Any aspiring AI developer has to clearly understand the responsibilities and expectations when entering the industry in this role. AI Developers can come from various backgrounds, but there are clear distinctions between this role and others like Software Engineer, ML Engineer, Data Scientist, or AI Engineers. Therefore, any AI Developer candidate has to posses the required knowledge and demonstrate proficiency in certain areas. In this course you will learn about the AI Developer role in the industry and compare the responsibilities of AI Developers with other engineers involved in AI development. After completing the course, you will recognize the mindset required to become a successful AI Developer and become aware of multiple paths for career progression and future opportunities

A working knowledge of multiple AI development frameworks is essential to AI developers. Depending on the particular focus, you may decide on a particular framework of your choice. However, various companies in the industry tend to use different frameworks in their products, so knowing the basics of each framework is quite helpful to the aspiring AI Developer. In this course you will explore popular AI frameworks and identify key features and use cases. You will identify main differences between AI frameworks and work with Microsoft CNTK and Amazon SageMaker to implement model flow.

Microsoft Cognitive Toolkit (CNTK) is an open source framework for distributed deep learning suitable for commercial applications. It's primarily used to develop neural networks but can also be used for machine learning and cognitive computing. It supports multiple languages and can easily be used in the cloud. These factors make CNTK a good fit for various AI projects. In this course, you'll explore the basic concepts required to work with Microsoft CNTK. You'll compare other frameworks with CNTK, examine the process of creating machine learning and deep learning models with CNTK, and learn how it can be used with several cloud services. You'll move on to learn where to access CNTK documentation, community, and installation guidelines. Finally, you'll use CNTK to predict diabetes using retina scans.

Keras is a deep learning package suitable for beginners. Although it is applied in multiple standard deep learning use cases, it is also used by commercial-grade products. To facilitate this, Keras provides additional, flexible options on top of the well-known Sequential API, which allow you to customize and create various neural networks. To utilize this, however, requires a more in-depth knowledge of the Keras framework. In this course, you'll develop the core skills needed to work with the Keras framework. You'll explore the advantages and disadvantages of using Keras over other frameworks, and examine how Keras can be used with TensorFlow. You'll move on to recognize how Keras is used for machine learning and deep learning. Finally, you'll implement two deep learning projects using the Keras framework.

Apache Spark provides a robust framework for implementing machine learning and deep learning. It takes advantage of resilient distributed databases to provide a fault-tolerant platform well-suited to developing big data applications. Because many large companies are actively using this framework, AI developers should be familiar with the basics of implementing AI with Apache Spark and Spark ML. In this course, you'll explore the concept of distributed computing. You'll identify the benefits of using Spark for AI Development, examining the advantages and disadvantages of using Spark over other big data AI platforms. Next, you'll describe how to implement machine learning, deep learning, natural language processing, and computer vision using Spark. Finally, you'll use Spark ML to create a movie recommendation system commonly used by Netflix and YouTube.

Amazon offers AI developers a wide variety of tools and frameworks including Amazon Web Services (AWS) and the Amazon Machine Learning (ML) framework. By integrating complex machine and deep learning development with the extensive computing capabilities of Amazon, Amazon ML allows AI developers to adopt big data AI services. With many companies actively using AWS and Amazon ML, a basic knowledge of this framework is beneficial. In this course, you'll learn how to use Amazon ML together with AWS, to work with big data, and to create machine and deep learning models. You'll also examine the basics of automated model deployment with Amazon SageMaker. Next, you'll explore how to use Amazon ML for image and video analysis, text-to-speech translation, and text analytics. Finally, you'll implement a system to analyze movie review sentiment using the Amazon ML framework.

Cognitive modeling can provide additional human qualities to AI systems. It is traditionally used in cognitive machines and expert systems. However, with extra computing power, it can be applied to more profound AI approaches like neural networks and reinforcement learning systems. Knowledge of cognitive modeling applications is essential to any AI developer aspiring to design AI architectures and develop large-scale applications. In this course, you'll examine the role of cognitive modeling in AI development and its possible applications in NLP, image recognition, and neural networks. You'll outline core cognitive modeling concepts and significant industry use cases. You'll list open source cognitive modeling frameworks and explore cognitive machines, expert systems, and reinforcement learning in cognitive modeling. Finally, you'll use cognitive models to solve real-world problems.

Robots can utilize machine learning, deep learning, reinforcement learning, as well as probabilistic techniques to achieve intelligent behavior. This application of AI to robotic systems is found in the automotive, healthcare, logistics, and military industries. With increasing computing power and sophistication in small robots, more industry use cases are likely to emerge, making AI development for robotics a useful AI developer skill. In this course, you'll explore the main concepts, frameworks, and approaches needed to work with robotics and apply AI to robots. You'll examine how AI and robotics are used across multiple industries. You'll learn how to work with commonly used algorithms and strategies to develop simple AI systems that improve the performance of robots. Finally, you'll learn how to control a robot in a simulated environment using deep Q-networks.

Adopting the foundational techniques of natural language processing (NLP), together with the Bidirectional Encoder Representations from Transformers (BERT) technique developed by Google, allows developers to integrate NLP pipelines into their projects efficiently and without the need for large-scale data collection and processing. In this course, you'll explore the concepts and techniques that pave the foundation for working with Google BERT. You'll start by examining various aspects of NLP techniques useful in developing advanced NLP pipelines, namely, those related to supervised and unsupervised learning, language models, transfer learning, and transformer models. You'll then identify how BERT relates to NLP, its architecture and variants, and some real-world applications of this technique. Finally, you'll work with BERT and both Amazon review and Twitter datasets to develop sentiment predictors and create classifiers.

In this lab, you will perform AI Developer tasks such as implementing prediction models and using the CNTL framewwork, as well as performing sentiment analysis and image classification. Then, test your skills by answering assessment questions after performing categoary classification using BERT and prediction analysis using pySpark.

Final Exam: AI Developer will test your knowledge and application of the topics presented throughout the AI Developer track of the Skillsoft Aspire AI Apprentice to AI Architect Journey.