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July 2, 2024

2024 Machine Learning Revolution: 5 Trends You Need to Know

The field of artificial intelligence (AI) and machine learning (ML) is rapidly evolving, with new trends shaping the landscape. As we step into 2024, let’s explore five key trends that will impact businesses, drive innovation, and transform the way we approach ML.

Table of Contents:

  • 5 Trends You Need to Know
  • The Most Upskilling Programs
  • Conclusion

5 Trends You Need to Know

1. Generative AI Sparkles but Needs to Deliver Value

While Generative AI has garnered attention, its real significance is unknown. Tech enthusiasm is great, but value delivery is slow. Few companies have commercial implementations of generative AI, and most are currently testing it. Companies need organizational transformation, data curation, and infrastructure integration to maximize their potential.

2. MLOps and DataOps for Efficient Data Management

Companies are investing in MLOps and DataOps to simplify ML model deployment and administration. These systems improve model performance, accuracy, and productivity. Complex ML models need effective data handling.

3. Shortage of Skilled Data Scientists and Engineers

Demand exceeds availability for competent data scientists and engineers. Companies struggle to find ML, AI, and data science skills. Upskilling teams and encouraging domain expert-data professional cooperation are key to addressing this deficit.

4. AI-Based Products Proliferate

AI has spread beyond research laboratories to daily items. AI-powered recommendation engines and personalized marketing improve user experiences. Companies are adding ML capabilities to their products to compete.

5. Microservices and Containerization for ML Infrastructure

ML infrastructure is embracing microservices and containerization. Containerized ML models launch, scale, and reproduce smoothly. Monolithic systems are broken down to increase flexibility and agility.

The Most Upskilling Programs

Several reputed institutes have come up with comprehensive courses that help hone your skills. The AI and machine learning course fees vary from one institute to the other.

1. PGP in Machine Learning & Artificial Intelligence from NIIT

The PGP in Machine Learning & Artificial Intelligence from NIIT is a machine learning online course with a duration of 40 weeks. It covers the entire spectrum of machine learning, from data analytics and visualization to predictive modeling and forecasting.

Course Content and Highlights:

  • Data Analytics Using Excel
  • Analytics using SQL
  • Introduction to Programming using Python
  • Python for Data Science
  • Exploratory Data Analysis
  • Exploratory Data Analysis using Tableau
  • Story Telling using Tableau
  • Capstone project 1
  • Statistics & Data Visualization using Python
  • Data Modelling using Machine Learning – part 1
  • Data Modelling using Machine Learning – part 2
  • ML Mini Project
  • Time Series Forecasting
  • Capstone Project 2
  • Online study with live sessions and mentor support
  • Industry Projects
  • Mentorship and Guidance from Experts
  • Certification and Accreditation from NIIT and StackRoute
  • Job Assistance and Placement Support

2.  Machine Learning A-Z: Hands-On Python & R In Data Science from Udemy

Machine Learning A-Z: Hands-On Python & R in Data Science is a 44-hour course and covers the most fundamental and practical parts of machine learning like data pre-processing and regression, classification and clustering.

Course Content and Highlights:

  • Part 1: Data Preprocessing
  • Part 2: Regression
  • Part 3: Classification
  • Part 4: Clustering
  • Part 5: Association Rule Learning
  • Part 6: Reinforcement Learning
  • Part 7: Natural Language Processing
  • Part 8: Deep Learning
  • Part 9: Dimensionality Reduction
  • Part 10: Model Selection & Boosting
  • Expert Instructors Kirill Eremenko and Hadelin de Ponteves
  • Video lectures, code templates, exercises, and projects
  • Q&A forum, certificate of completion, lifetime access to course materials

Conclusion

As we navigate the 2024 ML revolution, organizations must stay agile, invest in talent, and adopt best practices. Whether it’s generative AI, efficient data management, or AI-powered products, embracing these trends will drive success in the dynamic world of ML.

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