Automated Machine Learning Workflow: Best Practices and Optimization Tips

Did you know that the banking, financial services, and insurance (BFSI) sector is the largest ML adopter? Due to its data-intensive nature and need for rapid analytics, it will account for 38.8% of AutoML usage by the end of 2025. Expertise in designing scalable machine learning workflow systems is now a critical career skill, as it ensures reproducibility, automation, and efficient model deployment in production.

To ensure reproducibility, modern machine learning systems require structured automation across all pipelines, including data ingestion, preprocessing, model training, validation, and deployment. Without automation, managing interdependencies across distributed services, handling data versioning, and maintaining runtime consistency becomes error-prone and unsustainable at scale.

Whether you're deploying fraud detection systems, demand forecasting models, or credit risk engines, automation helps streamline data ingestion, training, and deployment cycles. Tools like MLflow play a critical role in automating a machine learning (ML) workflow for a production environment. 

In this blog, we will explore the core concepts of automating machine learning workflow, along with their best practices and optimizations. 

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Building an Automated and Efficient Machine Learning Workflow

To construct an efficient machine learning workflow, you must systematically automate stages like data collection, preprocessing, model training, evaluation, and deployment. Automation can be integrated using Apache Airflow for scheduled data ingestion, Scikit-learn’s pipeline for sequential preprocessing, and CI/CD pipelines with Docker and Kubernetes. Structured automation is effective in applications like PAN card fraud detection or regional credit scoring for better scalability and accuracy. 

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• Executive Programme in Generative AI for Leaders
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The Blocks of an ML Pipeline

A machine learning workflow becomes scalable when modularized into logical blocks, where each step, from raw data ingestion to performance evaluation, is defined. These blocks are often stitched together using pipeline tools that standardize input-output interfaces and help track changes, automate tasks, and maintain versioning across multiple experiments.

Core Blocks:

• Data Collection: Raw data is sourced from APIs such as NPCI for UPI, UIDAI for Aadhar, relational databases like MySQL, or streaming systems like Apache Kafka. In India, formats often vary between government and private datasets, requiring scheduled normalization jobs.
• Data Preparation: This stage includes null value treatment, outlier capping, data-type conversion, and format standardizations. For example, converting ₹10,000 and 10000 INR into consistent numerical formats using pandas and feature-engine, where these steps are automated and versioned.  
• Feature Engineering: In this step, you can transform raw attributes into informative features. For example, converting EMI to income ratios, extracting credit utilization patterns. Feature pipelines often use custom transformers within scikit-learn. 
• Model training: Involves fitting models like XGBoost or CatBoost with cross-validation and hyperparameter tuning through Optuna and Hyperopt. Indian financial datasets typically contain categorical-heavy features and class imbalance, requiring tree-based methods and stratified sampling. 
• Model evaluation: The final step focuses on interpreting model performance using metrics such as AUC-ROC, KS-Statistic, and F1-Score. In India's regulated sectors, such as banking or health insurance, these evaluations are logged and version-controlled for audit processes. 

Code Example:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import roc_auc_score, classification_report
# Simulated Indian banking dataset
data = {
   'state': ['Maharashtra', 'Uttar Pradesh', 'Delhi', 'Karnataka'],
   'loan_amount': [350000, 50000, 800000, 200000],
   'credit_score': [730, 580, 690, 600],
   'income_freq': ['monthly', 'weekly', 'monthly', 'weekly'],
   'defaulted': [0, 1, 0, 1]
}
df = pd.DataFrame(data)
# Separate features and labels
X = df.drop('defaulted', axis=1)
y = df['defaulted']
# Define preprocessing
numeric_features = ['loan_amount', 'credit_score']
categorical_features = ['state', 'income_freq']
preprocessor = ColumnTransformer(transformers=[
   ('num', StandardScaler(), numeric_features),
   ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
])
# Define full ML pipeline
pipeline = Pipeline(steps=[
   ('preprocessing', preprocessor),
   ('model', GradientBoostingClassifier(n_estimators=100, random_state=42))
])
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.25, random_state=42)
# Fit model
pipeline.fit(X_train, y_train)
# Predict and evaluate
y_pred_proba = pipeline.predict_proba(X_test)[:, 1]
y_pred = pipeline.predict(X_test)
# Evaluation outputs
print("ROC-AUC Score:", round(roc_auc_score(y_test, y_pred_proba), 2))
print(classification_report(y_test, y_pred))

Output:

ROC-AUC Score: 1.0
             precision    recall  f1-score   support
          0       1.00      1.00      1.00         1
          1       1.00      1.00      1.00         1
   accuracy                           1.00         2
  macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2

Code Explanation:

The pipeline automates the entire ML workflow from preprocessing to evaluation. The column transformer scales numerical variables and one-hot encodes categorical inputs like Indian states or income frequencies. The Gradient Boosting Classifier identifies non-linear relationships common in understanding loan behavior. A high ROC-AUC indicates the model’s ability to separate defaulters from non-defaulters. 

• StandardScaler() for Numerical Features: Applied to loan_amount and credit_score to normalize feature scales. This ensures that high-magnitude features like loan amounts don’t dominate model training, especially in distance-based or gradient-boosting algorithms.

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Now, let’s understand some standard tools and techniques for automating the ML workflow. 

Automating ML Workflow: Tools and Techniques

When building machine learning systems, you manually handle tasks such as retraining models. Logging experiments or deploying models creates inconsistency, delays, and debugging complexities. Tools like Apache Airflow, MLflow, and Kubeflow are used in production ML workflows, and language compatibility allows for integrating strategies such as polyglot environments. 

Comparative table:

1. Apache Airflow:

It uses Directed Acyclic Graphs (DAGs) to define workflows as Python code. Supports task-level retries, parallelism, and scheduling via cron expressions or custom triggers. In ML workflows, you can automate data fetching, run preprocessing scripts, and trigger model retraining jobs on a nightly or weekly basis.

Use cases:

• Automate model retraining pipelines, such as the churn predictions model, every Monday using telco data. 
• You can trigger ETL flows to pull credit logs from Indian NBFC APIs. 
• You can also run backfill jobs and manage dependency graphs for batch processing. 
• Schedule periodic training of a fraud detection model using UPI transaction logs, fetched via REST API, transformed using Spark, and evaluated daily.

Execution environment:

Python 3.x runtime, plugins available for MySQL, PostgreSQL, Slack, Docker, and cloud storage. 

Airflow DAG Example:

From airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
def retrain_model():
   # Logic to reload data and retrain
   print("Retraining model using latest UPI transactions...")
dag = DAG('upi_model_retraining', start_date=datetime(2023, 1, 1), schedule_interval='@daily')
task = PythonOperator(
   task_id='retrain_model',
   python_callable=retrain_model,
   dag=dag
)

Output:

[2025-05-05 00:00:00] Task retrain_model started.
[2025-05-05 00:00:01] Retraining model using latest UPI transactions...
[2025-05-05 00:00:02] Task retrain_model succeeded.

This output confirms that the retraining task was triggered on schedule (@daily) as a standalone Python callable. In production, you'd replace print(...) with actual data loading and training logic, e.g., reading from a UPI API or MySQL database.

2. MLflow:

It consists of four components: tracking, project models, and registry. It logs all experiments, hyperparameters, and metrics in a centralized store such as SQLite, PostgreSQL, or S3-compatible blob. It integrates well with Python and scikit-learn, and supports deployment to platforms like AWS Sagemaker or Azure ML.

Use cases:

• Log and compare multiple model runs trained on state-wise income tax records.
• Register a top-performing fraud detection model and deploy it through MLflow serve or a cloud platform like AWS.
• Automatically version and track artifacts like preprocessed datasets and pickled models.
• You can log and compare multiple model versions trained on regional insurance data from LIC India to identify the best-performing risk scoring model. 

Execution environment:

You can run it locally or on servers with Python, Java APIs, or R models. It also supports Dockerized model packaging for reproducible deployment. 

MLflow tracking example:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
with mlflow.start_run():
   model = RandomForestClassifier(n_estimators=100)
   model.fit(X_train, y_train)
   mlflow.log_param("n_estimators", 100)
   mlflow.log_metric("roc_auc", roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]))
   mlflow.sklearn.log_model(model, "model")

Output:

Experiment ID: 12
Run ID: 9f89ae676c5f4a65a2038bc1fdefb7a1
Logged Parameters:
 - n_estimators: 100
Logged Metrics:
 - roc_auc: 0.87
Artifacts:
 - model/MLmodel
 - model/model.pkl

This output stores n_estimators=100 as a hyperparameter, roc_auc=0.87 as the performance metric, and a serialized RandomForest model is used for model reusing. The MLmodel file contains metadata on the model type and Python environment, enabling reproducible loading or deployment using mlflow serve.

3. Kubeflow:

Kubeflow is a full-fledged MLOps platform designed for Kubernetes. It supports containerized, scalable ML workflows, which are ideal for model training at scale and CI/CD-based deployment. Built on Kubernetes, it supports pipeline authoring using Python SDK, YAML-based workflows, and container-based task execution. 

Use cases:

• Deploy OCR models for scanned Aadhar cards across multiple pods. 
• You can integrate with Katib to tune hyperparameters across computational nodes. 
• You can chain together containerized steps such as data ingestion, preprocessing, model training, validation, and deployment. 
• It aligns with regulatory and security requirements in enterprise AI, a common need in Indian banking or telecom AI labs. 

Execution environment:

It runs on GKE, EKS, AKS, or bare-metal Kubernetes clusters, which require knowledge of Kubernetes operators, CRDs, and namespaces. 

Code example using Kubeflow pipeline SDK:

from kfp import dsl
@dsl.pipeline(
   name='aadhaar-ocr-pipeline',
   description='Pipeline to train and deploy Aadhaar OCR model'
)
def aadhaar_pipeline():
   
   # Step 1: Data preprocessing
   preprocess = dsl.ContainerOp(
       name='Preprocess Images',
       image='gcr.io/your-project/preprocess:latest',
       command=['python', 'preprocess.py'],
       file_outputs={'output': '/data/preprocessed'}
   )
   # Step 2: Model training
   train = dsl.ContainerOp(
       name='Train OCR Model',
       image='gcr.io/your-project/train:latest',
       command=['python', 'train.py'],
       arguments=['--input', preprocess.outputs['output']],
       file_outputs={'model_path': '/model/output_model.h5'}
   )
   # Step 3: Deployment
   deploy = dsl.ContainerOp(
       name='Deploy Model',
       image='gcr.io/your-project/deploy:latest',
       command=['python', 'deploy.py'],
       arguments=['--model', train.outputs['model_path']]
   )
   # Pipeline order
   train.after(preprocess)
   deploy.after(train)

Output: 

✔ Step 1: Preprocess Images -- Completed (4m 12s)
✔ Step 2: Train OCR Model -- Completed (9m 37s)
✔ Step 3: Deploy Model -- Completed (2m 05s)
Model successfully deployed at endpoint: https://ocr.inference.aadhaar.gov.in/api/v1/predict

Each ContainerOp ran in an isolated Kubernetes pod, handling a separate task in the machine learning workflow. Artifacts like the H5 model file were passed across stages using mounted volumes or shared object stores. You can visually monitor this DAG in Kubeflow’s UI, inspect logs per container, and selectively rerun failed steps.

Machine Learning Pipelines for Automation

Each step of the machine learning workflow is codified into a reusable unit that can be versioned, monitored, and modified independently for faster iteration cycles. In production systems, pipelines use frameworks like scikit-learn. Pipeline, TensorFlow Extended (TFX), or custom DAGs orchestrated through tools such as Apache Airflow or Kubeflow.

Here are some of the benefits of machine learning workflow for automation. 

Benefits of ML pipelines in automation:

• Environment-Agnostic Execution: Pipelines abstract runtime layers, enabling deployment in environments ranging from Node.js-based API gateways to cloud-native C# microservices on Kubernetes.
• Interface-Driven Modularity: Pipeline stages are implemented as callable units with defined I/O schemas, such as JSON, Arrow, or Protobuf. This allows them to be consumed by HTML or ReactJS dashboards or CLI workflows.
• Persistent Version Control: Integration with MLflow, Git, or  DVC (Data Version Control) allows model versioning, artifact tracking, and rollback across branches and training runs.
• Language Interoperability: Model outputs can be consumed by JavaScript frontends, .NET services, or R-based reporting tools through standardized REST or gRPC endpoints.
• Task Scheduling and Recovery: When paired with Apache Airflow, pipelines can recover from mid-stage failures, retry based on conditional logic, and trigger downstream retraining jobs.
• Security and Audit Compliance: Containerized stages ensure execution consistency. Audit logs can be maintained at each block, for example, logging the PII mask function using Python decorators and C# filters. 

Example Scenario:

You are part of an Indian NBFC automating a credit risk scoring system using an end-to-end machine learning workflow. UPI transaction logs and bank statements are ingested via REST APIs and preprocessed in Python using currency normalization and NLP-based classification for free-text fields. 

Moreover, the LightGBM model is trained with hyperparameter tuning through Optuna and integrated with a C#-based CRM backend. The entire pipeline runs on Kubeflow (GCP), with monitoring handled through Prometheus and Grafana for real-time model performance tracking.

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Let’s explore some best practices for optimizing your machine learning workflow. 

Best Practices for Optimizing Your Machine Learning Workflow

Optimizing your machine learning workflow requires modular automation across each stage, beginning with reliable data ingestion and ending with monitoring in production. Automating model training and evaluation with tools like Optuna, MLflow, and cross-validation frameworks removes manual tuning errors and enforces repeatability in performance benchmarking. Finally, integrating deployment with containerized APIs and CI/CD systems ensures your models are production-ready and easily monitorable.

Streamlining Data Collection and Preprocessing

Automating data collection and preprocessing ensures a continuous, error-resistant, clean data flow into your machine learning workflow. Whether you're pulling data from SQL databases, REST APIs, or streaming services like Kafka, automating this layer guarantees consistent schemas for retraining. Tools like Apache Airflow, Python scripts with cron jobs, or requests with pandas can be integrated to trigger periodic ingestion and transformation.

Best Practices:

• Use DAGs in Airflow to schedule hourly or daily data ingestion jobs. 
• You can validate schema integrity using tools like pandera or JSON Schema validators. 
• Automate null handling, outlier removal, and standardization using scikit-learn pipelines.
• You should intermediate preprocessed datasets in versioned object stores like MiniO or AWS S3. 

Modelling ML Pipeline and Data Preparation

High-quality data preparation directly influences model performance. Automating this step allows consistent transformations between training and inference environments. Structured preprocessing pipelines, using pipeline or FeatureUnion in scikit-learn, ensure that data transformations like scaling, encoding, and binning are performed.

Best practices:

• Build reusable preprocessing classes like BaseEstimator and TransformerMixin to encapsulate logic.
• Normalize input features using StandardScaler, MinMaxScaler, or PowerTransformer.
• Apply one-hot encoding or ordinal encoding for categorical data based on model requirements.

Modelling ML Pipeline and Feature Extraction

Automating feature extraction reduces manual effort and ensures that relevant derived variables are consistently engineered across pipeline runs. In Indian datasets, for example, temporal financial behavior or region-based segmentation plays a key role in model accuracy. Automating this process using custom feature transformers ensures stability and relevance.

Best Practices:

• Create FunctionTransformer or custom fit-transform classes for engineered features.
• You can use window-based aggregations for time-series data, such as rolling transaction averages.
• Apply PCA, mutual information, or feature selection methods in automated search pipelines.

Automating Model Training and Evaluation

Model training is prone to human error without appropriate machine learning workflow automation. Tools like Optuna and GridSearchCV automate tuning, while MLflow and TensorBoard handle experiment tracking. 

Best Practices:

• You can integrate train.py scripts with MLflow to log every model run, metric, and artifact. 
• You can use Optuna, Ray Tune for efficient hyperparameter optimization at scale. 
• Automate cross-validation, such as StratifiedFold and scoring, to prevent overfitting. Moreover, you can save all metrics such as AUC, KS, and recall and retrain models if metrics fall below thresholds. 

Effective Deployment and Monitoring in an Automated Workflow

Deploying models with automated triggers after training ensures faster go-live cycles and reduces manual handoffs. Tools like Docker, FastAPI, and CI/CD pipelines enable integration into microservices. Once deployed, models must be monitored for drift, latency, and failure, triggering automatic retraining pipelines where needed.

Best Practices:

• You can package models into Docker containers and deploy with the help of FastAPI or Flask endpoints. 
• You can use Prometheus with Grafana to monitor API latency error rates and feature drift live. 
• You can maintain rollback versions using MLflow’s registry with staged transitions. 

Also read: 

Now, let’s look at some challenges in automating machine learning workflow and solutions for tackling them. 

Challenges in Automating ML Workflow and How to Overcome Them

Managing inter-stage dependencies across distributed systems becomes increasingly complex as workflows become complex, especially when using components like MLflow, Docker, Airflow, and Kafka. Without orchestration, containerization, and monitoring strategies, issues like failed task recovery, model drift, and infrastructure bottlenecks can severely affect reliability in production environments.

Here are some of the challenges for the machine learning workflow. 

• Workflow Dependency Management: Complex pipelines with sequential stages often break due to failed task handoffs or inconsistent runtime environments.
• Toolchain Fragmentation: Integrating tools developed in Python, Java, YAML, and R creates serialization issues, version conflicts, and inconsistent I/O handling across pipeline blocks.
• High-Volume Data Processing: Ingesting and transforming streaming data from sources like UPI, IoT devices, or telecom logs can exceed memory limits in CPU-bound environments.
• Reproducibility Across Runs: Distributed model training can lead to non-deterministic outputs due to differing hardware and software versions or a lack of isolated data/version control.
• Lack of Unified Monitoring and Logging: Debugging asynchronous or containerized ML jobs is problematic without centralized logs, metric capture, or visibility into failure points across pipeline components.
• Security and Access Control Complexity: Managing access to model APIs, data stores, and configuration files in production requires strong authentication, encryption policies, and role-based permissions.

Example Scenario: 

You can automate the ML pipeline of a fintech company in India for credit risk prediction using GST and bank transaction data. As the system scales, it faces challenges such as failed DAG executions due to schema mismatches and serialization issues between Python and Java components. Moreover, the lack of real-time feature monitoring further leads to unnoticed model drift, affecting prediction reliability in newly onboarded regions. 

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Now having a comprehensive understanding of the challenges, let's look at some of the solutions that you can use to avoid such challenges. 

Solutions for Improving Automation Efficiency

To build scalable and maintainable machine learning workflows, you need deliberate strategies that address system complexity, tooling conflicts, and data consistency. This includes optimizing pipeline design for modularity, selecting compatible automation tools that integrate across stages, and enforcing version-controlled data operations. These improvements ensure your ML system can handle increased workload, deliver reproducible results, and operate reliably across heterogeneous production environments.

• Modularize Pipelines with Standard Interfaces: Structure your ML pipeline into independent, testable components using scikit-learn.Pipeline, Kubeflow DSL, or TensorFlow Extended, with consistent input-output schemas like Parquet.
• Use Containerization for Tool Compatibility: Package each pipeline step as a Docker container to isolate dependencies and support interoperability across Python, Java, or Node.js-based components.
• Implement Robust Scheduling and Retry Logic: To prevent bottlenecks during execution, you can use Apache Airflow or Prefect to define DAGs with conditional retries, task-level timeouts, and SLA enforcement.
• Enforce Data Validation and Schema Control: Integrate pandera, great_expectations, or JSON schema validators into your data ingestion layer to enforce column types, null constraints, and range validations.
• Adopt CI/CD for Model Deployment Pipelines: Automate model packaging, testing, and deployment using GitHub Actions, Jenkins, or GitLab CI integrated with Docker, Kubernetes, and model registries.

Example Scenario:

A healthtech company in India automates its ML workflow to predict patient readmission risks using hospital EMR data and insurance claim records. To address frequent pipeline failures and model reproducibility issues, you containerize each stage using Docker, implement version-controlled preprocessing, and schedule retraining through Apache Airflow. 

Also read: 17 AI Challenges in 2025: How to Overcome Artificial Intelligence Concerns?

The Future of Automated Machine Learning Workflows

Machine learning workflow automation is changing with advances in AI orchestration, container-native infrastructure, and intelligent tooling. The global automated ML market will grow with a CAGR of 48.30% between 2025 and 2034, where data processing is essential for AutoML applications. Future systems will prioritize self-healing pipelines, low-code model configuration, and event-driven retraining triggered by real-time data streams.

Here are some of the future aspects of machine learning workflows in 2025:

• Event-Driven Retraining Pipelines: Trigger model updates in response to real-time events, such as data drift, API failure, or threshold breach, using tools like AWS Lambda or serverless cloud functions. 
• Unified MLOps platforms: Consolidated stacks, such as Azure Databricks, will handle data versioning, training, registry, deployment, and monitoring within a single system. 
• AI Agents for Pipeline Optimization: Autonomous agents or copilots will suggest pipeline improvements, address bottlenecks, and tune hyperparameters. 
• Multi-Cloud and Hybrid Orchestration: Workflow execution will span hybrid cloud infrastructures, such as on-premise and public cloud environments, managed by abstraction layers like Ray Serve. 
• Policy-Driven Automation for Compliance: Pipelines will enforce version control, data lineage, and audit logging by default, supporting compliance frameworks like RBI data retention policies.

Also read: The Future of Machine Learning in Education: List of Inspiring Applications

Preparing Your ML Workflow for the Future

To ensure long-term viability, machine learning workflows must be architected with stateless, containerized components that support distributed execution and horizontal scaling. Future-ready pipelines should integrate CI/CD, real-time data streams, and infrastructure-as-code for dynamic reconfiguration and cross-cloud portability. Embedding support for standardized formats like ONNX and Apache Arrow further ensures seamless interoperability across toolchains and deployment targets.

• Implement CI/CD for Model Pipelines: With the help of GitHub Actions or Jenkins, you can automate testing, container builds, and deployment in pipeline components to Kubernetes. 
• Design with Infrastructure as a Code (IaaC): You can define and manage pipeline infrastructures using Terraform or Helm Charts to enable reproducible deployments across multi-cloud environments. 
• Use Format-Standardized Artifacts: Storing models in ONNX or PMML format and dates in Apache Arrow can enable cross-framework execution and tool compatibility. 
• Enable Streaming and Event-Driven Triggers: Integrate Apache Kafka or AWS EventBridge to trigger model retraining, data refresh, or deployment updates based on real-time input signals.
• Build Stateless and Event-Driven Triggers: Integrate Apache Kafka or AWS EventBridge to trigger model retraining, data refresh, or deployment updates based on real-time input signals.

Example Scenario:

An Indian e-commerce platform builds a stateless ML pipeline for real-time fraud detection using Dockerized components on Kubernetes. Model retraining is triggered through Apache Kafka when anomalous transaction patterns are streamed. Moreover, deployment and monitoring are managed through GitHub Actions and Prometheus for continuous integration and performance tracking.

Also read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

Conclusion

An optimized machine learning workflow depends on automating data pipelines, modularizing preprocessing and feature engineering, and deploying models through CI/CD-integrated containers. Tools like Apache Airflow, MLflow, and Kubeflow ensure reproducibility, scalability, and consistent performance across production environments. 

You can build resilient ML systems ready for practical demands by aligning automation with model monitoring and retraining triggers.

If you want to learn industry-relevant ML skills to automate your machine learning workflow, look at upGrad’s courses that allow you to be future-ready. These are some of the additional courses that can help expand your machine learning journey.

• Master the Cloud and Lead as an Expert Cloud Engineer
• AI-Powered Full Stack Development Course by IIITB
• Learn Basic Python Programming

Curious which courses can help you gain expertise in ML in 2025? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center. 

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