Ollama is a platform for running large language and multimodal models locally, supporting governance and responsible AI practices.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to implement fairness, transparency, and accountability in Ollama-powered applications.
By the end of this training, participants will be able to:
Apply responsible AI principles in Ollama deployments.
Implement content filtering and bias mitigation strategies.
Design governance workflows for AI alignment and auditability.
Establish monitoring and reporting frameworks for compliance.
Format of the Course
Interactive lecture and discussion.
Hands-on governance workflow design labs.
Case studies and compliance-focused exercises.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mistral AI is a powerful family of open-source and enterprise-ready AI models for language, multimodal, and agentic applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to build, deploy, and manage AI agents using Mistral’s Medium 3, Le Chat Enterprise, and Devstral models.
By the end of this training, participants will be able to:
Understand the architecture and capabilities of Mistral Medium 3, Le Chat Enterprise, and Devstral.
Design and implement AI agents leveraging Mistral models for enterprise and developer use cases.
Integrate coding systems, connectors, and enterprise data into agent workflows.
Optimize performance, cost, and compliance for Mistral-powered agents.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ollama is a platform that allows running large language and multimodal models locally while supporting secure deployment strategies.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to deploy Ollama with strong data privacy and regulatory compliance measures.
By the end of this training, participants will be able to:
Deploy Ollama securely in containerized and on-prem environments.
Apply differential privacy techniques to safeguard sensitive data.
Implement secure logging, monitoring, and auditing practices.
Enforce data access control aligned with compliance requirements.
Format of the Course
Interactive lecture and discussion.
Hands-on labs with secure deployment patterns.
Compliance-focused case studies and practical exercises.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mistral AI is an open AI platform that enables teams to build and integrate conversational assistants into enterprise and customer-facing workflows.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level product managers, full-stack developers, and integration engineers who wish to design, integrate, and productize conversational assistants using Mistral connectors and integrations.
By the end of this training, participants will be able to:
Integrate Mistral conversational models with enterprise and SaaS connectors.
Implement retrieval-augmented generation (RAG) for grounded responses.
Design UX patterns for internal and external chat assistants.
Deploy assistants into product workflows for real-world use cases.
Format of the Course
Interactive lecture and discussion.
Hands-on integration exercises.
Live-lab development of conversational assistants.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ollama is a platform that enables running large language and multimodal models locally.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to master prompt engineering techniques to optimize Ollama outputs.
By the end of this training, participants will be able to:
Design effective prompts for diverse use cases.
Apply techniques such as priming and chain-of-thought structuring.
Implement prompt templates and context management strategies.
Build multi-stage prompting pipelines for complex workflows.
Format of the Course
Interactive lecture and discussion.
Hands-on exercises with prompt design.
Practical implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.
By the end of this training, participants will be able to:
Design legal-specific LangGraph workflows that preserve auditability and compliance.
Integrate legal ontologies and document standards into graph state and processing.
Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mistral AI is an open and enterprise-ready AI platform that provides features for secure, compliant, and responsible AI deployment.
This instructor-led, live training (online or onsite) is aimed at intermediate-level compliance leads, security architects, and legal/ops stakeholders who wish to implement responsible AI practices with Mistral by leveraging privacy, data residency, and enterprise control mechanisms.
By the end of this training, participants will be able to:
Implement privacy-preserving techniques in Mistral deployments.
Apply data residency strategies to meet regulatory requirements.
Set up enterprise-grade controls such as RBAC, SSO, and audit logs.
Evaluate vendor and deployment options for compliance alignment.
Format of the Course
Interactive lecture and discussion.
Compliance-focused case studies and exercises.
Hands-on implementation of enterprise AI controls.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ollama is a platform for running large language and multimodal models locally and at scale.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level engineers who wish to scale Ollama deployments for multi-user, high-throughput, and cost-efficient environments.
By the end of this training, participants will be able to:
Configure Ollama for multi-user and distributed workloads.
Optimize GPU and CPU resource allocation.
Implement autoscaling, batching, and latency reduction strategies.
Monitor and optimize infrastructure for performance and cost efficiency.
Format of the Course
Interactive lecture and discussion.
Hands-on deployment and scaling labs.
Practical optimization exercises in live environments.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.
By the end of this training, participants will be able to:
Deploy and optimize CV and NLP models using CANN and AscendCL.
Use CANN tools to convert models and integrate them into live pipelines.
Optimize inference performance for tasks like detection, classification, and sentiment analysis.
Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.
Format of the Course
Interactive lecture and demonstration.
Hands-on lab with model deployment and performance profiling.
Live pipeline design using real CV and NLP use cases.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.
By the end of this training, participants will be able to:
Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
Integrate financial data standards and ontologies into graph state and tooling.
Implement reliability, safety, and human-in-the-loop controls for critical processes.
Deploy, monitor, and optimize LangGraph systems for performance, cost, and SLAs.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Ollama is a platform that enables running and fine-tuning large language and multimodal models locally.
This instructor-led, live training (online or onsite) is aimed at advanced-level ML engineers, AI researchers, and product developers who wish to build and deploy multimodal applications with Ollama.
By the end of this training, participants will be able to:
Set up and run multimodal models with Ollama.
Integrate text, image, and audio inputs for real-world applications.
Build document understanding and visual QA systems.
Develop multimodal agents capable of reasoning across modalities.
Format of the Course
Interactive lecture and discussion.
Hands-on practice with real multimodal datasets.
Live-lab implementation of multimodal pipelines using Ollama.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mistral models are open-source AI technologies that now extend into multimodal workflows, supporting both language and vision tasks for enterprise and research applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level ML researchers, applied engineers, and product teams who wish to build multimodal applications with Mistral models, including OCR and document understanding pipelines.
By the end of this training, participants will be able to:
Set up and configure Mistral models for multimodal tasks.
Implement OCR workflows and integrate them with NLP pipelines.
Design document understanding applications for enterprise use cases.
Develop vision-text search and assistive UI functionalities.
Format of the Course
Interactive lecture and discussion.
Hands-on coding exercises.
Live-lab implementation of multimodal pipelines.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply NLP techniques using Python in Google Colab.
By the end of this training, participants will be able to:
Understand the core concepts of natural language processing.
Preprocess and clean text data for NLP tasks.
Perform sentiment analysis using NLTK and SpaCy libraries.
Work with text data using Google Colab for scalable and collaborative development.
Advanced Ollama Model Debugging & Evaluation is an in-depth course focused on diagnosing, testing, and measuring model behavior when running local or private Ollama deployments.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to ensure reliability, fidelity, and operational readiness of Ollama-based models in production.
By the end of this training, participants will be able to:
Perform systematic debugging of Ollama-hosted models and reproduce failure modes reliably.
Design and execute robust evaluation pipelines with quantitative and qualitative metrics.
Implement observability (logs, traces, metrics) to monitor model health and drift.
Automate testing, validation, and regression checks integrated into CI/CD pipelines.
Format of the Course
Interactive lecture and discussion.
Hands-on labs and debugging exercises using Ollama deployments.
Case studies, group troubleshooting sessions, and automation workshops.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level researchers, analysts, and knowledge workers who want to leverage Claude AI for processing large amounts of data, synthesizing information, and automating research tasks.
By the end of this training, participants will be able to:
Use Claude AI for literature reviews and research synthesis.
Summarize reports, research papers, and long-form documents.
Extract key insights and trends from structured and unstructured data.
Integrate Claude AI into research and knowledge management workflows.
Devstral and Mistral models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training (online or onsite) is aimed at intermediate–level to advanced–level ML engineers, platform teams, and research engineers who wish to self-host, fine-tune, and govern Mistral and Devstral models in production environments.
By the end of this training, participants will be able to:
Set up and configure self-hosted environments for Mistral and Devstral models.
Apply fine-tuning techniques for domain-specific performance.
Implement versioning, monitoring, and lifecycle governance.
Ensure security, compliance, and responsible usage of open-source models.
Format of the Course
Interactive lecture and discussion.
Hands-on exercises in self-hosting and fine-tuning.
Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at advanced-level data engineers and DevOps professionals who wish to leverage LangChain's capabilities by integrating it with various cloud services.
By the end of this training, participants will be able to:
Integrate LangChain with major cloud platforms such as AWS, Azure, and Google Cloud.
Utilize cloud-based APIs and services to enhance LangChain-powered applications.
Scale and deploy conversational agents to the cloud for real-time interaction.
Implement monitoring and security best practices in cloud environments.
LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are crucial for compliance, interoperability, and building decision-support systems that align with medical workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.
By the end of this training, participants will be able to:
Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
Apply best practices for reliability, traceability, and explainability in sensitive environments.
Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Format of the Course
Interactive lecture and discussion.
Hands-on exercises with real-world case studies.
Implementation practice in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI platform engineers, DevOps for AI, and ML architects who wish to optimize, debug, monitor, and operate production-grade LangGraph systems.
By the end of this training, participants will be able to:
Design and optimize complex LangGraph topologies for speed, cost, and scalability.
Engineer reliability with retries, timeouts, idempotency, and checkpoint-based recovery.
Debug and trace graph executions, inspect state, and systematically reproduce production issues.
Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level professionals who wish to deepen their understanding of conversational agents and apply LangChain to real-world use cases.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its application in building conversational agents.
Develop and deploy conversational agents using LangChain.
Integrate conversational agents with APIs and external services.
Apply Natural Language Processing (NLP) techniques to improve the performance of conversational agents.
This instructor-led, live training in 東京 (online or onsite) is aimed at beginner-level to intermediate-level content creators, writers, and marketers who want to leverage Claude AI for content generation, editing, and summarization to enhance blogs, reports, and marketing materials.
By the end of this training, participants will be able to:
Use Claude AI for content brainstorming and idea expansion.
Enhance writing with AI-assisted editing and proofreading.
Generate summaries for long-form content and reports.
Automate marketing copy creation for different platforms.
This instructor-led, live training in 東京 (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.
By the end of this training, participants will be able to:
Set up an efficient environment for fine-tuning AI models on Ollama.
Prepare datasets for supervised fine-tuning and reinforcement learning.
Optimize AI models for performance, accuracy, and efficiency.
Deploy customized models in production environments.
Evaluate model improvements and ensure robustness.
Mistral is a high-performance family of large language models optimized for cost-effective production deployment at scale.
This instructor-led, live training (online or onsite) is aimed at advanced-level infrastructure engineers, cloud architects, and MLOps leads who wish to design, deploy, and optimize Mistral-based architectures for maximum throughput and minimum cost.
By the end of this training, participants will be able to:
Implement scalable deployment patterns for Mistral Medium 3.
Apply batching, quantization, and efficient serving strategies.
Optimize inference costs while maintaining performance.
Design production-ready serving topologies for enterprise workloads.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at developers and IT professionals of all skill levels who wish to automate tasks and processes using AI without writing extensive code.
By the end of this training, participants will be able to:
Design and implement complex workflows using n8n's visual programming interface.
Integrate AI capabilities into workflows using LangChain.
Build custom chatbots and virtual assistants for various use cases.
Perform advanced data analysis and processing with AI agents.
This instructor-led, live training in 東京 (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.
By the end of this training, participants will be able to:
Deploy and configure Ollama for private AI processing.
Integrate AI models into secure enterprise workflows.
Optimize AI performance while maintaining data privacy.
Automate business processes with on-premise AI capabilities.
Ensure compliance with enterprise security and governance policies.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level software developers and AI engineers who wish to integrate Claude AI into their applications, build AI-powered chatbots, and enhance software functionality with AI-driven automation.
By the end of this training, participants will be able to:
Use the Claude AI API to integrate AI into applications.
Develop AI-driven chatbots and virtual assistants.
Enhance applications with AI-powered automation and NLP.
Optimize and fine-tune Claude AI models for different use cases.
LangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.
This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
By the end of this training, participants will be able to:
Design graph-structured content and email workflows with conditional logic.
Integrate LLMs, APIs, and data sources for automated personalization.
Manage state, memory, and context across multi-step campaigns.
Evaluate, monitor, and optimize workflow performance and delivery outcomes.
Format of the Course
Interactive lectures and group discussions.
Hands-on labs implementing email workflows and content pipelines.
Scenario-based exercises on personalization, segmentation, and branching logic.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Le Chat Enterprise is a private ChatOps solution that provides secure, customizable, and governed conversational AI capabilities for organizations, with support for RBAC, SSO, connectors, and enterprise app integrations.
This instructor-led, live training (online or onsite) is aimed at intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who wish to deploy, configure, and govern Le Chat Enterprise in enterprise environments.
By the end of this training, participants will be able to:
Set up and configure Le Chat Enterprise for secure deployments.
Enable RBAC, SSO, and compliance-driven controls.
Integrate Le Chat with enterprise applications and data stores.
Design and implement governance and admin playbooks for ChatOps.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Devstral is an open-source framework designed for building and running coding agents that can interact with codebases, developer tools, and APIs to enhance engineering productivity.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimize coding agents using Devstral.
By the end of this training, participants will be able to:
Set up and configure Devstral for coding agent development.
Design agentic workflows for codebase exploration and modification.
Integrate coding agents with developer tools and APIs.
Implement best practices for secure and efficient agent deployment.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at beginner-level professionals who wish to integrate Claude AI into their daily workflows to improve efficiency and automation.
By the end of this training, participants will be able to:
Use Claude AI for automating repetitive tasks and streamlining workflows.
Enhance personal and team productivity using AI-powered automation.
Integrate Claude AI with existing business tools and platforms.
Optimize AI-driven decision-making and task management.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
Set up and deploy LLMs using Ollama.
Optimize AI models for performance and efficiency.
Leverage GPU acceleration for improved inference speeds.
Integrate Ollama into workflows and applications.
Monitor and maintain AI model performance over time.
This instructor-led, live training in 東京 (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.
By the end of this training, participants will be able to:
Grasp the fundamental principles of LangChain.
Set up and configure the LangChain environment.
Understand the architecture and how LangChain interacts with large language models (LLMs).
LangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.
By the end of this training, participants will be able to:
Design graph-based workflows that coordinate LLM agents, tools, and memory.
Implement conditional routing, retries, and fallbacks for robust execution.
Integrate retrieval, APIs, and structured outputs into agent loops.
Evaluate, monitor, and harden agent behavior for reliability and safety.
Format of the Course
Interactive lecture and facilitated discussion.
Guided labs and code walkthroughs in a sandbox environment.
Scenario-based design exercises and peer reviews.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.
By the end of this training, participants will be able to:
Establish a development environment with LLMs and essential tools.
Expertly perform NLU and NLI tasks with LLMs.
Extract, infer, and utilize knowledge graphs effectively.
Generate and manage dialogues using LLMs for conversational applications.
Evaluate content quality and diversity generated by LLMs and generative AI.
Apply ethical principles, ensuring fairness and responsible use of LLMs.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.
By the end of this training, participants will be able to:
Understand the fundamentals of fine-tuning for NLP tasks.
Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
Optimize hyperparameters for improved model performance.
Evaluate and deploy fine-tuned models in real-world scenarios.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who wish to understand the inner workings of GPT models, explore the capabilities of GPT-3 and GPT-4, and learn how to leverage these models for their NLP tasks.
By the end of this training, participants will be able to:
Understand the key concepts and principles behind Generative Pre-trained Transformers.
Comprehend the architecture and training process of GPT models.
Utilize GPT-3 for tasks such as text generation, completion, and translation.
Explore the latest advancements in GPT-4 and its potential applications.
Apply GPT models to their own NLP projects and tasks.
This instructor-led, live training in 東京 (online or onsite) is aimed at beginner-level business professionals, customer support teams, and tech enthusiasts who wish to understand the fundamentals of Claude AI and leverage it for business applications.
By the end of this training, participants will be able to:
Understand Claude AI’s capabilities and use cases.
Set up and interact with Claude AI effectively.
Automate business workflows with conversational AI.
Enhance customer engagement and support using AI-driven solutions.
This instructor-led, live training in 東京 (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.
By the end of this training, participants will be able to:
Understand the fundamentals of LangChain and its components.
Integrate LangChain with large language models (LLMs) like GPT-4.
Build modular AI applications using LangChain.
Troubleshoot common issues in LangChain applications.
LangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
Explain core LangGraph concepts (nodes, edges, state) and when to use them.
Build prompt chains that branch, call tools, and maintain memory.
Integrate retrieval and external APIs into graph workflows.
Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
Interactive lecture and facilitated discussion.
Guided labs and code walkthroughs in a sandbox environment.
Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Mistral Medium 3 is a high-performance, multimodal large language model designed for production-grade deployment across enterprise environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI/ML engineers, platform architects, and MLOps teams who wish to deploy, optimize, and secure Mistral Medium 3 for enterprise use cases.
By the end of this training, participants will be able to:
Deploy Mistral Medium 3 using API and self-hosted options.
Optimize inference performance and costs.
Implement multimodal use cases with Mistral Medium 3.
Apply security and compliance best practices for enterprise environments.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
This instructor-led, live training in 東京 (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.
By the end of this training, participants will be able to:
Understand the fundamentals of Ollama and its capabilities.
Set up Ollama for running local AI models.
Deploy and interact with LLMs using Ollama.
Optimize performance and resource usage for AI workloads.
Explore use cases for local AI deployment in various industries.
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
This course is aimed at developers and data scientists who wish to understand and implement artificial intelligence in their applications. Special focus will be given to data analysis, distributed artificial intelligence, and natural language processing.
ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions.
In this instructor-led, live training, participants will learn how to build chatbots in Python.
By the end of this training, participants will be able to:
Understand the fundamentals of building chatbots
Build, test, deploy, and troubleshoot various chatbots using Python
Audience
Developers
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training in 東京, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
Design and code DL for NLP using Python libraries.
Create Python code that reads a substantially huge collection of pictures and generates keywords.
Create Python Code that generates captions from the detected keywords.
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.
By the end of this training, participants will be able to:
Install and configure spaCy.
Understand spaCy's approach to Natural Language Processing (NLP).
Extract patterns and obtain business insights from large-scale data sources.
Integrate the spaCy library with existing web and legacy applications.
Deploy spaCy to live production environments to predict human behavior.
Use spaCy to pre-process text for Deep Learning
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
To learn more about spaCy, please visit: https://spacy.io/
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
Use a command-line tool that summarizes text.
Design and create Text Summarization code using Python libraries.
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, embedding terms, building graphs and logging
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お客様の声 (4)
prompts engineering part
Michal - GE HealthCare
コース - Generative AI with Large Language Models (LLMs)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
コース - Artificial Intelligence (AI) Overview
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
コース - Natural Language Processing with TensorFlow
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
コース - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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