Currently, many data scientists struggle to reap the full benefits of the machine learning models they create. This is not because of a lack of knowledge to design the right models, but mainly due to a lack of required skills to successfully operationalize these models into the existing software architectures in an organization.
With this in mind, we’re excited to launch the all-new Machine Learning DevOps Engineer Nanodegree Program. The program focuses on the software engineering fundamentals required to successfully streamline the deployment of data and machine learning models in a production-level environment.
Machine Learning DevOps: Optimize Your Machine Learning Models
Machine Learning DevOps or ML Ops is the fusion of machine learning and operations. MLOps is a set of methods used to automate the various aspects and stages of machine learning model building and monitoring over time.
According to a Forbes report, the ML Ops market is estimated to be over $4 billion by 2025. Not only this it is also being seen to be a major component of the AI solution landscape.
Why Should You Learn MLOps Now?
TechCrunch reports that more firms are investing resources into MLOps to increase productivity and create trusted, enterprise-grade models.
However, since MLOps is a relatively new career opportunity, the field lacks skilled engineers. One of the reasons for this gap is the lack of quality training material that can train people for the job. Udacity’s Machine Learning DevOps Nanodegree program does just that.
Additionally, you must know how to use Jupyter notebooks to solve data science-related problems. You should also be well-versed in writing scripts using NumPy, pandas, Scikit-learn, TensorFlow/PyTorch in Jupyter notebooks that clean data (as part of ETL), feed it into a machine learning model and validate the performance of the model. Finally, you should know everything about using the Terminal, version control in Ginand using GitHub.
This four-month advanced Nanodegree program will teach you to do the following:
- Implement production-ready Python code/processes for deploying ML models outside of cloud-based environments facilitated by tools such as AWS SageMaker, Azure ML, etc.
- Engineer automated data workflows that perform continuous training (CT) and model validation within a CI/CD pipeline based on updated data versioning
- Create multi-step pipelines to retrain and deploy models after data updates automatically
- Track model summary statistics and monitor model online performance over time to prevent model-degradation
It comes with a gamut of projects designed to give you a real-world experience.
COURSE 1: Clean Code Principles
Develop skills that are essential for deploying production machine learning models.
PROJECT 1: Predict Customer Churn with Clean Code
In this project, you’ll implement your learnings to identify credit card customers most likely to churn. This project will give you practice using your skills for testing, logging, and coding best practices from the lessons.
It will also introduce you to a problem data scientists across companies face all the time: How do we identify (and later intervene with) customers who are likely to churn?
COURSE 2: Building a Reproducible Model Workflow
This course empowers students to be more efficient, effective and productive with modern, real-world ML projects by adopting best practices around reproducible workflows.
PROJECT 2: Build an ML Pipeline for Short-term Rental Prices in NYC
You’ll write a machine learning pipeline to solve the following problem: A property management company is renting rooms and properties in New York for short periods on various rental platforms. They need to estimate the typical price for a given property based on the cost of similar properties. The company receives new data in bulk every week, so the model needs to be retrained with the same cadence, necessitating a reusable pipeline.
You’ll write an end-to-end pipeline covering data fetching, validation, segregation, train and validation, test and release. They will run it on an initial data sample and then re-run it on a new data sample simulating a new data delivery.
COURSE 3: Deploying a Scalable ML Pipeline in Production
This course teaches students how to deploy a machine learning model into production robustly.
PROJECT 3: Deploying a Machine Learning Model on Heroku with FastAPI
In this project, you’ll deploy a machine learning model on Heroku. You’ll use Git and DVC to track their code, data, and model while developing a simple classification model on the Census Income Data Set.
After developing the model, you’ll finalize it for production by checking its performance on slices and writing a model card summarizing the knowledge about the model. You’ll put together a Continuous Integration and Continuous Deployment framework and ensure their pipeline passes a series of unit tests before deployment.
COURSE 4: Automated Model Scoring and Monitoring
This course will help students automate the DevOps processes required to score and re-deploy ML models.
PROJECT 4: A Dynamic Risk Assessment System
You’ll make predictions about attrition risk in a fabricated dataset. After completing this project, you’ll have a full end-to-end, automated ML project that performs risk assessments. This project can be a useful addition to your portfolio, and the concepts you apply in the project can be directly implemented to business problems across industries.
Enroll Now in the Machine Learning DevOps Nanodegree Program
If you’re a data scientist and want to help integrate your machine learning models into your company’s operating tech stack, then this program is for you.
Check out the Machine Learning DevOps Nanodegree program and start learning now!