- Basic understanding of Azure services
- Familiarity with
Python programming- Knowledge of
data science and
machine learning concepts
- Experience with using Jupyter Notebooks
DP-3007: Train and deploy a machine learning model with Azure Machine Learning Certification Training Overview
The DP-3007 course prepares students to use Azure Machine Learning for building, training, and deploying ML models. It covers data exploration, cleaning, preparation, and using Azure tools for experiments. You'll learn about custom models, frameworks like
TensorFlow or PyTorch, and leveraging AutoML for model creation. Model deployment and management through endpoints, monitoring with Azure Machine Learning services, and using pipelines for workflow automation are fundamental aspects of the course. The training also highlights ML security practices and ensures a solid practical understanding of the Azure ML platform.
Why Should You Learn DP-3007: Train and deploy a machine learning model with Azure Machine Learning?
The DP-3007 course equips learners with key skills: implementing data solutions, running experiments, training predictive models, and deploying ML models using Azure. Achieving proficiency in these areas enhances job competitiveness, accelerates professional growth in the AI field, and improves organizations' capability to innovate and make data-driven decisions.
Target Audience for DP-3007: Train and deploy a machine learning model with Azure Machine Learning Certification Training
- Data scientists
- ML engineers
- IT professionals
- Developers with an interest in machine learning
- Azure users looking to utilize ML services
- Technical professionals aspiring to learn model deployment
Why Choose Koenig for DP-3007: Train and deploy a machine learning model with Azure Machine Learning Certification Training?
- Certified Instructor-led sessions
- Enhances career opportunities in ML with Azure
- Tailored training programs to meet individual needs
- Exotic destination training options
- Competitive and budget-friendly pricing structure
- Recognized as a leading training institute
- Adjustable scheduling with flexible dates availability
- Live, interactive online training environment
- Comprehensive catalog of courses across various technologies
- Accredited and officially recognized training partner
DP-3007: Train and deploy a machine learning model with Azure Machine Learning Skills Measured
After completing DP-3007 training, an individual can create and configure Azure Machine Learning workspaces, run experiments and train models, select appropriate algorithms and hyperparameters, use feature engineering and data transformations, deploy and manage machine learning models in production environments, and monitor and analyze model performance. The skills also include knowledge of Azure's ML services, understanding of data storage options, and proficiency in using Azure ML Studio for designing and deploying ML pipelines.
Top Companies Hiring DP-3007: Train and deploy a machine learning model with Azure Machine Learning Certified Professionals
Top companies hiring DP-3007 certified professionals include Microsoft, IBM, Deloitte, Accenture, and Amazon Web Services. They seek experts in training and deploying ML models on Azure, valuing the certification for its focus on practical Azure ML skills.The learning objectives of the DP-3007 course, "Train and deploy a machine learning model with Azure Machine Learning," typically include:
1. Understanding the Azure Machine Learning ecosystem and its components.
2. Utilizing the Azure ML workspace to manage the machine learning lifecycle.
3. Preprocessing data and engineering features using Azure ML tools.
4. Training machine learning models with various algorithms within Azure ML.
5. Evaluating model performance and selecting the optimal model.
6. Deploying models to production environments using Azure ML services.
7. Consuming deployed models and integrating them with applications.
8. Monitoring and managing deployed models for performance and drift.
Participants should learn to build, deploy, and maintain machine learning solutions in the Azure cloud environment after completing this course.
