long-term commitment.


Databricks is a tool based on cloud data engineering. This MLOps application is used to process and transform a large amount of data and analyze it through machine learning models. Large flows of data processing and transforming were a huge task for the data engineers and data scientists so, Databricks an MLOps-based application presented by the creators of Apache Spark, which is an open-source unified analytics engine for a huge amount of data processing. 

Databricks collaborates with Microsoft Azure, Amazon Web Services, and Google Cloud Platform also because it makes it convenient for businesses to manage this huge amount of data and to perform machine learning tasks on it. It uses Lake House architecture which helps in a way that it provides Data warehousing capabilities to a Data Lake. It prevents multiple data pushing, which as a result allows us to develop ML applications using languages i.e., R, Python, SQL, Scala, etc.

Databricks uses multiple developer tools, data sources, and partner solutions. 

  • Data Sources: This MLOps-based application can read and write in different formats like JSON, Delta Lake, XML, and many others. It also allows data storage providers such as Google Big Query, Amazon S3, etc.
  • Developer Tools: It allows different tools also, such as Visual Studio Code, PyCharm, IntelliJ, etc.
  • Partner Solutions: Databricks has integrations with different solutions applications, e.g., Power BI, Tableau, Cassandra, and others. It uses these solutions to access and use data scenarios such as Data Preparations and Transformation, Machine Learning, Business Intelligence, etc.


Let us discuss the features of Databricks:

  • Language

The interface it provides supports multiple coding languages. Using some commands, we can build algorithms. Some languages it supports are R, Python, SQL, etc. e.g., if you have to do data transformation tasks you can do it using Spark SQL, you can do model performance using Python, and Data visualized using R language.

  • Productivity

It increases productivity in a way that it provides you with a collaborative environment for data engineers, and business analysts with a common workspace where you can do tasks more productively. You can also do changes frequently without finding them because it can do itself with its built-in version control tool which increases productivity and reduces effort.

  • Flexibility

Apache spark used this for Cloud environments. In Databricks they updated it to the next level and now Databricks provides scalable jobs of spark in the field of data science. It is flexible for both small-scale and large-scale jobs like development, testing, and Big Data processing. It is also trained to shut down the cluster automatically if it is not in use (in an idle state).

  • Data Source

It has the capability of connecting to many data sources to perform big data analytics on a large scale. It can connect to AWS, Azure, and google cloud as well as CSV, SQL Server, and JSON.

  • High Availability

If, in any case, the cluster crashes, Databricks will relaunch it.

  • Elasticity

It can scale up or down your clusters based on your needs. It depends on how you needed it.

  • Notifications

It will tell you about the progress of your task by sending an email that your tasks are completed, failed, or in progress. It will keep you updated.

Features of Databricks (Diagram)


Databricks is an MLOps-based application that works on the concept of Data Lakehouse with a unified cloud-based platform. It can relate to cloud-based storage providers also such as Google Cloud Storage, AWS S3, etc. The architecture of Databricks will give you much clear understanding of its components and what will be application do.

Layers of Databricks Architecture

  • Delta Lake: It is called a storage layer. Delta lake provides Atomicity, Consistency, Isolation, and Durability) also known as ACID while integrating data processing, metadata handling, etc. It is most compatible with Apache spark API’s.
  • Delta Engine: For the efficient processing of data stored in the data lake, a Delta Engine such that query engine is optimized.
  • Many other in-built tools exist in Databricks. These Tools support Data Science, MLOps, BI Reporting, etc.
  • The interesting and good thing is that all these components are integrated as one and can only be accessed by a single workspace user interface.

We can develop machine learning models and it can analyze performance on its own if we set the job periodically.If multiple users try to run their notebooks on the same cluster, it can go unstable.
It is made for big data computation.All code you have to run must be in notebooks.
It is very easy to move from another platform to this application as it supports many languages.Cannot make a visual query (without any code).
You can transfer the results of a spark query to a Python environment easily.Management system for files is not good.
It is cloud-native so no problem working on any prominent cloud provider.Gets slow sometimes due to more computational work at the same time.
Data storage is very vast and can store structured, unstructured, and streaming.You have to restart the cluster if the system crashes.
Data science tools such as BI, AI, and ML.
Easy to use with a large scope of features.


Three major companies are offering Databricks MLOps application:

  • Amazon Web Services
  • Microsoft Azure
  • Google Cloud

Databricks pricing on AWS

Amazon web services provides three categories of Databricks. Standard, Premium, and Enterprise are three variants of Databricks provided by AWS. There is a difference of features in them such that Standard is the lowest variant and Premium is the upper variant than Standard, and uppermost variant, the Enterprise.

Microsoft Azure
Only one platform for workload and modelsData analytics and ML for business use
Jobs Compute
Jobs Compute Photon
$0.15 / DBU$0.30 / DBU
Delta Live Tables
Delta Live Tables Photon
-$0.30 - $0.54 / DBU
SQL Compute-$0.22/ DBU
All-Purpose Compute
All-Purpose Compute photon
$0.40 / DBU$0.55 / DBU

Standard Premium Enterprise
Only one platform for workload and models Data analytics and ML for business use Data analytics and ML for critical workloads
Jobs light compute
$0.07 / DBU $0.10 / DBU $0.13 / DBU
Jobs Compute
Jobs Compute Photon
$0.10 / DBU $0.15 / DBU $0.20 / DBU
Delta Live Tables
Delta Live Tables Photon
$0.20 - $0.36 / DBU $0.20 - $0.36 / DBU $0.20 - $0.36 / DBU
SQL Compute - $0.22 / DBU $0.22 / DBU
All-Purpose Compute
All-Purpose Compute photon
$0.40 / DBU $0.55 / DBU $0.65 / DBU

Google Cloud
Only one platform for workload and modelsData analytics and ML for business use
Jobs Compute
Jobs Compute Photon
$0.15 / DBU$0.22 / DBU
SQL Compute
-$0.22 / DBU
DLT Advance Compute
DLT Advance Compute Photon
$0.40 / DBU$0.40 / DBU
All-Purpose Compute
All-Purpose Compute photon
$0.40 / DBU$0.55 / DBU

Vertex AI

It is a controlled machine learning end-to-end platform that companies use for the deployment and maintenance of artificial intelligence models. Vertex AI uses 80% fewer lines of code than other platforms. This enables data scientists and machine learning engineers to implement machine learning operations, MLOps, more effectively. This makes the management of ML projects a whole lot more accessible throughout the development lifecycle. Data scientists face problems during patching ML point solutions which affect the entire development phase by causing delays. This reduces the production of ML models. To create ease for data scientists, Vertex AI uses Google Cloud Services for developing machine learning models. This saves time for training and deploying ML models. Data scientists can experiment with these ML models more conveniently and shift toward the production and deployment phase faster. It will bring more of the agile aspect in shifting the overall dynamics of the market.

It offers unified implementations of four concepts:

  • A dataset can be structured or unstructured. It contains metadata (data of data), including observations that can be stored on the Google Cloud Platform, more commonly abbreviated as GCP.
  • A training pipeline is a sequence of instructions to train an ML model using a dataset. These instructions help in reproduction and audibility.
  • A model is an ML model with metadata built with a Training Pipeline.
  • An endpoint can be called by the user for online predictions and helping material. It may have one model or more than one; the same goes for the version of those models. 

Once we have the dataset, we can use it for different machine-learning models. You can retrieve Explainable AI through an endpoint irrespective of how the model has been trained.


  • Vertex Explainable AI

Vertex AI can come into play at various stages in the development lifecycle of an ML model. For instance, let’s assume that your ML model has thrown up a prediction that doesn’t settle well with your perception. Then your main concern will be why this model predicted this. Vertex Explainable AI is used to address this issue. It integrates attributes of the features to make it easier for the data scientist to comprehend the predictions made by the model. 

  • Vertex Feature Store

Another rising aspect is how machine learning models are built and used for distinguishing between feature engineering and the actual building of the ML model. Feature engineering indicates the creation of a normalized dataset that can be used for several different ML models. So, every time a model is built, this process has to be performed to proceed. To simplify this, we have Vertex Feature Store. It is a centralized source for organizing, serving, and storing ML features. 

  • Vertex AI Vizier

Throughout the learning phase of an ML model, it uses a parameter whose value controls this learning process. Such a parameter is called a hyperparameter. A black box optimization service called Vertex AI Vizier aids in enhancing these hyperparameters. Vizier algorithms are constantly under improvement for faster convergence and better handling of real-life edge cases. These models are well-calibrated and self-tuning. It offers a hierarchical search space and multi-objective optimization, unlike traditional single-objective optimization. Vizier can work with any system that is evaluable. For instance, it can be used for finding the most appropriate and effective neural network width, depth, and learning rate for a TensorFlow model (a neural network model with one or more layers).

  • All these features together help in unifying Artificial Intelligence and Machine Learning.

The attached diagram below depicts the learning process of MLOps with Vertex AI:

It reduces the cost of building your own infrastructure.Vertex does not provide a seamless customer experience.
It saves time and lessens the effort to train the ML models.It can improve the usability and speed of building new models.
The models/solutions can be implemented without the risk of production deployment.Many of its tools are marked as previews, and it isn’t up to the hype yet.
It provides faster solutions to problems, thus paving the way to solve complex tasks.Some features are add-ins that require you to pay to use them.


Vertex AI charges for three main events:

  1. Model training;
  2. Deploy the model to an endpoint;
  3. Making predictions by using the models;

Image Data
OperationPrice per node hour (classification)Price per node hour (object detection)
Training edge on-device model$18.00$18.00
Deployment and online prediction$1.375$2.002
Batch prediction$2.222$2.222

Video Data
OperationPrice per node hour (classification, object tracking)Price per node hour (action recognition)
Training edge on-device model$10.78$11.00

Tabular Data
OperationPrice per node hour (classification/regression)
PredictionsSame as for the custom-trained models

Text Data

Legacy data upload (PDF only)
First 1000 pages free every month
$1.50 per 1000 pages
$0.60 per 1000 pages over 5,000,000

$5.00 per 1000 text records
$25.00 per 1000 document pages
(legacy only)

Vertex AI Forecast & Explainable AI

Explainable AI$0.1 - $1.5 approximately
*depends on the regions
Training$21.25/hour (for all regions)

$0.2 per 1K data points
$0.1 per 1K data points
$0.02 per 1K data points

Phase Pricing

Explainable AI
With time series decomposition,
explainability does not incur any
additional charges

$250.00 per TB
* Number of Candidate Models *
Number of Back testing Windows
Prediction $5.00 per TB


MLOps is picking up steam amongst data engineers and ML enthusiasts. Numerous platforms with new and better algorithms to deploy ML models like Qwak are being introduced in the industry.

There are various end-to-end MLOps platforms, as listed above. In this article, Qwak, vertex, Databricks, and amazon Sagemaker are described in detail.

Making a decision about which MLOps platform you want to opt for depends on your desired model and its use cases. Also, you can contact professionals to make a choice. Make sure to read all the features to make a wise choice.

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