672 AI Test & Evaluation Specialist Advanced KSAT Journey

The world of technology continues to transform at a rapid pace, with intelligent technology incorporated at every stage of the business process. Intelligent information systems (IIS) reduce the need for routine human labor and allow companies to focus instead on hiring creative professionals. In this course, you'll explore the present and future roles of intelligent informational systems in AI development, recognizing the current demand for IIS specialists. You'll list several possible IIS applications and learn about the roles AI and ML play in creating them. Next, you'll identify significant components of IIS and the purpose of these components. You'll examine how you would go about creating a self-driving vehicle using IIS components. Finally, you'll work with Python libraries to build high-level components of a Markov decision process.

Microsoft Cognitive Toolkit provides powerful machine learning and deep learning algorithms for developing AI. Knowing which problems are easier to solve using Microsoft CNTK over other frameworks helps AI practitioners decide on the best software stack for a given application. In this course, you'll explore advanced techniques for working with Microsoft CNTK and identify which cases benefit most from MS CNTK. You'll examine how to load and use external data using CNTK and how to use its imperative and declarative APIs. You'll recognize how to carry out common AI development tasks using CNTK, such as working with epochs and batch sizes, model serialization, model visualization, feedforward neural networks, and machine learning model evaluation. Finally, you'll implement a series of practical AI projects using Python and MS CNTK.

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.

Designing successful and competitive AI products involves thorough research on its existing application in various markets. Most large scale businesses use AI in their workflows to optimize business operations. AI Architects should be aware of all possible applications of AI so they can look at market trends and come up with the most appropriate, novel, and useful AI solutions for their industry. In this course, you'll explore examples of standard AI applications in various industries like Finance, Marketing, Sales, Manufacturing, Transportation, Cybersecurity, Pharmaceutical, and Telecommunications. You'll examine how AI is utilized by leading AI companies within each of these industries. You'll identify which AI technologies are common across all industries and which are industry-specific. Finally, you'll recognize why AI is imperative to the successful operation of many industries.

Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.

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.

Continuous integration and continuous deployment (CI/CD) are crucial in machine learning operations (MLOps) as they automate the integration of ML models into software development. Continuous machine learning (CML) refers to an ML model's ability to learn continuously from a stream of data. In this course, you will build a complete Data Version Control (DVC) machine learning pipeline in preparation for continuous machine learning. You will modularize your machine learning workflow using DVC pipelines, configure DVC remote storage on Google Drive, and set up authentication for DVC to access Google Drive. Next, you will configure CI/CD through CML and use the open-source CML framework to implement CI/CD within your machine learning project. Finally, you will see how for every git push to your remote repository, a CI/CD pipeline will execute your experiment and generate a CML report with model metrics for every GitHub commit. At the end of this course, you will be able to use DVC's integration with CML to build CI/CD pipelines.

Even with several years of practical experience in the security field, knowledge and application of specific security concepts and principles may have eluded even the seasoned security professional. Use this course to brush up on some of the vital, core security principles, such as confidentiality, integrity, and non-repudiation. Be reminded of the critical role of security design in the ISO OSI 7-layer Reference Model and the 4-layer TCP/IP Reference Model. Upon completion of this course, you'll be fully attuned to the most fundamental aspects of security. Furthermore, you can use this course to prepare for the CISSP exam.

A security professional must be familiar with risk management concepts to be able to apply them effectively. Use this course to explore the management of risks to tangible and intangible assets. Get familiar with the details of vulnerability and risk assessment, countermeasure selection and implementation, and risk frameworks. This course will also help you examine the monitoring, measuring, and reporting of risk and delve further into threat modeling and supply chain risk management (SCRM). You'll have an understanding of risk management fundamentals and how to apply them after completing this course. Moreover, you can also use this course to prepare for the CISSP exam.