An AI Assistant on Your Company Knowledge, Without Code Training Course

This live, instructor-led training in Latvia (online or onsite) is tailored for AI engineers and software developers at beginner to intermediate levels seeking to establish a strong foundation in AI engineering principles and practices.

By the conclusion of this training, participants will be able to:

• Comprehend the core concepts and technologies underlying AI and machine learning.
• Build basic machine learning models using TensorFlow and PyTorch.
• Apply AI methodologies to solve practical challenges in software development.
• Manage and maintain AI projects by implementing best practices in AI engineering.
• Recognize the ethical considerations and duties involved in creating AI systems.

In this two-day, hands-on initiative, participants gain the practical abilities to utilize Generative AI tools effectively, securely, and consistently across technical and operational duties. The training highlights the transformation of AI into a reliable everyday assistant for drafting and enhancing documentation, organizing requirements, speeding up analysis and reporting, aiding in problem resolution, and optimizing cross-functional communication. Learners will acquire proven prompting patterns, validation techniques to mitigate errors and 'hallucinations,' and simple governance practices to protect sensitive information and intellectual property. The course is engineered to deliver instantly usable templates, workflows, and a repeatable methodology for AI-assisted tasks.

This instructor-led live training, conducted in Latvia (either online or onsite), is tailored for intermediate to advanced AI experts and software developers seeking to develop intelligent applications using AI and ML.

By the conclusion of this program, participants will be equipped to:

• Comprehend the sophisticated concepts and technologies driving AI and ML.
• Utilize data analysis and visualization to support AI/ML model creation.
• Successfully build, train, and deploy AI/ML models.
• Design intelligent applications that address practical, real-world issues.
• Examine the ethical implications of AI usage in various sectors.

This live, instructor-led training in Latvia (available online or onsite) is tailored for entry-level IT professionals aiming to build a foundational understanding of intelligent applications and their diverse industry applications.

By the end of the session, participants will be capable of:

• Comprehending the evolution, core tenets, and influence of artificial intelligence.
• Identifying and deploying various machine learning algorithms.
• Managing and analyzing data effectively for AI purposes.
• Recognizing the practical benefits and constraints of AI in various sectors.
• Exploring the ethical questions and societal effects associated with AI technology.

This live, instructor-led training in Latvia, offered either online or on-site, targets data scientists, engineers, and AI professionals at intermediate to advanced levels who seek to deeply understand intelligent applications and utilize them to resolve complex, real-world issues.

Upon completion, participants will be equipped to:

• Implement and evaluate deep learning architectures.
• Deploy machine learning solutions at scale within distributed computing environments.
• Construct and deploy reinforcement learning models for strategic decision-making.
• Create advanced NLP systems capable of deep language understanding.
• Apply computer vision methods for comprehensive image and video analysis.
• Navigate the ethical dimensions of AI system development and deployment.

This live, instructor-led session conducted in Latvia (either online or on-site) caters to data scientists and software developers at beginner to intermediate levels who aim to master Milvus and apply it effectively in various AI contexts.

Upon completion, participants will be equipped to:

• Grasp the core architecture and capabilities of Milvus.
• Deploy vector databases within a variety of AI applications.
• Execute similarity searches that balance high precision with rapid performance.
• Leverage Milvus to address real-world AI complexities.

This live, instructor-led training in Latvia (available online or onsite) is designed for beginner and intermediate-level data scientists and software developers seeking to learn how to leverage Pinecone for various AI scenarios, from semantic search to personalization.

By the end of this training, participants will be able to:

• Understand the architecture and features of Pinecone.
• Implement vector databases in different AI applications.
• Perform similarity searches with high accuracy and speed.
• Apply Pinecone to real-world AI challenges.

Artificial Intelligence is swiftly emerging as the bedrock of contemporary product management, fundamentally altering approaches to identifying customer requirements, crafting solutions, and driving strategic decisions. Much like the industrial revolution brought by electricity, AI is redefining the lifecycle of product creation, validation, and evolution.

This live, instructor-led training—available both online and onsite—is designed for Product Owners, Scrum Masters, and Agile Coaches seeking to leverage AI to refine their product ownership capabilities and enhance decision-making processes.

Upon completion, participants will be equipped to:

• Grasp the core principles of Artificial Intelligence, distinguishing between conventional AI and Generative AI (GenAI).
• Utilize AI to bolster the seven key Product Owner stances, including the emerging Orchestrator role, as well as established positions such as Visionary, Experimenter, and Customer Representative.
• Develop proficiency in effective prompting, treating AI tools as specialized, intelligent collaborators.
• Enhance customer insights by generating AI-assisted personas for hypothesis testing and discovery, ensuring genuine customer engagement remains central.
• Formulate and articulate a compelling product vision using structured methodologies like the 3x3 Framework, utilizing AI to craft and visualize narratives.
• Employ Generative AI to expedite prototyping and hypothesis validation, including the creation of mock-ups and product concepts.
• Inform product decisions by leveraging AI to analyze, prioritize, and assess features based on value, feasibility, and user impact.
• Streamline routine operational tasks—such as email management, stakeholder coordination, and note-taking—via AI, allowing greater focus on strategic and creative endeavors.
• Facilitate discourse on AI ethics, bias, and data security to ensure responsible and sustainable implementation.
• Conceive and design high-value AI use cases aligned with specific product and organizational contexts.
• Experiment with approximately 20 distinct AI tools and models, ranging from productivity assistants and prototyping platforms to generative video, image, and coding applications.

This live, instructor-led training delivered in Latvia (via online or on-site methods) is designed for entry-level software developers and database administrators seeking to understand the fundamentals of vector databases, encompassing their architecture, functionality, and applicable scenarios.

Following the completion of this training, participants will be able to:

• Comprehend the definition and utility of vector databases.
• Identify the distinctions between vector and traditional databases.
• Acquire basic skills for creating and managing vector data.
• Conduct simple similarity searches.
• Appreciate the contribution of vector databases to AI applications.

This instructor-led, live training in Latvia (online or onsite) is aimed at intermediate-level to advanced-level data scientists and machine learning engineers who wish to master the use of vector databases for scalable, high-performance AI applications.

By the end of this training, participants will be able to:

• Understand the principles of vector databases.
• Learn techniques for creating and managing vector embeddings.
• Explore indexing strategies for high-dimensional data.
• Develop skills for performing efficient similarity searches.
• Apply vector database knowledge to machine learning projects.