Databricks Mosaic Agentic AI Training Course in Gurgaon
- Get mastery in building Agentic AI workflows for intelligent automation & to drive key businesses powered through agentic ai frameworks like Databricks Mosaic AI Agent Framework
- Experience blended learning through interactive offline and online sessions.
- Job Oriented Training Course
- Course Duration - 3 months
- Get Trained from Industry Experts
Train using realtime course materials using online portals & trainer experience to get a personalized teaching experience.
Trained through leading industry professionals to get realtime project exposure
Gain professionals insights through leading industry experts across domains
24/7 Q&A support designed to address training needs
Databricks Mosaic Agentic AI Course
Kickstart your carrer in AI with certification in Generative AI with Databricks Mosaic Agentic AI course, that will help in shaping the carrer to the current industry needs that need automation using intelligent workflows like Databricks Mosaic Agentic AI framework in every domain & sphere of the industry that will allow organizations to boost decision making & also thrive business growth with improved customer satisfaction.
- Self-paced videos & training materials with archived session recordings
- Success Aimers targets industry employers to gain visibility
- Industry-paced training with realtime scenarios using Databricks Mosaic Agentic AI for intelligent automation.
- Real-World industry scenarios with projects implementation support
- Live Virtual classes heading by top industry experts alogn with project implementation
- Q&A support sessions
- Job Interview preparation & use cases
Explain Databricks Mosaic Agentic AI Engineer?
AI Engineers automate business processes via Databricks Mosaic Agentic AI framework for intelligent automation that will allow organizations to boost decsion making & also thrive business growth with improved customer satisfaction. AI engineers build optimized workflows, deployment & maintain the AI development lifecycle to deliver high-précised solutions to the clients using Agentic AI frameworks/workflow builders.
Role of Databricks Mosaic Agentic AI Engineer?
Databricks Mosaic AI Engineers manages the end-to-end AI development lifecycle using Agentic AI frameworks and intelligent workflow strategies.
Responsibilities include:
- Agentic AI Tools Adoption & automation using patterns like RAG ,Tools ,Reflection & others
- Continuously exploring new AI based frameworks/tools to build optimized workflows
- Deploying AI model within cloud infrastructure securely & seamlessly.
- Develop & Design AI workflows that meets business needs with automation scripts
- Success Aimers helps aspiring AI professionals to build, deploy, manage these Agents effectively seamlessly & to scale as per business needs.
Who should opt for Databricks Mosaic AI Engineer course?
Gen AI course accelerates/boost carrier in AI & technology-driven organizations
- AI & Application Engineers – Develop Agentic AI workflows to automate business processes.
- Data Engineers – Implementing Data Pipelines using AI
- AI Developers – Automated workflows using Agentic AI frameworks/tools
- AI Leaders – Leading AI Governance & initiative within enterprise.
- Cloud & Infrastructure Engineers – Deploying AI developed applications seamlessly & effectively.
Prerequisites of Databricks Mosaic AI Engineer Course?
Prerequisites required for the Databricks Mosaic Agentic AI Engineer Certification Course
- Below is the prerequisite to become a successful AI Professional:
- High School Diploma or a undergraduate degree
- Python Programming language
- IT Foundational Knowledge along wit Data Science & Software Development skills
- Knowledge of Cloud Computing Platforms like AWS, AZURE & GCP will be an added advantage.
Kind of Job Placement/Offers after Databricks Mosaic AI Engineer Certification Course?
- Job Carrer Path in Databricks Mosaic Agentic AI Automation
- Databricks Mosaic AI Engineer – Develop & Deploy AI developed solutions/applications using agentic frameworks/tools
- Databricks Mosaic AI Engineer – Build context-aware, Enterprise ready AI systems.
- Databricks AI Automation Engineer – Design, Developed & build automated workflows to drive key business processes/decisions
- Cloud AI Engineer / AI Cloud Architect – Deploy AI Solutions seamlessly & effectively across enterprises with AI Governance strategy
- AI Model Deployment Engineer – Automate Model Deployment & AI Lifecycle management via MLOps pipeline & Databricks Mosaic Agentic AI Frameworks
| Training Options | Weekdays (Mon-Fri) | Weekends (Sat-Sun) | Fast Track |
|---|---|---|---|
| Duration of Course | 2 months | 3 months | 15 days |
| Hours / day | 1-2 hours | 2-3 hours | 5 hours |
| Mode of Training | Offline / Online | Offline / Online | Offline / Online |
Databricks Mosaic AI Engineer Certification Course Curriculum
Start your carrer in AI with certification in Databricks Mosaic Agentic AI Engineer course, that will help in shaping the carrer to the current industry needs that need automation using intelligent workflows like Azure Open AI, Azure AI Foundry, n8n, Microsoft AutoGen, LangGraph & others in every domain & sphere of the industry that will allow organizations to boost decision making & also thrive business growth with improved customer satisfaction.
Databricks Mosaic AI
Course Details - Databricks Mosaic AI
Introduction
- Introduction to AI Agents
- What are AI Agents?
- How an AI Agent Works ?
Databricks Fundamentals
- Introduction to Databricks: Data and AI Workloads
- Azure Databricks v/s Azure Synapse Analytics
- Microsoft Fabric and Azure Databricks: Match Made in Heaven!!
- Lab: Deploying a Databricks Workspace on Azure (Hands-On Lab)
Data Analytics using Apache Spark & SQL
- Understanding Apache Spark and Data Analytics using Azure Databricks
- What is Data Lake and Delta Lake?
- What is a Data Lakehouse?
- What is Apache Spark?
- Unity Catalog ( and MS Purview integration)
- Deploying a Databricks Workspace and a Compute Cluster (Hands-On Lab)
- Introduction to Delta Live Tables and Medallion Architecture
- Lab 4: Implementing the Medallion Architecture with Spark (Hands-On Lab)
Gen AI Primer and Fundamentals
- Introduction to GenAI Systems
- Introduction to AI Agents and Compound AI Systems
- Prompt Engineering: Introduction and Best-Practices
- Prompt Engineering Techniques: CoT, Zero-Shot, Few-Shot etc.
- Lab: Deploying a Foundational LLM and AI Playground (Hands-On Lab)
- Lab: Querying LLM with the Databricks SDK (Hands-On Lab)
- Lab: Querying LLM with the OpenAI SDK (Hands-On Lab)
- Lab: Working with the "ai_query" SQL Function (Hands-On Lab)
- Introduction to MLFlow and PyFunc for Custom AI Models and Engines
- Introduction to the Mosaic AI Framework in Databricks
- Lab: Registering a LLM Application using MLFlow (Hands-On Lab)
- Lab: Deploying LLM Application as Mosaic AI Endpoint (Hands-On Lab)
- Chat Completions API to call GPT engine via code (Hands-On Lab)
- What Are Vector Embeddings?
- Making Use of Embedding Engines via code (Hands-On Lab)
- What is RAG (Retrieval Augmented Generation) ?
RAG – Using your Enterprise Data
- Understanding RAG - Using your Enterprise Data
- Understanding Fine-Tuning
- Select Chunking Strategy based on model and retrieval evaluation
- Lab: Getting Comfortable with Vector Embeddings (Hands-On Lab)
- Lab: Preparing Data for RAG in Delta Table Format (Hands-On Lab)
- Understanding different types of vector indices in Databricks
- Understanding Searching and Ranking Algorithms
- Lab: Creating our Managed Delta Sync Vector Index (Hands-On Lab)
- Lab: Creating our RAG Custom Model Pipeline (Hands-On Lab)
- Lab: Deploying our RAG Application Endpoint (Hands-On Lab)
- Lab: Evaluate RAG app performance using MLflow (Hands-On Lab)
AI Agents using Langchain
- Introduction to LangChain
- Lab: Getting Started with LangChain (Hands-On Lab)
- Lab: SQL User Defined Function with LangChain (Hands-On Lab)
- Lab: Coding a Simple Multi-Agent Chain with LangChain (Hands-On Lab)
Machine Learning and Deep Learning using Azure Databricks
- Introduction to ML on Azure Databricks
- Understanding Basic ML Concepts
- Understanding Linear Algorithms
- Understanding non-Linear Algorithms
- Module 1: Model Training (Hands-On Lab)
- Module 2: MLFlow (Hands-On Lab)
- Module 3: Hyperparameter tuning using Hyperopt (Hands-On Lab)
- Module 4: Using AutoML (Hands-On Lab)
- Introduction to Deep Learning Models
- Module 5: Deep Learning (Hands-On Lab)
Generative AI and Azure Databricks
- Introduction to Generative AI on Azure Databricks
- Getting Started with GenAI on Azure Databricks (Hands-On Lab)
- Calling Foundational Models via Code (Hands-On Lab)
- Calling Open-Source Models from AI Foundry (Hands-On Lab)
- Creating a Basic Chatbot using GPT engine (Hands-On Lab)
- Building a RAG Chatbot (Hands-On Lab)
Agents with Azure Databricks
- Introduction to Agents and Compound AI Systems
- Intro to Building Agents with Azure Databricks
- Lab: Interacting With Agents through Playground (Hands-On Lab)
- Note: Intro to LangGraph
- Lab: Building Agent with external Weather API
- Lab: Building a Unity Catalog Hosted Function for Agent (Hands-On Lab)
- Lab: Deploying our Agent (Hands-On Lab)
- Title - Open API 3.0
- What is Open API 3.0 ?
- Understanding the Workflow for Yahoo Finance
- Demo: Create Rapid API Key
- Demo: Create a Connection to Rapid API in Azure
- Demo: Create a Stocks Agent and Attach Open API 3.0
- Demo: Test the Stocks Agent in Playground
- Demo: Install the Libraries and Setup Environment
- Authenticate/Initialize/ Load Open API Spec
- Get Connection / Create Auth Object / Create Open API Tool
- Create Agent / Thread / Conversation Loop
- Execute the Program and run Queries
Multi-Agent Systems with Semantic Kernel
- Introduction to Semantic Kernel SDK
- Introduction to Semantic Kernel SDK Agent Framework
- Lab 1 : Getting started with creating an Azure ChatCompletionAgent (Hands-On Lab)
- Lab 2 : Part 1 : Azure AI Agent with SDK – Deploying Infrastructure (Hands-On)
- Lab 2 : Part 2 : Deploying the Azure AI Agent Code (Hands-On Lab)
- Lab 3 : Azure AI Agent with Out-of-the-Box Plugins (Hands-On Lab)
- Lab 4 : Implementing an Agent Chat Framework (Hands-On Lab)
Agentic Systems: Deploy and Evaluate RAG Apps with Databricks AI
- Understand Unity Catalog from GenAI Perspective
- Unity Catalog Demo
- Build tools and save them as Unity Catalog functions
- Create and deploy your first agent with LangChain
- Evaluate your agent and build an evaluation loop to ensure new versions perform better on your dataset
- Prepare documents and build a knowledge base with Vector Search
- Deploy a real-time Q&A chatbot using RAG
- Evaluate performance with Mosaic AI Agent Evaluation and MLflow 3.0
- Scan and extract information using Databricks' built-in ai_parse_document function
- Monitor live agents and review production behavior
- Deploy a chatbot front-end with the Lakehouse Application
Build GenAI Apps Faster with New Foundation Model Capabilities
- Access, Govern, and Monitor any Foundation Models with Databricks Model Serving
- Challenges with Productionizing Foundation Models
- Databricks Model Serving: Unified Serving for any Foundation Model
- Access any Foundation Model
- Query Models via a Unified Interface
- Govern and Monitor All Models
- Securely Customize Models with Your Private Data
Creating High Quality RAG Applications with Databricks
- Creating High Quality RAG Applications with Databricks
- A New Suite of Tools to Get Generative AI Applications to Production
- Serving Real-Time Data for Your RAG App
- Comparing, Tuning, and Serving Foundation Model
- Ensuring Quality and Safety in Production
Best Practices for LLM Evaluation of RAG Applications
- Challenges with auto-evaluation in practice
- Applying effective auto-evaluation for RAG applications
- Alignment with Human Grading
- Accuracy through Examples
Build , Deploy and Monitor Multi-Agents Applications using Databricks Mosaic Agentic AI framework . These tools helps to build a multi-agentic using Databricks Foundry Models, Databricks Agent Service, Foundry Observability & others then visualize them on customized dashboards in form of graphs & bar charts. We can also bring automation using these Agentic AI platform that will help to automate business processes autonomous AI Agents .Â
Hours of content
Live Sessions
Software Tools
After completion of this training program you will be able to launch your carrer in the world of AI being certified as Databricks Mosaic Agentic AI Certified Professional.
With the AI Certification in-hand you can boost your profile on Linked, Meta, Twitter & other platform to boost your visibility
- Get your certificate upon successful completion of the course.
- Certificates for each course
- Gen AI
- Vector DB
- Agentic AI
- Lang Chain
- Lang Graph
- Azure Open AI
- Auto GEN
- Model Context Protocol (MCP)
- AI Governance
- Autonomous Agents
- Databricks Mosaic AI
- Multimodal RAG's
- Azure AI Foundry
45% - 100%
Designed to provide guidance on current interview practices, personality development, soft skills enhancement, and HR-related questions
Receive expert assistance from our placement team to craft your resume and optimize your Job Profile. Learn effective strategies to capture the attention of HR professionals and maximize your chances of getting shortlisted.
Engage in mock interview sessions led by our industry experts to receive continuous, detailed feedback along with a customized improvement plan. Our dedicated support will help refine your skills until your desired job in the industry.
Join interactive sessions with industry professionals to understand the key skills companies seek. Practice solving interview question worksheets designed to improve your readiness and boost your chances of success in interviews
Build meaningful relationships with key decision-makers and open doors to exciting job prospects in Product and Service based partner
Your path to job placement starts immediately after you finish the course with guaranteed interview calls
Why should you choose to pursue a Databricks Mosaic Agentic AI course with Success Aimers?
Success Aimers teaching strategy follow a methodology where in we believe in realtime job scenarios that covers industry use-cases & this will help in building the carrer in the field of AI & also delivers training with help of leading industry experts that helps students to confidently answers questions confidently & excel projects as well while working in a real-world
What is the time frame to become competent as a Databricks Mosaic Agentic AI engineer?
To become a successful Databricks Mosaic Agentic AI Engineer required 1-2 years of consistent learning with dedicated 3-4 hours on daily basis.
But with Success Aimers with the help of leading industry experts & specialized trainers you able to achieve that degree of mastery in 6 months or one year or so and it’s because our curriculum & labs we had formed with hands-on projects.
Will skipping a session prevent me from completing the course?
Missing a live session doesn’t impact your training because we have the live recorded session that’s students can refer later.
What industries lead in Databricks Mosaic Agentic AI implementation?
- Manufacturing
- Financial Services
- Healthcare
- E-commerce
- Telecommunications
- BFSI (Banking, Finance & Insurance)
- Travel Industry
- Â
Does Success Aimers offer corporate training solutions?
At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.
How is the Success Aimers Databricks Mosaic Agentic AI Certification Course reviewed by learners?
Our Databricks Mosaic Agentic AI Engineer Course features a well-designed curriculum frameworks focused on delivering training based on industry needs & aligned on ever-changing evolving needs of today’s workforce due to AI.
Also our training curriculum has been reviewed by alumni & praises the thorough content & real along practical use-cases that we covered during the training. Our program helps working professionals to upgrade their skills & help them grow further in their roles…
Can I attend a demo session before I enroll?
Yes, we offer one-to-one discussion before the training and also schedule one demo session to have a gist of trainer teaching style & also the students have questions around training programme placements & job growth after training completion.
What batch size do you consider for the course?
On an average we keep 5-10 students in a batch to have a interactive session & this way trainer can focus on each individual instead of having a large group.
Do you offer learning content as part of the program?
Students are provided with training content wherein the trainer share the Code Snippets, PPT Materials along with recordings of all the batches
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