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MLOps Course with Training & Placement in USA

Certificate course in MLOps Engineering offers the first in the industry Machine Learning operations program which is a potent culmination of best trainers, innovative course material, and an AI-enabled LMS platform – AISPRY.
  • 40 Hours Classroom & Online Sessions
  • 80 Hours Assignments
  • Complementary Kubernetes for Beginners
  • Complimentary DevOps for Beginners
  • Complementary Python Programming
MLOps Engineering course reviews - 360digitmg
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MLOps Engineering course reviews - 360digitmg
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Academic Partners & International Accreditations
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  • MLOps Course with NEF

Machine Learning - this is the buzzword that has everyone talking! Over the past few years, there has been a steady transition of Machine Learning from being strictly an academic discipline to a very exciting technological domain. The use cases are innumerable from analyzing videos from an autonomous vehicle (AV)s to providing highly personalized medical care, Machine Learning has become ubiquitous in every industry. However, most companies still have not been able to standardize the Machine Learning systems to become fully automated in a way that produces the models and results automatically. This has led to the birth of a new kind of discipline - Machine Learning Operations or MLOps for short. This field is still emerging but as companies look to leverage Machine Learning and Deep Learning to improve their business processes, MLOps Engineers will become one of the most sought after roles. It is estimated that 85% of most Machine Learning projects fail because among other things there is no standardized way of deploying these models to ‘production’. With this course, we aim to bridge the gap between and train MLOps Engineers that can deploy any model to production efficiently and quickly.

MLOps Engineering

MLOps course duration - 360digitmg

Total Duration

40 Hours

MLOps course pre-requisites- 360digitmg


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

MLOps Course Overview

The MLOps Engineering course is a first of its kind program which tackles the subject of deploying the Machine Learning models in production and at scale. This program is born out of a frustration that we experienced while working on consulting engagements and trying to deploy Machine Learning projects into production. The challenges that any ML project faces is to ‘operationalize’ and ‘productionalize’ the code. There is no platform or guidelines that usually exist in other software engineering projects which makes it very difficult to deploy ML models quickly and efficiently. As part of this course, you will learn to deploy models into production environments using cutting edges open-source frameworks like Tensorflow Extended, Apache Beam, Apache Airflow, Kubernetes, and Kubeflow.

MLOps Course Learning Outcomes

This course has been meticulously and laboriously designed to be one of the pioneering works in the field of MLOps. While there is both a lot of demand and supply of Data Scientists, the market is experiencing a crushing shortage of MLOps engineers who can then convert the models into products and services that can be automatically deployed. This course is one of the first to offer MLOps training and will help the learners land coveted jobs as ML Engineers. ML projects have a lot of hidden technical debt as referenced in this wonderful paper. Unfortunately, the ML code will only be a very insignificant part of the entire codebase required to put an ML project into operation as shown in the below picture. So, this course addresses how an ML project can be quickly deployed into production with highly reusable pipelines.

Understand the need for MLOps in the world of data science
Familiarize yourself with Docker and the need for containerization
Become familiar with Tensorflow Extended (TFX) and its various components
Build data ingestion, validation pipelines using TFX
Build orchestrated ML pipelines using Kubeflow, Apache Airflow, Apache Beam
Gain a deep understanding of Kubernetes clusters and how they operate
Utilize the magic of Kubeflow pipelines to build, and deploy ML pipelines
Deploy models in the major cloud platforms - AWS, GCP, and Azure

Block Your Time

MLOps online course - 360digitmg

60 hours

Classroom Sessions

MLOps online course - 360digitmg

40 hours


Who Should Sign Up?

  • Data Scientists
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • DevOps Engineers
  • IT/Software Engineers
  • Machine Learning Architects
  • Model Risk Managers/Auditors

Modules for MLOps Course in USA

The modules of the Data Science course are designed meticulously as per the business trends. Much emphasis is placed on algorithms, concepts, and statistical tools. Python is considered to be the most important programming language and the data scientists have to be pro in using Python. The module introduces descriptive analytics, Data mining, Data visualization, Linear regression, and Multiple Linear regression. Students will learn about Lasso, Ridge, and logistic regression. Learn predictive modeling which is very important and useful. Learn various concepts like Machine Learning algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression. The decision tree algorithm is a popular non-linear tree-based algorithm. Furthermore, the module introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm. Students will learn the Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy. The other modules explain the difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. Learn the concept of a time series and techniques to deal with time-series data such as AR, ARMA, and ARIMA models. Students will also learn the latest techniques called Black box and Support vector machines. This course is delivered with real-time projects, students gain hands-on experience and will able to perform with confidence. This type of training helps to build technical knowledge among the students and prepare them to face real business challenges.

CRISP-DM stands for Cross-industry standard process for data mining and is the bedrock framework of any data science project. This module will explain in detail the cyclical process and all the phases of this methodology. The phases are:

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

Exploratory Data Analysis is usually where the data scientists tend to spend most of their time in the project. This often involves understanding the dataset, summarizing and describing the data at hand.

This is the phase where the statistical techniques are applied to draw inferences from the data. Typically, there are a lot of statistical techniques that are leveraged during this module and applied to the data.

This module introduces the various visual plots such as the bar plot, histogram, scatter plot, box and whiskers plot, etc., and how they can be leveraged to identify patterns and correlations in the data.

This module introduces the basic concepts of probability distributions and how that knowledge is extensively applied in data science projects. The various types of distributions like Gaussian (Normal), Bernoulli, Poisson, Binomial, Multinomial, etc.

This module introduces the concepts of Hypothesis testing and defines in detail about Null and Alternative Hypothesis and the scenarios where each of them could be proved. It discusses in detail the concepts like parametric tests like 1 sample t-test, 2 sample t-test, ANOVA and nonparametric tests like Chi-Square tests.

This module marks the beginning of predictive analytics and lays the foundation for the rest of the course modules of Machine Learning. The module introduces one of the oldest and simplest supervised learning techniques - the linear regression (ordinary least squares).

As can be easily understood, this is an extension of the OLS Linear Regression, by expanding the input space into multiple linear outputs. We discuss this using one of the most commonly used datasets and learn how it can be implemented in Python and R programming languages.

This module discusses the other advanced and useful types of regression models - Lasso and Ridge regression. We discuss the pros and cons of each of these models with practical examples using Python and R.

Also called the Logit model, this algorithm predicts the probability of the event falling into a certain category - Pass/Fail, Fraud/Not Fraud, Yes/No etc. We also get introduced to the concept of maximum likelihood estimation techniques which form the regression coefficients.

This is an extension of the Logistic Regression - the only difference being the output class could have more than 2 classes.

Count data is a data type in which the observations can only consist of non-negative numbers (0,1,2, 3 …). This module introduces regression models for such data such as Poisson Regression and Negative Binomial Regression and their application in survival analytics.

This module provides a general overview of unsupervised learning before launching into one of the most famous of its techniques - Clustering. Multiple algorithms such as connectivity based (Hierarchical), centroid based (k-Means), Distribution based, etc are discussed and implemented along with real- life scenarios.

This module discusses another great technique called the Principal Component Analysis. It is one of the dimensionality reduction algorithms which answers the question - how do we reduce the feature space without losing a lot of information.

This is a rules based machine learning algorithm where the algorithm learns interesting patterns in data and attempts to answer the question - what goes with what. Different algorithms such as Apriori, FP-growth are discussed and implemented.

This module builds on the concepts of association rule learning and implements a recommendation engine. A great example could be the recommendation engine as seen in Amazon or any other e-commerce platform that suggests items based on your current or previous selections.

This module builds on the fundamentals of Graph Theory and delves into the architecture and implementation of any network with emphasis on Social Networks. The module extensively covers the modelling and visualization of networks and some practical implementations.

This module introduces machine learning algorithms by discussing one of the most popular algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression.

This module introduces the decision tree algorithm which is a popular non-linear tree based algorithm. Furthermore, the discussion then introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm.

This module builds on the Bagging algorithm introduced earlier and also adds another ensemble technique called Boosting. The theory behind both the ensemble techniques is discussed in detail.

This module introduces Gradient Descent and explains how it is combined with boosting to achieve gradient boosting. Then we discuss the advanced implementations of gradient descent such as AdaBoost and Extreme Gradient Boosting.

All the prior modules have been dealing with structured data (data that can be stored as rows and columns and that follow relational requirements). This module introduces the unstructured data in the form of text data and provides some tools and techniques to analyze it.

This module discusses another important algorithm - Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy.

This module discusses the perceptron, which is the fundamental concept of an artificial neural network. The basic version of an ANN called the multi layer perceptron (MLP) is also discussed.

This module discusses the various building blocks of neural networks - perceptron, backpropagation, activation functions, dropout, dense vs sparse layers ,etc.

The difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. The architecture is explored and some guidelines are laid out.

This module discusses another Black Box technique - Support Vector Machines.

This module builds on the concepts from Module 12 and discusses how survival analysis is performed in practical and real-life scenarios.

This module introduces the concept of a time series. All the previous modules (except text data) were longitudinal or cross-sectional datasets. There is no temporal component in the data (or it is ignored for the purposes of algorithm). This module introduces techniques to deal with time-series data such as AR, ARMA and ARIMA models.

The following modules will take the student through the course in a step by step fashion building upon the foundations and progressing to advanced topics. Initially, the first module introduces the students to the general ML workflow and the different phases in an ML lifecycle. The subsequent chapters will introduce the participant to Tensorflow Extended (TFX) followed by a deep dive into its various components and how they facilitate the different phases of the ML lifecycle. The learner will then gain an understanding of how TFX components are used for data ingestion, validation, preprocessing, model training and tuning, evaluation, and finally deployment. Later chapters will also introduce the learner to the orchestration software Kubeflow, Apache Airflow, and Apache Beam. Using a combination of all these tools, the learner will be able to deploy models in some popular cloud platforms like AWS, GCP, and Azure.

One of the key benefits of investing in machine learning pipelines is that all the steps of the data science life cycle can be automated. As and when new data is available (for training), ideally an automated workflow should be triggered which performs data validation, preprocessing, model training, analysis, and deployment. A lot of data science teams spend ridiculous amounts of time, money and resources doing all these tasks manually. By investing in an ML workflow, these issues could be resolved. Some of the benefits include (but are not limited to):

  • Create new models, don’t get stuck maintaining Existing Models
  • Preventing and Debugging Errors
  • Audit Trail
  • Standardization

The Tensorflow Extended (TFX) library contains all the components that are needed to build robust ML pipelines. Once the ML pipeline tasks are defined using TFX, they can then be sequentially executed with an orchestration framework such as Airflow or Kubeflow Pipelines.


During this module, you will learn to install TFX and its fundamental concepts along with some literature which will make the future modules easier to understand. Additionally, you will learn Apache Beam which is an open-source tool that helps in defining and executing some data manipulation tasks. There are two basic purposes of Apache Beam in the TFX pipelines:


  • It forms the base of several TFX components for data preparation/preprocessing and validation
  • Is one of the orchestration frameworks for TFX components so a good understanding of Apache Beam is necessary if you wish to write custom components

In the previous modules, we set up TFX the ML MetadataStore. In this module, we discuss how to ingest data into a pipeline for consumption in various TFX components (like ExampleGen). There are several TFX components that allow us to ingest data from files or services. In this module we discuss the fundamental concepts, explore how to split the datasets into train and eval files and practically understand how to join multiple data sources into one all-encompassing dataset. We will also understand what a TFRecord stands for and how to interact with external services such as Google Cloud BigQuery. You will also learn how TFRecord can work with CSV, Apache Avro, Apache Parquet etc. This module will also introduce some strategies to ingest different forms of data structured, text, and images. In particular, you will learn

  • Ingesting local data files
  • Ingesting remote data files
  • Ingesting directly from databases (Google BigQuery, Presto)
  • Splitting the data into train and eval files
  • Spanning the datasets
  • Versioning
  • Working with unstructured data (image, text etc)

Data validation and preprocessing is essential for any machine learning algorithm to perform well. The old adage ‘garbage-in, garbage out’ perfectly encapsulates this fundamental characteristic of any ML model. As such, this module will focus on validation and preprocessing of data to ensure the creation of high performing ML models.


Data Validation: This module will introduce you to a Python package called Tensorflow Data Validation (TFDV) which will help in ensuring that


  • The data in the pipeline is in line with what the feature engineering step expects
  • Assists in comparing multiple datasets
  • Identifies if the data changes over time
  • Identify the schema of the underlying data
  • Identify data skew and data shift

Real-world data is extremely noisy and not in the same format that can be used to train our machine learning models. Consider a feature which has values as Yes and No tags which need to be converted to a numerical representation of these values (e.g., 1and 0) to allow for consumption by an ML model. This module focuses on how to convert features into numerical representations so that your machine learning model can be trained.


We introduce Tensorflow Transform (TFT) which is the TFX component specifically built for data preprocessing allowing us to set up preprocessing steps as TensorFlow graphs. Although this step of the model has a considerable learning curve it is important to know about it for the following reasons:


  • Efficiently preprocessing the data within the context of the entirety of the dataset
  • The ability to scale the data preprocessing steps efficiently
  • Develop immunity to potentially encountering training-serving skew

As part of the previous modules, we completed data preprocessing and transforming the data to fit our model formats. The next logical step in the pipeline is to begin the model training, perform analysis on the trained models and evaluate and select the final model. This module already assumes that you have the knowledge of training and evaluating models so we don’t dwell fully in the different model architectures. We will learn about the TFX Trainer component which helps us in training a model which can easily be put into production. Additionally, you will also be introduced to Tensorboard which can be used to monitor training metrics, visualize word embeddings in NLP problems or view activations for layers in a deep learning model.

During the model training phase, we typically monitor its performance on an evaluation set and use Hyperparameter optimization to improve performance. As we are building an ML pipeline, we need to remember that the purpose is to answer a complex business question modelling a complex real-world system. Oftentimes our data deals with people, so a decision that is made by the ML model could have far-reaching effects for real people and sometimes even put them in danger. Hence it is critical that we monitor your metrics through time—before deployment, after deployment, and while in production. Sometimes it may be easy to think that since the model is static it does not need to be monitored constantly, but in reality, the incoming data into the pipeline will more likely than not change with time, leading to performance degradation.


TFX has produced the Tensorflow Model Analysis (TFMA) module which is a fantastic and super-easy way to obtain exhaustive evaluation metrics such as accuracy, precision, recall, AUC metrics and f1-score, RMSE, MAPE, MAE among others. Using TFMA, the metrics can be visually depicted in the form of a time series spanning all the different model versions and as an add-on, it gives the ability to view metrics on different splits of the dataset. Another important feature is that by using this module it is easy to scale to large evaluation sets via Apache Beam. Additionally in this module, you will learn

  • How to analyse a single model using TFMA
  • How to analyse multiple models using TFMA
  • Checking for fairness among models
  • Apply decision thresholds with fairness indicators
  • Tackling model explainability
  • Using the TFX components Resolver, Evaluator and Pusher to analyze models automatically

This module is in many ways the crux of the MLOps domain because the original question was - ‘I have a great ML model prototype, how do I deploy it to production?’. With this module, we answer that question with - here is how: using Tensorflow Serving which allows ML engineers and data engineers to deploy any TensorFlow graph allowing them to generate predictions from the graph through its standardized endpoints. TF Serving takes care of the model and version control allowing for models to be served based on policies and the ability to load models from various sources. All of this is accomplished by focussing on high-performance throughput to achieve low-latency predictions. Some of the topics discussed in this module are:

  • How to export models for TF (TensorFlow) Serving
  • Signatures of Models
  • How to inspect exported models
  • Set up of TF Serving
  • How to configure a TF Server
  • gRPC vs REST API architecture
  • How to make predictions from a model server using
    • gRPC
    • REST
  • Conduct A/B testing using TF Serving
  • Seeking model metadata from the model server using
    • gRPC
    • REST
  • How to configure batch inference requests

Pipeline orchestration tool is crucial to ensure that we are abstracted from having to write some glue code to automate an ML pipeline. Pipeline orchestrators usually lie under the components introduced in the previous modules.


  • Decide upon the orchestration tool - Apache Beam vs Apache Airflow vs Kubeflow
  • Overview of Kubleflow pipelines on AI Platform
  • How to push your TFX Pipeline into production
  • Pipeline conversion for Apache Beam and Apache Airflow
  • How to set up and orchestrate TFX pipelines using
    • Apache Beam
    • Apache Airflow
    • Kubeflow

This module builds on the concepts previously discussed and delves into some of the data-driven algorithms available to address the time-series problems such as MA, EMA and Econometric Models.

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