Course Prerequisites
• Basic understanding of Python programming language
• Familiarity with data structures and algorithms
• Foundational knowledge in Statistical Methods
• Prior experience with machine learning algorithms
• Proficiency in using libraries such as TensorFlow, Keras, PyTorch and Scikit-learn
• Basic understanding of DevOps and MLOps practices
• Experience with cloud computing, preferably AWS or GCP.
Machine Learning in Production Certification Training Overview
Machine Learning in Production certification training arms learners with essential skills to deploy ML models effectively. The course covers a wide range of topics, such as strategies for model deployment, ways to maintain and monitor models, methodologies for retraining, handling data drift, and managing experiments. Learners also grasp understanding ML infrastructure, robust testing methods, and various ways for models' continuous integration and deployment. This certification helps learners understand the complexities of ML, the production pipeline, and how to build scalable systems.
Why Should You Learn Machine Learning in Production?
The Machine Learning in Production course in stats equips learners with innovative strategies for designing, building, and deploying ML models effectively. Key benefits include gaining practical knowledge, strengthening problem-solving skills, optimizing processes, and improving predictions in various business domains. The course also enhances employability in the thriving field of data-analysis and artificial intelligence.
Target Audience for Machine Learning in Production Certification Training
- Data scientists seeking to enhance their skills
- AI specialists interested in applying machine learning
- Developers wanting to incorporate machine learning into their applications
- IT professionals tasked with launching or managing AI projects
- Business analysts needing to understand machine learning implementations
- Tech enthusiasts keen on understanding machine learning applications in production.
Why Choose Koenig for Machine Learning in Production Certification Training?
- Gain knowledge from Certified Instructors with vast industry experience.
- Boost your career with a certificate in Machine Learning in Production.
- Benefit from Customized Training Programs tailored to your learning needs.
- Enjoy Destination Training at your preferred location.
- Avail Affordable Pricing without compromising on quality.
- Be part of a Top Training Institute renowned globally.
- Choose Flexible Dates for training as per your convenience.
- Learn through Instructor-Led Online Training remotely.
- Select from a Wide Range of Courses across multiple technologies.
- Trust in Accredited Training recognized by major certifying bodies globally.
Machine Learning in Production Skills Measured
After completing the Machine Learning in Production certification training, an individual can acquire skills like designing, building, and deploying machine learning models in a production environment. They may also learn how to use tools for data preparation, feature engineering, model building and evaluation. Additionally, the training can equip them with proficiency in orchestrating a machine learning solution, applying DevOps practices to machine learning pipelines, evaluating and improving model accuracy, as well as managing and monitoring machine learning models in production.
Top Companies Hiring Machine Learning in Production Certified Professionals
Top tech giants like Google, Amazon, Facebook, and Microsoft lead the charge in hiring Machine Learning in Production certified professionals. Other emerging firms such as Uber, IBM, Edgeverve, Oracle, and Intel, which are heavily investing in AI and machine learning technologies, are also eager to hire such skilled professionals.
Learning Objectives - What you will Learn in this Machine Learning in Production Course?
The learning objectives of the Machine Learning in Production course are designed to equip students with the practical understanding and skills needed to deploy and manage machine learning models. The course aims to teach students how to modularize, package, and service machine learning models; use tools for managing and monitoring models in production; understand the life cycle of a machine learning model and the challenges of deploying models; and apply best practices for testing and maintaining models in production. It further intends to provide a clear understanding on crucial topics like model versioning, retraining, and data drift.
