Rajagopal Senthil

about 2 months ago

https://t.co/fUTDNInyYb

3 months ago

Exploring Databricks Lakeflow SDP & AUTO CDC INTO for implementing SCD Type 2 with full history tracking. Modern incremental CDC pipelines without complex MERGE logic. #Databricks #Lakeflow #CDC #DataEngineering #data https://t.co/NJiaNhzyrF

Accelerating enterprise innovation through AI, data, and cloud-native engineering. Engineering what's next. 🌐 | https://t.co/vR3Squ1SB8

3 months ago

Databricks Delta Sharing enables secure cross-org data collaboration across clouds & BI tools. Important: consumers get READ-ONLY access, no DML/DDL operations. #Databricks #DeltaSharing #data https://t.co/Er7Fi2pEr6

sendakil retweeted

Dhanian 🗯️

@e_opore

3 months ago

AWS IDENTITY AND ACCESS MANAGEMENT (IAM) INTRODUCTION AWS Identity and Access Management (IAM) is a security service that helps you securely control access to AWS resources. IAM allows you to define who can access resources, what actions they can perform, and under which conditions access is granted. WHAT IS IAM? → IAM is a global AWS service used for authentication and authorization → Helps manage users, permissions, and secure access to AWS services → Enables fine-grained access control across the AWS environment CORE COMPONENTS OF IAM IAM USERS → Represents a person or application needing AWS access → Each user can have unique credentials → Supports passwords and access keys IAM GROUPS → Collection of IAM users → Simplifies permission management → Permissions assigned to a group apply to all users inside it IAM ROLES → Temporary access identities with specific permissions → Used by AWS services, applications, and external users → No permanent credentials attached IAM POLICIES → JSON documents defining permissions → Specify what actions are allowed or denied → Can be attached to users, groups, or roles HOW IAM WORKS → User/Application sends request to AWS → AWS authenticates identity credentials → IAM policies are evaluated → Access is either Allowed or Denied → Requested AWS resource responds accordingly TYPES OF IAM POLICIES MANAGED POLICIES → Predefined or reusable policies → Can be attached to multiple identities INLINE POLICIES → Directly embedded into a single user, group, or role → Tight one-to-one relationship AWS MANAGED POLICIES → Created and maintained by AWS → Covers common use cases CUSTOMER MANAGED POLICIES → Created and managed by the organization → Provides custom access control IAM SECURITY FEATURES MULTI-FACTOR AUTHENTICATION (MFA) → Adds extra security layer beyond passwords → Requires a second verification factor PASSWORD POLICIES → Enforces strong password requirements → Controls expiration and complexity rules ACCESS ANALYZER → Identifies resources shared externally → Helps maintain least privilege access IAM ACCESS KEYS → Used for programmatic access via CLI or SDKs → Should be rotated regularly for security BEST PRACTICES FOR IAM → Follow the Principle of Least Privilege → Enable MFA for all users → Avoid using the root account for daily tasks → Rotate credentials regularly → Use IAM Roles instead of sharing access keys → Audit permissions frequently REAL-WORLD IAM ARCHITECTURE FLOW → Developer → IAM User → IAM Group → Policies → AWS Services → EC2 Instance → IAM Role → Temporary Credentials → S3 Access → Application → STS Token → Limited-Time Permissions COMMON IAM USE CASES DEVELOPER ACCESS → Grant developers controlled access to AWS resources APPLICATION AUTHENTICATION → Allow applications to securely interact with AWS services CROSS-ACCOUNT ACCESS → Share access between AWS accounts securely using IAM Roles TEMPORARY ACCESS MANAGEMENT → Provide short-term credentials for secure operations WHY IAM IS IMPORTANT → Protects AWS resources from unauthorized access → Simplifies permission management → Enhances cloud security posture → Supports compliance and governance requirements TIP AWS IAM is a foundational security service that enables secure and scalable access management across AWS environments. By properly configuring users, groups, roles, and policies, organizations can maintain strong security while ensuring efficient resource access. Grab the AWS EBOOK https://t.co/G3pdEOq4DK

e_opore's tweet photo. AWS IDENTITY AND ACCESS MANAGEMENT (IAM)

INTRODUCTION
AWS Identity and Access Management (IAM) is a security service that helps you securely control access to AWS resources. IAM allows you to define who can access resources, what actions they can perform, and under which conditions access is granted.

WHAT IS IAM?

→ IAM is a global AWS service used for authentication and authorization
→ Helps manage users, permissions, and secure access to AWS services
→ Enables fine-grained access control across the AWS environment

CORE COMPONENTS OF IAM

IAM USERS
→ Represents a person or application needing AWS access
→ Each user can have unique credentials
→ Supports passwords and access keys

IAM GROUPS
→ Collection of IAM users
→ Simplifies permission management
→ Permissions assigned to a group apply to all users inside it

IAM ROLES
→ Temporary access identities with specific permissions
→ Used by AWS services, applications, and external users
→ No permanent credentials attached

IAM POLICIES
→ JSON documents defining permissions
→ Specify what actions are allowed or denied
→ Can be attached to users, groups, or roles

HOW IAM WORKS

→ User/Application sends request to AWS
→ AWS authenticates identity credentials
→ IAM policies are evaluated
→ Access is either Allowed or Denied
→ Requested AWS resource responds accordingly

TYPES OF IAM POLICIES

MANAGED POLICIES
→ Predefined or reusable policies
→ Can be attached to multiple identities

INLINE POLICIES
→ Directly embedded into a single user, group, or role
→ Tight one-to-one relationship

AWS MANAGED POLICIES
→ Created and maintained by AWS
→ Covers common use cases

CUSTOMER MANAGED POLICIES
→ Created and managed by the organization
→ Provides custom access control

IAM SECURITY FEATURES

MULTI-FACTOR AUTHENTICATION (MFA)
→ Adds extra security layer beyond passwords
→ Requires a second verification factor

PASSWORD POLICIES
→ Enforces strong password requirements
→ Controls expiration and complexity rules

ACCESS ANALYZER
→ Identifies resources shared externally
→ Helps maintain least privilege access

IAM ACCESS KEYS
→ Used for programmatic access via CLI or SDKs
→ Should be rotated regularly for security

BEST PRACTICES FOR IAM

→ Follow the Principle of Least Privilege
→ Enable MFA for all users
→ Avoid using the root account for daily tasks
→ Rotate credentials regularly
→ Use IAM Roles instead of sharing access keys
→ Audit permissions frequently

REAL-WORLD IAM ARCHITECTURE FLOW

→ Developer → IAM User → IAM Group → Policies → AWS Services
→ EC2 Instance → IAM Role → Temporary Credentials → S3 Access
→ Application → STS Token → Limited-Time Permissions

COMMON IAM USE CASES

DEVELOPER ACCESS
→ Grant developers controlled access to AWS resources

APPLICATION AUTHENTICATION
→ Allow applications to securely interact with AWS services

CROSS-ACCOUNT ACCESS
→ Share access between AWS accounts securely using IAM Roles

TEMPORARY ACCESS MANAGEMENT
→ Provide short-term credentials for secure operations

WHY IAM IS IMPORTANT

→ Protects AWS resources from unauthorized access
→ Simplifies permission management
→ Enhances cloud security posture
→ Supports compliance and governance requirements

TIP
AWS IAM is a foundational security service that enables secure and scalable access management across AWS environments. By properly configuring users, groups, roles, and policies, organizations can maintain strong security while ensuring efficient resource access.

Grab the AWS EBOOK
https://t.co/G3pdEOq4DK

100

Who to follow

ASCENTT

@ascentt

Parent, Foodie, Travel, Soccer BI, Analytics & Big Data sales, Cloud & software development

sendakil retweeted

Darshil | Data Engineer👨🏻‍🔧

@parmardarshil07

3 months ago

2000: Data modeling = ER diagrams + 3NF 2010: + Star schemas + Dimensions 2015: + Big Data + Schema-on-read + NoSQL 2020: + Data Vault + Modern Data Stack 2026: + Iceberg + OBT + Data Contracts + LLM-aware schemas + ... → Today's "design a schema" interview Most candidates still jump to a star schema. The bar didn't just rise. It multiplied. Here's what "data modelling" actually means in 2026 👇 🟦 𝗧𝗵𝗲 𝟯 𝗹𝗮𝘆𝗲𝗿𝘀 → Conceptual (entities + relationships) → Logical (tables, columns, keys, normalization) → Physical (indexes, partitions, storage strategy) 🟩 𝗧𝗵𝗲 𝟱 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺𝘀 → 3NF / OLTP (transactional databases) → Dimensional / Kimball (analytics warehouses) → Data Vault (audit-heavy enterprise) → One Big Table (modern columnar) → Document / NoSQL (flexible schemas) 🟧 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘀𝗶𝗴𝗻𝗮𝗹 When the interviewer says "design a schema": → Junior: jumps to a star schema → Senior: asks WHICH paradigm fits the use case That ONE question separates levels. Most candidates lose senior offers not because they can't draw a star schema. They lose because they don't realize how much "data modeling" has expanded. I'm dropping a complete Data Modeling Masterclass on May 16. Inside: → All 5 paradigms compared (with code examples) → Worked example with Uber → Physical modeling (partitioning, indexing, costs) → Anti-patterns that fail interviews → Leveled expectations: junior / senior / staff+ FREE. Stay tuned. Data engineers, what's the first paradigm you think of when you hear "data modelling"? 👇

parmardarshil07's tweet photo. 2000: Data modeling = ER diagrams + 3NF
2010: + Star schemas + Dimensions
2015: + Big Data + Schema-on-read + NoSQL
2020: + Data Vault + Modern Data Stack
2026: + Iceberg + OBT + Data Contracts + LLM-aware schemas + ...

→ Today's "design a schema" interview

Most candidates still jump to a star schema.
The bar didn't just rise. It multiplied.

Here's what "data modelling" actually means in 2026 👇
🟦 𝗧𝗵𝗲 𝟯 𝗹𝗮𝘆𝗲𝗿𝘀
→ Conceptual (entities + relationships)
→ Logical (tables, columns, keys, normalization)
→ Physical (indexes, partitions, storage strategy)

🟩 𝗧𝗵𝗲 𝟱 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺𝘀
→ 3NF / OLTP (transactional databases)
→ Dimensional / Kimball (analytics warehouses)
→ Data Vault (audit-heavy enterprise)
→ One Big Table (modern columnar)
→ Document / NoSQL (flexible schemas)

🟧 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘀𝗶𝗴𝗻𝗮𝗹
When the interviewer says "design a schema":
→ Junior: jumps to a star schema
→ Senior: asks WHICH paradigm fits the use case

That ONE question separates levels.

Most candidates lose senior offers not because they can't draw a star schema.

They lose because they don't realize how much "data modeling" has expanded.

I'm dropping a complete Data Modeling Masterclass on May 16.
Inside:
→ All 5 paradigms compared (with code examples)
→ Worked example with Uber
→ Physical modeling (partitioning, indexing, costs)
→ Anti-patterns that fail interviews
→ Leveled expectations: junior / senior / staff+

FREE.

Stay tuned.

Data engineers, what's the first paradigm you think of when you hear "data modelling"? 👇

224

198

sendakil retweeted

Matt Dancho (Business Science)

@mdancho84

4 months ago

8 Types of LLMs used in AI Agents (Must know for Gen AI Data Scientists & AI Engineers): Here's what they are and what they do: 🧵

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Shabnam Parveen

@shabnam_774

4 months ago

🚨 In 1993, Steve Jobs literally predicted the future of technology decades before it happened. Most people still haven’t seen this. Long before the iPhone, modern Internet, or AI boom… He was already describing it. Watching it today feels unreal. He talked about computers becoming personal companions not just tools, but extensions of how we think and live. Devices you carry, systems that understand you, and technology that feels almost human. He imagined a world where everything is connected, information flows instantly, and software adapts to people not the other way around. And his biggest insight? The future isn’t about machines. It’s about people. That’s why this still hits hard. Because while most people wait for the future… A few can see it coming years before it arrives.

426

113

405

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4 months ago

@dhanushkraja and gv sir we’ve created a song with the help of AI tools and would truly value your time if you could give it a listen. Your feedback would mean a lot to us. @gvprakash https://t.co/7jrVMhiuBT

155

sendakil retweeted

Analytics Vidhya

@AnalyticsVidhya

5 months ago

3 ways to scale your vector database 🧠⚙️ When your embeddings start to outgrow a single node, scaling isn’t optional it’s survival. Here’s how to do it right: 🔹 Sharding – Split your data across multiple servers to scale storage and speed up parallel imports. 🔹 Replication – Create redundant copies to handle high query loads and ensure zero downtime. 🔹 Vertical Scaling – Add more CPU and memory for faster queries (but remember, hardware caps out eventually). Scaling smartly isn’t just about performance it’s about reliability at scale.

AnalyticsVidhya's tweet photo. 3 ways to scale your vector database 🧠⚙️

When your embeddings start to outgrow a single node, scaling isn’t optional it’s survival. Here’s how to do it right:

🔹 Sharding – Split your data across multiple servers to scale storage and speed up parallel imports.
🔹 Replication – Create redundant copies to handle high query loads and ensure zero downtime.
🔹 Vertical Scaling – Add more CPU and memory for faster queries (but remember, hardware caps out eventually).
Scaling smartly isn’t just about performance it’s about reliability at scale.

275

sendakil retweeted

Dhanian 🗯️

@e_opore

6 months ago

How to learn Backend Development with Python. Grab the Backend with Python Developer Handbook; https://t.co/oKX1bJEfxs

215

136

sendakil retweeted

Python Programming

@PythonPr

6 months ago

RAG vs Agentic RAG

345

139K

sendakil retweeted

Maryam Miradi, PhD

@MaryamMiradi

8 months ago

🧠📸 How to Build Multimodal AI Agents from Scratch — No Prior Experience Needed. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝟵-𝗦𝘁𝗲𝗽 𝗚𝘂𝗶𝗱𝗲 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗲𝗲𝗻𝘀𝗵𝗼𝘁 𝘁𝗼 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴. STEP 0: WHAT IS AN AI AGENT? ✹ A system that uses an LLM to reason, plan, act, and adapt ✹ Goes beyond prompts & RAG — can use tools + learn from memory ✹ Use when workflows are complex, multi-step, or need personalization 》Agents ≠ demos. They’re decision-makers. STEP 1: DEFINE THE AGENT’S ROLE AND GOAL ✹ What will your agent do? ✹ Who is it helping? ✹ What kind of output will it generate? 》Example: An agent that answers questions and explains graphs from reports STEP 2: PREPARE THE MULTIMODAL CORPUS ✹ Convert each page of your PDFs into screenshots ✹ Preserve text + image layout together ✹ Store image paths as metadata 》Why? So retrieval captures full document context STEP 3: GENERATE UNIFIED EMBEDDINGS ✹ Use Vision-Language Models (VLMs) ✹ Avoid the “modality gap” of CLIP-style models ✹ One encoder for both text + visuals 》Tools: YJI-VLM, Gemini 2, Claude Opus STEP 4: STORE IN VECTOR DATABASE ✹ Embed screenshots and attach metadata ✹ Store in a retrievable format ✹ Make retrieval page-level, not just paragraph-level 》Tools: MongoDB Atlas, Weaviate, ChromaDB STEP 5: RETRIEVE CONTEXT VIA TOOL USE ✹ Agent uses vector DB to fetch relevant pages ✹ Returns image references, not raw text chunks ✹ Agent fetches actual images based on those references 》Think: tool = bridge to your multimodal memory STEP 6: ADD MEMORY (SHORT-TERM) ✹ Use session IDs to track conversations ✹ Load past Q&A into LLM at each turn ✹ Let agent reason over previous steps 》Long-term memory optional — short-term is a must STEP 7: PLUG IN A MULTIMODAL LLM ✹ Must support both images + text ✹ Inputs = query + memory + retrieved images ✹ Generates step-by-step plans or final answers 》Tools: Gemini 2.0, GPT-4o, Claude 3.5 STEP 8: DESIGN PLANNING AND FEEDBACK LOOPS ✹ Chain-of-Thought for stepwise reasoning ✹ ReAct for dynamic reasoning + tool calls ✹ Prompt the LLM to decide: act or answer? 》This is where real agentic behavior kicks in STEP 9: WRAP IT IN AN APP OR API (OPTIONAL) ✹ Let users upload reports, images, or ask questions ✹ Build a UI or expose your agent via API ✹ This turns your agent into a product 》Tools: Gradio, Streamlit, FastAPI https://t.co/zy8mvcqSrv ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to Build Real-World AI Agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴, trusted by 1,500+ builders worldwide! ➠ Build Geo, Audio, Video & Vision Agents ➠ Master 5 Modules: 𝗠𝗖𝗣 · LangGraph · PydanticAI · CrewAI · OpenAI Swarm ➠ Deploy for Healthcare, Finance, Smart Cities & More ➠ 9 Real-World Projects with Full Code Only basic Python required. 👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙): https://t.co/5i2v1fIrhJ

MaryamMiradi's tweet photo. 🧠📸 How to Build Multimodal AI Agents from Scratch — No Prior Experience Needed.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮 𝟵-𝗦𝘁𝗲𝗽 𝗚𝘂𝗶𝗱𝗲 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗲𝗲𝗻𝘀𝗵𝗼𝘁 𝘁𝗼 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴.

STEP 0: WHAT IS AN AI AGENT?

✹ A system that uses an LLM to reason, plan, act, and adapt
✹ Goes beyond prompts & RAG — can use tools + learn from memory
✹ Use when workflows are complex, multi-step, or need personalization
》Agents ≠ demos. They’re decision-makers.

STEP 1: DEFINE THE AGENT’S ROLE AND GOAL

✹ What will your agent do?
✹ Who is it helping?
✹ What kind of output will it generate?
》Example: An agent that answers questions and explains graphs from reports

STEP 2: PREPARE THE MULTIMODAL CORPUS

✹ Convert each page of your PDFs into screenshots
✹ Preserve text + image layout together
✹ Store image paths as metadata
》Why? So retrieval captures full document context

STEP 3: GENERATE UNIFIED EMBEDDINGS

✹ Use Vision-Language Models (VLMs)
✹ Avoid the “modality gap” of CLIP-style models
✹ One encoder for both text + visuals
》Tools: YJI-VLM, Gemini 2, Claude Opus

STEP 4: STORE IN VECTOR DATABASE

✹ Embed screenshots and attach metadata
✹ Store in a retrievable format
✹ Make retrieval page-level, not just paragraph-level
》Tools: MongoDB Atlas, Weaviate, ChromaDB

STEP 5: RETRIEVE CONTEXT VIA TOOL USE

✹ Agent uses vector DB to fetch relevant pages
✹ Returns image references, not raw text chunks
✹ Agent fetches actual images based on those references
》Think: tool = bridge to your multimodal memory

STEP 6: ADD MEMORY (SHORT-TERM)

✹ Use session IDs to track conversations
✹ Load past Q&A into LLM at each turn
✹ Let agent reason over previous steps
》Long-term memory optional — short-term is a must

STEP 7: PLUG IN A MULTIMODAL LLM

✹ Must support both images + text
✹ Inputs = query + memory + retrieved images
✹ Generates step-by-step plans or final answers
》Tools: Gemini 2.0, GPT-4o, Claude 3.5

STEP 8: DESIGN PLANNING AND FEEDBACK LOOPS

✹ Chain-of-Thought for stepwise reasoning
✹ ReAct for dynamic reasoning + tool calls
✹ Prompt the LLM to decide: act or answer?
》This is where real agentic behavior kicks in

STEP 9: WRAP IT IN AN APP OR API (OPTIONAL)

✹ Let users upload reports, images, or ask questions
✹ Build a UI or expose your agent via API
✹ This turns your agent into a product
》Tools: Gradio, Streamlit, FastAPI

https://t.co/zy8mvcqSrv

≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣

⫸ꆛ Want to Build Real-World AI Agents?

Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴,
trusted by 1,500+ builders worldwide!

➠ Build Geo, Audio, Video & Vision Agents
➠ Master 5 Modules: 𝗠𝗖𝗣 · LangGraph · PydanticAI · CrewAI · OpenAI Swarm
➠ Deploy for Healthcare, Finance, Smart Cities & More
➠ 9 Real-World Projects with Full Code

Only basic Python required.

👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙): https://t.co/5i2v1fIrhJ

630

135

524

35K

sendakil retweeted

Ezekiel

@ezekiel_aleke

9 months ago

Data Science in one page🔥

241

119

sendakil retweeted

Book Therapy

@Book_therapy223

9 months ago

Better communication, better outcomes.

204

123

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Python Developer

@PythonDvz

9 months ago

AI Agents Tech Stack

sendakil retweeted

sysxplore

@sysxplore

9 months ago

How Git Works?

556

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sendakil retweeted

about 1 year ago

New release from @PacktDataML >> Hands-On #AI for #IoT : Expert #MachineLearning and #DeepLearning techniques for developing smarter IoT systems [2nd Edition] See it at https://t.co/36PCOv9cLc 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🔴Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data 🔵Enhance your IoT solutions with advanced AI techniques, including deep learning, optimization, and generative adversarial networks 🟢Gain practical insights through industry-specific IoT case studies in manufacturing, smart cities, and automation 🔴Purchase of the print or Kindle book includes a free PDF eBook

KirkDBorne's tweet photo. New release from @PacktDataML >> Hands-On #AI for #IoT : Expert #MachineLearning and #DeepLearning techniques for developing smarter IoT systems [2nd Edition]

See it at https://t.co/36PCOv9cLc

𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:

🔴Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data

🔵Enhance your IoT solutions with advanced AI techniques, including deep learning, optimization, and generative adversarial networks

🟢Gain practical insights through industry-specific IoT case studies in manufacturing, smart cities, and automation

🔴Purchase of the print or Kindle book includes a free PDF eBook

sendakil retweeted

about 1 year ago

The Handbook of #DataScience and #AI — Generate Value from Data with #MachineLearning and Data #Analytics: https://t.co/ok23tSbaCV —— #DataScientist #ML #DataStrategy #AnalyticsStrategy #DataLeadership #Datapreneurship #Datapreneur @LeadershipData #CDO #CMO #CTO #CAIO #Startups

KirkDBorne's tweet photo. The Handbook of #DataScience and #AI — Generate Value from Data with #MachineLearning and Data #Analytics: https://t.co/ok23tSbaCV
——
#DataScientist #ML #DataStrategy #AnalyticsStrategy #DataLeadership #Datapreneurship #Datapreneur @LeadershipData #CDO #CMO #CTO #CAIO #Startups https://t.co/80cFCXwDzI

sendakil retweeted

about 1 year ago

#MachineLearning for Data Streams is imperative for digital enterprises to move at the speed of data! ★★★★ Get the book: https://t.co/TA53CFp3Pv ★★★★ —————— #IoT #IIoT #DataScience #AI #ML #StreamAnalytics #EdgeComputing #Edge #EdgeAI #AnomalyDetection #DataScientist

KirkDBorne's tweet photo. #MachineLearning for Data Streams is imperative for digital enterprises to move at the speed of data!
★★★★
Get the book: https://t.co/TA53CFp3Pv
★★★★
——————
#IoT #IIoT #DataScience #AI #ML #StreamAnalytics #EdgeComputing #Edge #EdgeAI #AnomalyDetection #DataScientist https://t.co/w1xBmzhe1L

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