MLOps Training
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MLOps is crucial in today’s data-driven landscape, where organizations require efficient systems for deploying and managing ML models. Our course provides a thorough understanding of essential tools like TensorFlow Extended, Apache Airflow, Apache Beam, Kubernetes, and Kubeflow, which are pivotal for effective ML operations. Participants will gain practical experience through real-time projects and learn how to apply these tools in real-world scenarios, preparing them for lucrative opportunities in leading companies.
MLOps Course Overview
MLOps, a fusion of Machine Learning and IT Operations, bridges the gap between data scientists and IT professionals by streamlining the deployment and management of machine learning models. Our MLOps course addresses the industry's need for effective ML model production and scaling. It covers essential tools such as TensorFlow Extended, Apache Airflow, Apache Beam, Kubernetes, and Kubeflow, which are crucial for successful ML model deployment.
Through real-time projects and hands-on experience, participants will gain practical exposure to working in real-world applications. This course is tailored to prepare students for high-demand roles in major companies, equipping them with the skills to manage ML models effectively in production environments.
What is MLOps?
MLOps, short for Machine Learning Operations, is an emerging discipline that integrates machine learning with IT operations to streamline the deployment, management, and scalability of ML models. It involves the use of various tools and technologies to ensure that ML models are efficiently developed, deployed, and maintained in production environments. MLOps aims to enhance collaboration between data scientists and IT professionals, ensuring that ML models are operationally robust and scalable.
MLOps Course Learning Outcomes
360DigiTMG's MLOps training offers a comprehensive blend of theoretical knowledge and practical application. The curriculum is designed to address the current trends and essential concepts needed to excel in MLOps. The course covers critical topics, including:
This course equips students with the necessary tools and expertise to become proficient MLOps engineers, addressing the industry's shortage of skilled professionals capable of deploying and managing machine learning models efficiently.
Block Your Time
15+ hours
Self-Paced Sessions
Who Should Sign Up?
- Math, Science and Commerce graduates
- Mid-level executives with no programming knowledge
- Managers with basic programming knowledge
- Anyone with interest in coding
- Students aspiring to become adroit programmers
Training Modules of MLOps Course
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
MLOps: Current Trends
MLOps is rapidly becoming essential for efficient deployment and management of machine learning models, with its importance growing as organizations seek scalable solutions. Modern MLOps integrates seamlessly with cloud platforms and containerization technologies, enabling robust, scalable ML workflows. Automation and continuous integration are key trends, allowing rapid development, testing, and deployment of ML models with minimal manual intervention. CI/CD pipelines, model monitoring, and automated retraining processes ensure that models remain effective and adapt to evolving data patterns.
The rise of hybrid and multi-cloud environments further accelerates MLOps adoption, offering flexibility and cost optimization. Organizations are leveraging these environments to enhance their ML operations and improve performance across different infrastructures. Additionally, MLOps is addressing growing data privacy and security concerns by incorporating advanced security practices and ensuring compliance with data protection regulations. This focus on security, combined with advancements in model interpretability, is driving MLOps integration into mainstream business practices, making it a cornerstone of effective and efficient ML operations.
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Certificate
Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The MLOps Certificate is your passport to an accelerated career path.
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FAQs about MLOps
MLOps, or Machine Learning Operations, is a discipline that integrates machine learning with IT operations to streamline the deployment, management, and scalability of ML models. It involves various tools and technologies to ensure efficient development, deployment, and maintenance of ML models in production environments.
MLOps is crucial for efficiently deploying and managing ML models, ensuring they are scalable and operationally robust. It helps bridge the gap between data scientists and IT professionals, enabling seamless model deployment and ongoing management.
This program is suitable for individuals at all levels, from beginners to those seeking advanced skills in data analytics. Whether you are new to data analysis or looking to enhance your existing skills, this program offers valuable learning opportunities for everyone. The course covers essential tools such as TensorFlow Extended (TFX), Apache Airflow, Apache Beam, Kubernetes, and Kubeflow. These tools are pivotal for effective ML operations and model deployment.
You will gain expertise in deploying and managing ML models using modern tools and technologies. Topics include MLOps fundamentals, containerization with Docker, orchestration of ML pipelines, Kubernetes, and cloud deployment across platforms like AWS, GCP, and Azure.
This course is designed for individuals seeking to bridge the gap between data science and IT operations. It is ideal for data scientists, IT professionals, and anyone interested in developing skills to effectively deploy and manage ML models.
The course is accredited by Swayam, an initiative by the Government of India that ensures high educational standards. This accreditation adds credibility and value to the training provided by 360DigiTMG.
Upon completing the course, you will earn a certificate from 360DigiTMG, accredited by Swayam. This certification demonstrates your proficiency in MLOps and enhances your employability in the field.
The course includes hands-on experience through real-time projects, allowing you to apply the tools and techniques learned in practical scenarios. This prepares you for high-demand roles in major companies and equips you with skills to manage ML models effectively.
Current trends include the integration of MLOps with cloud platforms and containerization technologies, automation of CI/CD pipelines, model monitoring, and addressing data privacy and security concerns. Hybrid and multi-cloud environments are also driving the adoption of MLOps.
Swayam accreditation ensures that the course meets high standards of quality and education. It provides recognition and validation for the training received, enhancing the value of the certification and ensuring the course content is comprehensive and relevant.
Jobs for Python professionals
Python programming has gone mainstream and presents several avenues to meaningful work. So, if you are skilled in Python you can become a Developer, Product Manager, Data Analyst, Financial Advisor, etc.
Salaries for Python professionals
With each passing year, Python has taken the world of programming by storm. The average salary of a Python developer in the field of Data Science is Rs 618,413 and as a web developer, the average salary comes to Rs 409,101.
Projects in Python
The best way to understand the code is to work on projects to test your application. The various projects one can work on include creating and training machine learning models with TensorFlow and for data analysis and data mining purposes using Scikit-learn.
Open Source tools in Python
Some of the most widely used Python tools by coders and developers include Scikit-Learn, Keras, SciPy, and Theano and some of the automation testing tools include Selenium, TestComplete, and Robot Framework.
Modes of training for Core Python
The course in India is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Industrial application of Python
Python is used across various applications that involve predictive maintenance and automation tasks. It is also used for Console-based applications that run from the command-line or shell. Frameworks like PyTorch and TensorFlow also use Python.
Companies That Trust Us
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